bluuebunny/crossref_metadata_2025_split huggingface dataset:
Optimal treatment plan adaptation using mid-treatment imaging biomarkers
Authors: ten Eikelder S C M , Ferjančič P , Ajdari A , Bortfeld T , den Hertog D , Jeraj R 
Published: October 2020

Abstract
Previous studies on personalized radiotherapy (RT) have mostly focused on baseline patient stratification, adapting the treatment plan according to mid-treatment anatomical changes, or dose boosting to selected tumor subregions using mid-treatment radiological findings. However, the question of how to find the optimal adapted plan has not been properly tackled. Moreover, the effect of information uncertainty on the resulting adaptation has not been explored.
In this paper, we present a framework to optimally adapt radiation therapy treatments to early radiation treatment response estimates derived from pre- and mid-treatment imaging data while considering the information uncertainty. The framework is based on the optimal stopping in radiation therapy (OSRT) framework. Biological response is quantified using tumor control probability (TCP) and normal tissue complication probability (NTCP) models, and these are directly optimized for in the adaptation step. Two adaptation strategies are discussed: (1) uniform dose adaptation and (2) continuous dose adaptation. In the first strategy, the original fluence-map is simply scaled upwards or downwards, depending on whether dose escalation or de-escalation is deemed appropriate based on the mid-treatment response observed from the radiological images. In the second strategy, a full NTCP-TCP-based fluence map re-optimization is performed to achieve the optimal adapted plans.
We retrospectively tested the performance of these strategies on 14 canine head and neck cases treated with tomotherapy, using as response biomarker the change in the 3’-deoxy-3’[(18)F]-fluorothymidine (FLT)-PET signals between the pre- and mid-treatment images, and accounting for information uncertainty. Using a 10% uncertainty level, the two adaptation strategies both yield a noteworthy average improvement in guaranteed (worst-case) TCP.
Addendum: Electron-cyclotron heating and kinetic instabilities of a mirror-confined plasma: the quasilinear theory revised (2020 Plasma Phys. Control. Fusion 62 065005)
Authors: Shalashov A G , Gospodchikov E D , Izotov I V 
Published: October 2020

Abstract
We explain and fix a mistake of the original paper related to derivation of the Fokker–Planck kinetic equation for the distribution function in the non-canonical (not Hamiltonian) phase space.
Magnetically induced elastic deformations in model systems of magnetic gels and elastomers containing particles of mixed size
Authors: Fischer Lukas , Menzel Andreas M 
Published: October 2020

Abstract
Soft elastic composite materials can serve as actuators when they transform changes in external fields into mechanical deformation. Here, we theoretically address the corresponding deformational behavior in model systems of magnetic gels and elastomers exposed to external magnetic fields. In reality, such materials consist of magnetizable colloidal particles in a soft polymeric matrix. Since many practical realizations of such materials involve particulate inclusions of polydisperse size distributions, we concentrate on the effect that mixed particle sizes have on the overall deformational response. To perform a systematic study, our focus is on binary size distributions. We systematically vary the fraction of larger particles relative to smaller ones and characterize the resulting magnetostrictive behavior. The consequences for systems of various different spatial particle arrangements and different degrees of compressibility of the elastic matrix are evaluated. In parts, we observe a qualitative change in the overall response for selected systems of mixed particle sizes. Specifically, overall changes in volume and relative elongations or contractions in response to an induced magnetization can be reversed into the opposite types of behavior. Our results should apply to the characteristics of other soft elastic composite materials like electrorheological gels and elastomers when exposed to external electric fields as well. Overall, we hope to stimulate corresponding experimental realizations and the further investigation on the purposeful use of mixed particle sizes as a means to design tailored requested material behavior.
Nitrogen infusion R&D at DESY a case study on cavity cut-outs
Authors: Wenskat Marc , Bate Christopher , Dangwal Pandey Arti , Jeromin Arno , Keller Thomas F , Knobloch Jens , Köszegi Julia , Kramer Felix , Kugeler Oliver , Kulkarni Satish , Reschke Detlef , Schaffran Jörn , Dalla Lana Semione Guilherme , Sievers Sven , Steder Lea , Stierle Andreas , Walker Nicholas 
Published: October 2020

