bluuebunny/crossref_metadata_2025_split huggingface dataset:
Model design as a new socio-cyber-physical systems creating mechanism
Authors: Yurkevich E.V. , Kryukova L. N. 
Published: May 2021

Abstract
The purpose of this work is to consider the methodology of designing intellectual models of cyber-physical social systems that take into account the cybernetic properties of the hardware, software and the human factor specifics of the system developers and users. To ensure customizable solutions, model design process is based on systems analysis. This methodology can be implemented by optimizing the objective tree, which determines the development vector of the model being designed based on their importance; the information tree, which determines the structure of the knowledge base optimized by the cost of resources used; the task tree, which allows optimizing the development of the cyber-physical social systems based on the results of analyzing the criteria of the two trees. Intellectual properties of the model being designed are determined by automating the management of its development vector in accordance with the dynamics of the utility of its specific goals. Due to practical limitations on the amount of resources available, the task is to optimize the plan for creating a cyber-physical social system that maximizes the solution value with limited cost. The choice between more precise algorithms that are not applicable given fuzzy limitations caused by human factor allows solving the problems faster than when using deterministic or heuristic methods.
Development of Stability Control Mechanisms in Neural Network Forecasting Systems
Authors: Miloserdov I. , Miloserdov D. 
Published: May 2021

Abstract
The problem of ensuring the stable functioning of time series forecasting systems based on streaming recurrent neural networks with controlled elements is considered. The mechanisms necessary and sufficient for its maintenance are derived, which involve maintaining the balance of the learning history and modifying the synapse learning rules in order to establish a balance between positive and negative potential. The results of experiments to assess the accuracy of forecasting are presented.
Accuracy improving of pre-trained neural networks by fine tuning
Authors: Konarev D. I. , Gulamov A. A. 
Published: May 2021

Abstract
Methods of accuracy improving of pre-trained networks are discussed. Images of ships are input data for the networks. Networks are built and trained using Keras and TensorFlow machine learning libraries. Fine tuning of previously trained convoluted artificial neural networks for pattern recognition tasks is described. Fine tuning of VGG16 and VGG19 networks is done by using Keras Applications. The accuracy of VGG16 network with fine tuning of the last convolution unit increased from 94.38% to 95.21%. An increase is only 0.83%. The accuracy of VGG19 network with fine tuning of the last convolution unit increased from 92.97% to 96.39%, which is a 3.42% increase.
Blockchain-based approach to achieve credible traceability of agricultural product transactions
Authors: Yi Wenlong , Huang Ximeng , Yin Hua , Dai Shiming 
Published: May 2021

Abstract
The traditional traceability system for agricultural product transactions is susceptible to information alteration and damage, which may lead to issues regarding product quality and food safety. And blockchain is a technology that boasts tamper-proofing, complete traceability and time-stamped storage. Considering the above, this study proposes a new blockchain-based approach to the quality management of agricultural products and introduces Solidity-based prototype smart contracts for agricultural product transactions. Test results show that the new traceability system can offer good performance in terms of data upload and block response time. The method proposed in this paper can be used as a solution for quality management of agricultural product that boasts whole process transparency, full-link reliability and joint supervision by all nodes in the system.
Smart home security management
Authors: Tatarnikova T. , Sovetov B. 
Published: May 2021

AbstractThe problem of ensuring the information security of the smart home is discussed, the main vulnerabilities and the consequences they can cause are listed. Mechanisms for protecting information in the smart home system based on the characteristics of consumed resources: energy, time, computing power, are proposed. A description of the smart home system assembled model is presented, and consumed resources characteristics allowing reasonable choice of the method for protecting information transmitted by devices over open channels are measured by using this model.
Investigation of the method of determination of password resistance to brute force based on an artificial neural network
Authors: Izrailov K. E. , Zhukovskaya P. E. , Kurta P. A. , Chechulin A. A. 
Published: May 2021

