bluuebunny/crossref_metadata_2025_split huggingface dataset:
Diagnostisches und therapeutisches Management der Episkleritis und Skleritis
Authors: Turgut Ferhat , Dingerkus Vita , Tappeiner Christoph , Becker Matthias 
Published: February 2023

ZusammenfassungDas schmerzhafte „rote Auge“ ist wohl das auffälligste Zeichen einer Augenerkrankung, hinter der milde, aber auch schwere Verläufe stecken können. Die Episkleritis und Skleritis sind hierbei wichtige Differenzialdiagnosen. Die rechtzeitige Diagnose ist nicht nur wichtig für die notwendige Therapie, sondern kann vor einer irreversiblen Visusminderung, einem Augenverlust sowie Organschäden schützen und ggf. sogar das Mortalitätsrisiko senken.
Quality Improvement Initiative Implementation at the Unit or Hospital Level
Authors: Colley Alexis , Wick Elizabeth 
Published: February 2023

AbstractQuality improvement efforts take considerable commitment, including mentorship, training, and resources. Leveraging an established framework, such as that outlined by the American College of Surgeons, to design, implement, and analyze quality improvement projects offers the best chance for success. Herein, we illustrate the application of this framework to a gap in advance care planning for surgical patients. This article helps outline how to go from identifying and outlining a problem, to articulating a clearly defined project goal that is specific, measurable, attainable, relevant, and timebound, and later implementing and analyzing a gap in quality identified at the unit (e.g., service line, inpatient unit, clinic) or hospital level.
Plastic waste in surface waters of an urban estuary
Authors: Santos Elizângela Alves dos , Silva Cavalcanti Jacqueline Santos 
Published: February 2023

Context Plastic pollution is a threat to ecosystems and is a global problem. Aims We evaluated the spatio-temporal degradation stage of plastic waste found in surface waters (<1 m) of the Capibaribe River estuary. Methods The analysed waste items were sampled in three distinct areas within the estuary, namely, upper, middle and lower regions. Key results In total, 381 items of debris were classified as macroplastics (≤25 mm), with soft fragments (83.9%) and hard fragments (4.9%) being predominant. Advanced degradation stage occurred throughout the estuary (90.5%). A concentration of 1757 microplastics (<2.2 mm) was found predominant in the upper and middle regions. Soft fragments corresponded to the most abundant type (69%). Conclusion In the middle estuary, a plastic hotspot was observed, caused by the flood tide flow, river flow and the flow of a tributary, forming a convergence cell that helps retain plastic debris for long periods. Implications Identifying the origin, sources and how the hydrodynamic characteristics influence the distribution of plastic debris along the estuary is relevant because it will help decision-makers to develop municipal and state action plans for the recovery of this ecosystem.
The SLC25A47 locus controls gluconeogenesis and energy expenditure
Authors: Yook Jin-Seon , Taxin Zachary H. , Yuan Bo , Oikawa Satoshi , Auger Christopher , Mutlu Beste , Puigserver Pere , Hui Sheng , Kajimura Shingo 
Published: February 2023

Mitochondria provide essential metabolites and adenosine triphosphate (ATP) for the regulation of energy homeostasis. For instance, liver mitochondria are a vital source of gluconeogenic precursors under a fasted state. However, the regulatory mechanisms at the level of mitochondrial membrane transport are not fully understood. Here, we report that a liver-specific mitochondrial inner-membrane carrier SLC25A47 is required for hepatic gluconeogenesis and energy homeostasis. Genome-wide association studies found significant associations between SLC25A47 and fasting glucose, HbA1c, and cholesterol levels in humans. In mice, we demonstrated that liver-specific depletion of SLC25A47 impaired hepatic gluconeogenesis selectively from lactate, while significantly enhancing whole-body energy expenditure and the hepatic expression of FGF21. These metabolic changes were not a consequence of general liver dysfunction because acute SLC25A47 depletion in adult mice was sufficient to enhance hepatic FGF21 production, pyruvate tolerance, and insulin tolerance independent of liver damage and mitochondrial dysfunction. Mechanistically, SLC25A47 depletion leads to impaired hepatic pyruvate flux and malate accumulation in the mitochondria, thereby restricting hepatic gluconeogenesis. Together, the present study identified a crucial node in the liver mitochondria that regulates fasting-induced gluconeogenesis and energy homeostasis.
Distribution grid fault diagnosis based on fault indicator and random matrix
Authors: Li Fan , He Xing , Chen Ran , Wu Yi , Tian Yingjie 
Published: February 2023