Abstract
A first series of nitrogen infusion runs of 1.3 GHz single-cell cavities at DESY resulted in an unexpected and severe deterioration observed during the vertical cold test. To investigate the origin of the deterioration, one of the cavities underwent extensive radio-frequency measurements and a temperature- and magnetic field-mapping was performed in collaboration with the Helmholtz-Zentrum Berlin. After combining all results, regions of interests were identified and cut-out of the cavity. Subsequent surface analysis techniques (EBSD, PALS, PIXE, SEM/EDX, SIMS, XPS) were applied in order to identify the microscopic origin of the deterioration and especially the differences between hot and cold spots as well as quench spots. An excess of niobium carbides, reducing the thermal conductivity, was identified as the probable cause for the deterioration, and the size- and density-distributions were observed to be crucial for the resulting performance reduction. The origin for the local differences in the niobium carbide formation between hot and cold spots is an effect of preexisting variations of the crystal structure.
Evidence for Radio Loud to Radio Quiet Evolution from Red and Blue Quasars
Authors: Garofalo David , Bishop Katie 
Published: October 2020

Abstract
Recent work on red and blue quasi-stellar objects (QSOs) has identified peculiar number distributions as a function of radio-loudness that we explore and attempt to explain from the perspective of a picture in which a subset of the population of active galaxies evolves from the radio-loud to the radio-quiet state. Because the time evolution is slowed down by an order of magnitude or more for the radio-quiet phase, the numbers of red and blue QSOs approach each other at the extreme end of the radio-quiet range of radio-loudness with larger numbers. The rapid time evolution of most radio-loud phases, instead, makes the numbers similar but lower at the far radio-loud end. At the midpoint of radio-loudness, instead, the differences between red and blue QSOs experience their largest values which results from accretion rapidly spinning black holes down but subsequently spinning them up more slowly. Recovering these basic features of the observations is evidence for rapid evolution away from radio-loudness and slow evolution in radio-quiet states.
Solution to the Sudakov suppressed Balitsky-Kovchegov equation and its application to HERA data *
Authors: Xiang Wen-Chang , Wang Meng-Liang , Cai Yan-Bing , Zhou Dai-Cui 
Published: October 2020

Abstract
We analytically solve the Sudakov suppressed Balitsky-Kovchegov evolution equation with fixed and running coupling constants in the saturation region. The analytic solution of the S-matrix shows that the

rapidity dependence of the solution with the fixed coupling constant is replaced by the

dependence in the smallest dipole running coupling case, as opposed to obeying the law found in our previous publication, where all the solutions of the next-to-leading order evolution equations comply with

rapidity dependence once the QCD coupling is switched from the fixed coupling to the smallest dipole running coupling prescription. This finding indicates that the corrections of the sub-leading double logarithms in the Sudakov suppressed evolution equation are significant, which compensate for a part of the evolution decrease of the dipole amplitude introduced by the running coupling effect. To test the analytic findings, we calculate the numerical solutions of the Sudakov suppressed evolution equation, and the numerical results confirm the analytic outcomes. Moreover, we use the numerical solutions of the evolution equationto fit the HERA data. This demonstrates that the Sudakov suppressed evolution equation can achieve a good quality fit to the data.
The noise and impedance of microelectrodes
Authors: Mierzejewski Michael , Steins Helen , Kshirsagar Pranoti , Jones Peter D 
Published: October 2020