Abstract
The paper deals with a topical subject of information system access security through development of a polling system aimed at forecasting the resistance of the user’s passwords against brute force. A method of development of such a system, which is based on computer-aided learning, is suggested. This method is based on the following hypothesis: “Social and psychological features of the password system users have a bearing on the complexity of created passwords.” The method is elaborated in the form of the following 6 steps: development of a group of questions which reflect the preferences of the password system user; implementation of a polling system which requests the user’s typical password; selection of a mechanism of independent estimation of password complexity; application of the polling system to generate implicit interrelations of specific features of users and their passwords; development and training of a neural network to solve a regression problem based on the results of the polling system use; and estimation of the neural network by a test set of users. The experiment results are presented in a form of a diagram. The results of the experiment are discussed and conclusions are drawn about the method development.
Analysis of Control Channel Cybersecurity of the Consumer-Grade UAV by the Example of DJI Tello
Authors: Mamchenko M.V. 
Published: May 2021

Abstract
This paper presents the assessment of cybersecurity of the non-industrial unmanned aerial vehicle (UAV; using DJI Tello as an example) wireless control channel, implemented on the basis of Wi-Fi Wireless Local Area Network technology. As a result of the research, a model of an adversary conducting a cyberattack on the UAV communication channel was developed, and scenarios of an intruder’s actions (aimed at taking control of the UAV or disabling it) were considered. Relevant block diagrams of the actions of an adversary were also developed. Based on the results of the analysis, a security strategy is proposed. It provides the maximum possible protection (based on the technical characteristics of the drone) and comprises not only passive protection in the form of setting a tough password, but also active actions –deploying a system to monitor the injections of deauthentication/dissociation frames and/or the creation of duplicate wireless networks.
Design of a direction finder based on the output of parallel hydroacoustic surveillance system
Authors: Melkanovich V.S. 
Published: May 2021

Abstract
The problem of synthesizing a direction finding algorithm based on a sample composed of the output signals of a limited number of channels of a scanning sonar system is considered. The problem is formulated as a search for the maximum of the likelihood function by its lattice representation. Options for using the optimal and quadratic interpolation are considered. The errors of displacement and loss of potential accuracy are estimated depending on sample dimension, angular step of scanning, and on the amount of the target movement within the measurement interval. The advantage of the proposed approach is shown in terms of its potential accuracy compared to the traditional approach to constructing direction finding paths based on Guanella’s technique.
Development of an effective hybrid system for generation and distribution of electric power for autonomous unmanned underwater vehicles
Authors: Kiselev N. , Martynova L. 
Published: May 2021

Abstract
The problem of coordinated operation of a hybrid system for generation and distribution of electric power to provide motion for an underwater vehicle is addressed. The architecture of a multi-agent vehicle system is chosen, the optimum distribution of consumers to electric lines is defined, effective switching of consumers between electric lines, with due regard to the functional and non-functional requirements for the system, is developed.
Artificial intelligence in technical diagnostics in the example of a marine auxiliary diesel engine
Authors: Kolpakov V. E. 
Published: May 2021

Abstract
The necessity of improving methods for determining the technical condition of ship power plants is justified, using the example of an auxiliary engine. A brief analysis of the most common systems of self-diagnostics is given. Features of development and use of artificial intelligence for diagnostics of internal combustion engines are described. The algorithm for conducting experimental studies to obtain a sample of diagnostic features is incorrect. The characteristics of synthesized neural networks are given and the accuracy of determining the technical state is estimated.
Research on Urban Road Traffic Accident Characteristics and Countermeasures: A Case Study of Ningbo City
Authors: Xiong Xiaoxia , Zhang Shuichao , Shen Yujie 
Published: May 2021

Abstract
Current road traffic safety situation is still challenging globally and needs continuous public attention. Exploring the characteristics of road traffic accidents could improve the understanding of accident causation mechanism and help develop countermeasures to reduce the accident occurrences. The paper first explores the general characteristics of urban road traffic accidents from the perspectives of temporal, spatial, and personal distributions. Random Forest (RF) Model is then utilized to identify important features affecting the occurrence of traffic accidents. Accordant countermeasures are finally discussed and proposed.
Research on Blood Supply Chain Optimization under Supply Unreliable and Stochastic Demand
Authors: Zhao Kangli , Xiang Changcheng , Chen Yan 
Published: May 2021

Abstract
This paper explores the problem of blood supply chain optimization under the double uncertainty of supply and demand, considers the impact of supply unreliability, demand difference and different inventory allocation methods on supply chain performance, and constructs a mathematical model of multi-cycle operation of blood supply chain. The blood supply chain system expiration rate under different strategy combinations was obtained through computer simulation optimization. Through the analysis of the results, it is found that the supply fluctuation has a greater impact on the performance of the supply chain. The performance adjustment effect of the distribution mode itself is small. Expiration due to short-term demand fluctuations is greater than the expiration rate caused by long-term demand fluctuations. Proposed a blood supply chain operation strategy for different supply environments.
Entrance Location Planning of HOV Lanes in Multi-lane Highway Considering System and HOV Lane State
Authors: Zang Guangzhi 
Published: May 2021

Abstract
The high-occupancy vehicle (HOV) lane is an effective traffic demand management that encourages carpooling. In this study, we examine entrance location planning of HOV lanes. A highway commuting system containing HOV lanes is proposed. Commuters make travel choices based on the principle of minimum commuting time, i.e., the system is in the user equilibrium state. By planning the HOV lanes’ entrance location, the manager indirectly influences the commuters’ travel choices, thus achieving the predetermined management goals. In this study, an optimization model is developed where the decision variable is the entrance location of the HOV lane, and the conditional constraint of user equilibrium is satisfied. The objective function is a weighted average of a system state indicator and an HOV lane state one. Numerical examples show that the proposed method solves the location planning problem of the HOV lane entrance. The quantitative results reveal that the system congestion is relieved when the HOV lane entrance is close to the city center, and the traffic condition of the HOV lane is improved when the entrance is close to the city’s edge.
Cause Analysis of Railway Traffic Accidents Based on Random Forest
Authors: Wang Minxuan , Liu Di 
Published: May 2021

Abstract
With the rapid growth of the railway operation scale, all kinds of railway traffic accidents happen from time to time, so it is great significant to accurately identify the main influencing factors and their influence degree. In this paper, random forest model is proposed to analyze the cause of railway traffic accidents. Considering people, equipment, environment and other aspects, 11 influencing factors were extracted from 491 accident data. The influence degree of different factors on the severity of the accident is judged through variable importance measures of the random forest. On the basis of this, some suggestions are put forward for raising the safety level of railway transportation. The results show that the random forest model is accurate for analyzing the causes of railway traffic accidents, which can provide decision support for railway transportation safety management.
Research on Selection of General Aviation Rescue Airports
Authors: Hu Bin , Pan Fang , Zhang Yunfei 
Published: May 2021

Abstract
Considering the complex factors affecting rescue in natural disaster-prone areas in China and the high efficiency of general aviation rescue, this paper combines a hierarchical analysis model and a P-median model to establish a two-stage hierarchical selection. A greedy take-away heuristic algorithm is proposed to combine the qualitative and quantitative analysis of the airport selection method. Finally, the Longmenshan earthquake zone was selected for analysis to verify the practicability of the method.
Prediction of the Quantity of Boxed Meals Preparation by High-Speed Train Based on ELM
Authors: Zhou Feifei , Yang Hao , Wang Jiahe , Sun Xi’an , Wu Xu 
Published: May 2021