Abstract

Objective. While the positive correlation between impedance and noise of microelectrodes is well known, their quantitative relationship is too rarely described. Knowledge of this relationship provides useful information for both microsystems engineers and electrophysiologists. Approach. We discuss the physical basis of noise in recordings with microelectrodes, and compare measurements of impedance spectra to noise of microelectrodes. Main results. Microelectrode recordings intrinsically include thermal noise,

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, with the real component of impedance integrated over the recording frequency band. Impedance spectroscopy allows the quantitative prediction of thermal noise. Optimization of microelectrode noise should also consider the contribution of amplifier noise. These measures enable a quantitative evaluation of microelectrodes’ recording quality which is more informative than common but limited comparisons based on the impedance magnitude at 1 kHz. Significance. Improved understanding of the origin of microelectrode noise will support efforts to produce smaller yet low noise microelectrodes, capable of recording from higher numbers of neurons. This tutorial is relevant for single microelectrodes, tetrodes, neural probes and microelectrode arrays, whether used in vitro or in vivo.
Design and Development of 220kV SF6 Gas Insulated GIS Actual Testing Platform
Authors: Zhang Shiling 
Published: October 2020

Abstract
To simulate the performance parameters of SF6 gas insulated AC GIS equipment under normal and failure condition in the laboratory environment, and the development trend of characteristic parameters such as partial discharge and SF6 gas decomposition can be monitored periodically and quantitatively, the 220kV SF6 gas insulated GIS type test platform is designed and developed. The test platform is in accordance with GIS specifications used in substation, including gas insulated circuit breaker and other typical GIS equipment. The external voltage is realized by the series resonant circuit. The paper starts from the design of the GIS bus and the solid insulation structure, and the three-dimensional E-field distribution characteristics of the solid insulator are simulated and analyzed. The hardware installation of the partial discharge measurement and SF6 gas decomposition monitoring on GIS model test platform are discussed. Through design and development of 220kV SF6 gas insulated GIS type-test platform, the feasibility of the structure design of GIS bus and the solid insulation are verified, it provides theoretical and practical basis for the operation and maintenance of SF6 gas insulated GIS equipment.
Natural scene text detection based on YOLO V2 network model
Authors: Haifeng Dong , Siqi Han 
Published: October 2020

Abstract
As an important part in the field, the natural scene text detection has been widely applied in visual navigation system, content-based image and video retrieval, instant translation system and so on. In this paper, we introduce several object detection network based on deep learning, and apply the YOLO v2 into natural scene text detection, changing the multi objects detection problems into the two classification problems. The main works in the paper include the following: prepare the datasets; we train the YOLO v2 with the optimum parameters, carry out the regression analysis of the coordinate parameters and categories of bounding boxes, obtain the detection result; according to different detection models, the detection results of different datasets are compared and analyzed, YOLO V2 model detection speed 0.105s/image has certain advantages.
The LSTM Neural Network Based on Memristor
Authors: Chu Ziqi , Xu Hui , Liu Haijun 
Published: October 2020

Abstract
The recurrent neural network adds the concept of time series on the basis of the traditional multi-layer feedforward neural networks, provides the memory function, and makes the network show good modeling ability on time-series data. Therefore, this paper proposes a LSTM (Long Short-Term Memory) neural network based on memristor. It establishes a discrete weighted LSTM network model by simplifying the traditional recurrent neural network, and uses memristor arrays on the premise of ensuring recognition performance. We realize the function of weight matrix to improve the structure of LSTM neural network, and finally carry out simulation research on the proposed neural network. And due to the volatility and yield of memristors, this paper also demonstrates and analyzes the impact of these two characteristics on network performance, and the performance level of the LSTM neural network based on memristor is verified under the existing preparation level. Experiments on the TIMIT speech database show that the proposed neural network in this paper has good accuracy and its speech recognition performance is superior.
Design and Implementation of Automatic Code Generation Method Based on Model Driven
Authors: Chen Hailang 
Published: October 2020