Abstract
Meal service is an important part of the railway transportation service, and when preparing the number of boxed meals before departure, the train must meet the needs of the train passengers for self-catering and food service. The demand of meals avoids causing too much waste. Extreme learning machine has the characteristics of fast learning speed and generalization ability, and has a wide range of applications in neural network prediction, this paper from the train perspective, by applying and improving the ELM model and using some of the high-speed train data from the Beijing-Qingdao section for model training. A prediction error analysis was performed to verify the reasonableness and feasibility of the model for the prediction of boxed meals.
Third-party Payment Services Selection for Logistics Public Information Platforms
Authors: Shi Xianlian , Deng Jianxin , Su Xiaogen , Cao Chenying 
Published: May 2021

AbstractThe payment mode has important effects on business transactions. This paper focuses on decision-making research on third-party payment services selection for logistics information platforms (LPIPs). From the point of view of technology adoption and LPIPs, six selection criteria of third-party payment services were proposed based on UTAUT model. An optimal decision-making approach measured by these criteria was put forward based on the multi-attribute decision-making theory. The final criteria weights were adjusted by the initial individuals separately obtained by the relative comparison method and entropy evaluation method. And the final evaluation of all was obtained by weighted averaging. An illustrative case was carried out based on the former method. It is indicated that the proposed decision-making method is effective and could well distinguish different third-party payment software for LPIPs in quantitative way.
Calculation and Analysis of Gauges for Suspension-type Monorail Vehicle
Authors: Zhan Shen , Chen Bingyan , Zou Jiancai , Qu Wenqiang , Wang Xiaojun 
Published: May 2021

Abstract
The differences between two types of monorail vehicles are compared, and the configuration of suspension-type monorail vehicle is introduced. The vertical and lateral displacements of carbody under two running conditions are derived from the corresponding directional motions, which is the basis on calculating the vehicle gauge for suspension-type monorail vehicles. A program is written to calculate the displacement and then the results are transformed into vehicle gauges. The validity of the derivation formulas is verified.
Drugs Sale Forecasting Based on SVR Integrated Promotion Factors
Authors: Liu Yang , Yang Xiao , Zhu Cuiling , Meng Jie 
Published: May 2021

Abstract
In order to improve the inventory management level of pharmaceutical chain enterprise, drug sales forecasting is needed. Using machine learning SVR model, we forecast the drugs sale amount with a relatively high accuracy. Suppose the sales amount is influenced by promotion strategy, we integrated the promotion factors into the SVR model. Experimental result of drug sales prediction on the real sales data of a large chain drug company S shows that the SVR algorithm integrated with promotion factors get the accuracy rate of 91% and the algorithm can greatly improve the drug sales prediction results compared with traditional time series model.
A high resolution and high detection efficiency depth-encoding detector for brain positron emission tomography based on a 0.75 mm pitch scintillator array
Authors: Du J. , Cherry S.R. 
Published: May 2021

Abstract
The quantitative accuracy and precision of brain positron emission tomography (PET) studies can be considerably improved using dedicated brain PET scanners with a uniform high resolution and a high sensitivity across the brain volume. One approach to building such a system is to construct the PET scanner using depth-of-interaction (DOI) encoding detectors with finely segmented and thick crystal arrays. In this paper, the performance of a DOI PET detector based on two 16 × 16 arrays of 2 × 2 mm^2 SiPMs coupled to both ends of a 44 × 44 array of 0.69 × 0.69 × 30 mm3 polished LYSO crystals was evaluated at different temperatures (-9∘C, 0∘C, 10∘C, and 20∘C) for brain PET applications. The pitch size of the LYSO array is 0.75 mm. The flood histograms show that all the crystal elements in the LYSO array can be resolved except some edge crystals, due to the limited light sharing. The average energy resolution, average DOI resolution, and average timing resolution across crystal elements are 21.1 ± 3.0%, 3.47 ± 0.17 mm, and 1.38 ± 0.09 ns, respectively, which were obtained at a bias voltage of 56.5 V and a temperature of 0∘C.
Design of sustainable dryland crop rotations require value judgements and efficient trade-offs
Authors: Hochman Zvi , Garcia Javier Navarro , Horan Heidi , Whish Jeremy , Bell Lindsay 
Published: May 2021