Abstract
With the continuous development of software development technology, people have summarized more and more repetitive work in the process of software development. These repetitive tasks increase the developer’s workload of repetitive coding, extend the market demand response time, and then increase development costs. At the same time, in the team development process, due to the uneven code writing ability among the team members, the coding style is not uniform, and the quality and maintainability of the code cannot be guaranteed. The article builds a software development model from the perspective of public functions such as software addition, deletion, modification, and query. On this basis, a code automatic generation engine based on Rete algorithm is designed to realize rapid software code generation. Application practice shows that this method can quickly generate codes with uniform coding style, and improve software development efficiency and code quality.
Construction of Blended ESP Teaching Model and Performance Evaluation System in the Context of “Internet +”
Authors: Ran Zhao 
Published: October 2020

Abstract
The “Internet +” era presents new possibilities and new requirements for ESP education. Online teaching under the background of the “Internet +” is to enable learners to make efficient use of fragmented time. The process and requirement of blended teaching is to break through the ideology of “learning only in the classroom”, so that students can spend time in learning even after class. There is a problem of overload of information in online platform teaching, which puts a certain burden on students’ knowledge construction, aggravates the cognitive load of students, and the lacks of teachers’ summarization and guidance. The article is to explore how to build a blended ESP learning model and corresponding performance evaluation system to make ESP learning in the “Internet +” context more systematic and efficient.
Research on Container Throughput Forecast Based on ARIMA-BP Neural Network
Authors: Zhang Yifei , Fu Yuhui , Li Genghua 
Published: October 2020

Abstract
In order to improve the accuracy of the container throughput, the paper proposed a prediction method based on ARIMA-BP neural network for the container throughput, and compared with the combined prediction method based on ARIMA-BP neural network, from the perspective of simple weighting and residual optimization. It is applied to the container throughput prediction of the Qingdao port statistics for a total of 24 quarters from 2014-2019. The results show that the prediction accuracy of the combination prediction method based on residual optimization was the highest. Compared with other prediction models, the evaluation indexes RMSE(Root Mean Square Error), MAE(Mean Absolute Error), and MAPE(Mean Absolute Percentage Error) were 15.95, 13.31 and 2.52% respectively and the prediction accuracy based on the BP neural network was lowest. The prediction method proposed in this paper for container throughput can provide guidance for the related personnel.
An Interpretable Framework for Stock Trend Forecasting
Authors: Wang Lewen , Ye Zuoxian 
Published: October 2020

Abstract
Stock trend forecasting plays a critical role when investing in the stock market. Comparing with traditional technical analysis and fundamental analysis, deep learning models own better forecasting performance. However, the poor interpretability of deep learning models brings lots of limitations to its practical application since the lack of interpretability increases the investment risk. In this paper, we propose a graph-based framework, which owns good interpretability while maintaining forecasting performance. Specifically, the framework explains the stock returns by dividing the returns into multiple parts: 1) the part related to individual stock; 2) the part related to company business; 3) the part related to the corresponding industry; 4) the part associated with the whole market. Extensive experiments on real-world Chinese stock market data have demonstrated the effectiveness of our proposed framework for stock trend forecasting. Afterward, we try to illustrate the interpretability of our framework.
Reactive power optimization of distributed photovoltaic access distribution network based on improved multi-objective particle swarm optimization
Authors: Xiaomeng Wu , Mingyue Yang 
Published: October 2020

Abstract
With the widespread development of distributed power generation technology, photovoltaic power generation has been widely used. Aiming at the past, the reactive power optimization algorithm with distributed photovoltaic access distribution network needs to convert the multi-objective problem into a single-objective solution process where the weight setting is not objective. This paper proposes a multi-objective optimization algorithm based on improved particle swarm optimization algorithm. The multi-objective model based on Pareto optimal solution is used to obtain the Pareto optimal solution set, and the optimal solution is selected from it. At the same time, the algorithm is improved, the external file is used to update the optimal position of the particle, the population diversity is increased to avoid premature problems, and the performance of the algorithm is improved by analyzing the test function. Through modeling, the influence of distributed photovoltaic power generation on the power grid is studied, and simulation is performed in the IEEE-33 node system. The simulation results prove the feasibility of the improved algorithm in this paper.
Research on the stiffness of Grillage Method in Virtual Cross Beam based on Fuzzy recognition
Authors: Sun Lixiang 
Published: October 2020

AbstractThe stiffness of virtual cross beam is an important parameter which affects the calculation results when using beam grillage method to analyze the stress of bridge structure. In order to obtain a reasonable value of the stiffness about the virtual cross beam, it is necessary to make comparative analysis. On the basis of the comprehensive comparative analysis of the stress and deflection of the control section in hollow slab bridge and simply supported beam bridge with twin-box single cross section, the value range of the virtual cross beam stiffness is obtained. Based on the theory of fuzzy mathematics, a fuzzy recognition model for the stiffness of the virtual beam was proposed. By calculating the relative membership degree vector of the stiffness in each virtual beam, the length and width of the cross section of the virtual beam is obtained to be 0.7 times height of the beam. In order to further verify the reasonable of the value, the stress and deflection values of the control section have been compared. These values are taken from the single-box and double-chamber plexiglass box girder model and highway single-box three-chamber box beam. The compared results show that the solution of the grillage model with the stiffness of the virtual beam in this paper is in good agreement with shell solutions and solid solutions.
Leukemic cell detection model based on deep learning
Authors: Chen Hongyi 
Published: October 2020

Abstract
This project examines the feasibility of building a deep learning model that can accurately distinguish leukemic cells from normal cells, as well as the viability of analyzing the black box model so as to explain it in human terms. An accurate and explainable model can be deployed in areas previously inaccessible to the limited existing methods of detecting leukemia in its early stages. It is acknowledged that this is a very difficult process, as cells from individuals vary and cancerous cells are highly morphologically similar to normal cells.
There are two major steps to this project. First, we built the cell classification model by supervised training on a convolutional neural network with provided labeled training data. Many parameters had to be adjusted to yield better results when tested on separate testing data. Second, we created a program designed to help explain the neural network, and the model is inserted into the program and tested on multiple examples. We examined the results, which highlighted regions the algorithm deems important in making its decision, and attempted to explain the algorithm as much as possible.
Though far from perfect, the results are intriguing. Our algorithm had an accuracy of 68% on the testing data set. This is clearly suboptimal; however, this is also significantly higher than random results, demonstrating that our algorithm learnt useful processes and that this research direction is worth exploring further. The main difficulty is the aforementioned similarity between leukemic and normal cells; thus, the training process must be complex and fine-tuned to yield good results. After analyzing the results from the explanation program, we determined that our algorithm focuses on the contour of cells in its decision-making process, especially unsmooth or protruded sections. To conclude, our algorithm is far from the level required for practical applications; however, we demonstrated that such algorithms are possible and can be explained.
Research on Digital, Networked and Intelligent Manufacturing of Modern Ship
Authors: Li Hao 
Published: October 2020

Abstract
The development of ship intelligent manufacturing is divided into three stages of digital, networked and intelligent manufacturing. In the digital manufacturing stage, shipbuilding enterprises strengthen the relationship between manufacturing and design to improve the efficiency of manufacturing. In the networked manufacturing stage, shipbuilding enterprises use the internet of things technology to improve the controllability in the production process and effectively enhance the coordination and cooperation between machines and persons. In the intelligent manufacturing stage, shipbuilding enterprises let the new generation of artificial intelligence technology run through the design, management, service and production, greatly improving the flexibility of manufacturing. This paper studies the application model of industrial design system in ship design, the application model of internet of things technology in the production control of shipbuilding enterprises, and the application model of new generation artificial intelligence technology in intelligent decision support, which makes a preliminary exploration for the intelligent development of shipbuilding enterprises.