Abstract
In agriculture, sustainability is framed as an aspiration to achieve multiple goals including positive production, environmental and social outcomes. These aspirations include: increasing production of nutritious food; minimising risk and maximising resilience in response to climate variability, fluctuating markets and extreme weather events; minimising impacts on global warming by reducing emissions; efficiently using limited resources; minimising negative on-site and off-site impacts; preserving biodiversity on farm and in nature; and achieving positive social outcomes reflected in farmers’ incomes (revenue and profit). Here we used cropping systems simulation to assess multiple (11) sustainability indicators for 26 crop rotations to quantify their sustainability throughout Australia’s subtropical cropping zone. Results were first expressed via a series of maps quantifying the minimal environmental impacts of attributes such as N applied, N leached, runoff and GHG emissions of the 26 crop rotations while identifying the locations of the optimal rotation for each attribute. Inspection of these maps showed that different rotations were optimal, depending on both location and the attribute mapped. This observation demonstrated that an 11-way sustainability win-win across all attributes was not likely to happen anywhere in the cropping zone. However, rotations that minimised environmental impacts were often among the more profitable rotations. A more holistic visualisation of the sustainability of six contrasting sites, using sustainability polygons, confirmed that trade-offs between sustainability indicators are required and highlighted that cropping in different sites is inherently more or less sustainable, regardless of the rotations used. Given that trade-offs between the various sustainability attributes of crop rotations are unavoidable, we plotted trade-off charts to identify which rotations offer an efficient trade-off between profit and other sustainability indicators. We propose that these maps, sustainability polygons and trade-off charts can serve as boundary objects for discussions between stakeholders interested in achieving the sustainable intensification of cropping systems.
Large deviations of currents in diffusions with reflective boundaries
Authors: Mallmin E , du Buisson J , Touchette H 
Published: May 2021

Abstract
We study the large deviations of current-type observables defined for Markov diffusion processes evolving in smooth bounded regions of

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d

with reflections at the boundaries. We derive for these the correct boundary conditions that must be imposed on the spectral problem associated with the scaled cumulant generating function, which gives, by Legendre transform, the rate function characterizing the likelihood of current fluctuations. Two methods for obtaining the boundary conditions are presented, based on the diffusive limit of random walks and on the Feynman–Kac equation underlying the evolution of generating functions. Our results generalize recent works on density-type observables, and are illustrated for an N-particle single-file diffusion on a ring, which can be mapped to a reflected N-dimensional diffusion.
The Multifractal Characteristics of Seismic Activities in China
Authors: Xu Linfeng , Chen Jiemin , Liu Zhixin , Liu Yan , Tian Jiawei 
Published: May 2021

Abstract
In recent years, with the frequent occurrence of geological disasters, the prediction and analysis of earthquakes has gradually become a hot topic. This paper studies the multifractal spatio-temporal characteristics of China seismic energy. The study data derives from the seismic activities in the Mainland China from 1970 to 2013. In this study, it comes to a comprehensive conclusion of spatial and temporal multifractal characteristics of earthquake activities in China. The study area is divided into five regions: The Xinjiang region, the Qinghai-Tibetan Plateau region, the Northern China, the Northeast China and the Southern China. In this study, we analyze the spectrum curves of the seismic releasing energy time series to each region, and compare the differences. Then the differences of the multifractal characteristics in the time dimension can be pointed out based on the curve, and the law of earthquake occurrence is analyzed from the sequence diagrams of time intervals among various tectonic areas. In the spatial dimension, it discrete seismic data points for each region through rectangular square grid at different resolutions as the input, then derive the multifractal spectrum curve of spatial characteristic to each region. By analyzing the multifractal dimension, it explores the similarities and differences of the spatial and temporal characteristics of the earthquake among five regions.