Abstract
The analysis and utilization of massive recording data of distribution grid fault indicator is beneficial to improve the effect of distribution grid fault diagnosis. In this paper, the distribution grid monitoring are realized by the random matrix theory (RMT) of high dimensional statistical analysis. The fault diagnosis method based on RMT has the advantages of no need for detailed distribution grid topology, comprehensive utilization of wide-area spatiotemporal data, and observation from a muti-dimensional view. By explaining the application principle of limit spectrum distribution function, the linear eigenvalue statistics (LES) is proposed as the state monitoring index. Distribution grid fault diagnosis based on fault indicator and RMT provides a new data-driven method for distribution grid fault diagnosis while efficiently utilizing massive fault indicator data.
Supply chain quality level decision considering block-chain technology
Authors: Zheng Jinyan , Feng Liangqing 
Published: February 2023

Abstract
Aiming at the problem of unqualified product quality, a two-level supply chain game model under two modes is established. The backward induction method is used to solve the decision variables to be analyzed and explore the application conditions of blockchain technology. We found that the supply chain with the smaller application cost, the smaller qualified rate of product quality and service quality, and the larger loss of reputation is the most suitable to use blockchain technology. Product quality, service quality and market price are improved. In addition, we also discuss the influence of sensitivity coefficient on product quality and service quality. The smaller the cost coefficient of product quality and the larger the importance of product quality are, the better the effect of product quality improvement is. The smaller the cost coefficient of service quality and the larger the importance of service quality are, the better the effect of service quality improvement is. It provides a reliable basis for better quality decision.
EKConv: Compressing Convolutional Neural Networks with Evolutionary Kernel Convolution
Authors: Yang Dengjie , Chen Zhiwei , Sun Yi , He Qing , Ye Shiwei , Chen Deyuan 
Published: February 2023

Abstract
Convolutional neural networks (CNNs) have achieved tremendous success in visual recognition tasks but mainly rely on massive learnable parameters. To solve this problem, many effective and efficient convolution operators have been proposed, such as group-wise convolution, point-wise convolution, and depth-wise convolution. However, the above convolution operations model and optimize the weight relationship within the same convolutional layer. To reduce the network parameters, we explicitly construct the relationship between convolution kernels of adjacent layers. Specifically, we propose an evolutionary kernel convolution, namely EKConv, to generate weight parameters by group-wise convolution efficiently. In particular, EKConv makes the kernel parameters of the current convolutional layer inherit from its preceding adjacent kernel, which promotes the information exchange between convolution kernels. More importantly, EKConv is a novel plug-and-play module that can be easily embedded into mainstream CNNs. Extensive experimental results show that EKConv can compress the parameters of CNNs by a large margin yet barely sacrifice image classification performance.
Research on pollution identification of catenary insulator based on Improved Gray Wolf algorithm
Authors: Li Zhiyuan , Wang Zhicheng , Zhan Mingxing 
Published: February 2023

Abstract
Support vector machines are used as classifiers for the contact network insulator fouling level recognition model. To improve the accuracy of the recognition model, the search strategy of the gray wolf algorithm is improved, the population is initialized using chaotic mapping rules, and the position of individual gray wolves is updated based on a dynamic weighting strategy. The improved algorithm is applied to the kernel parameter and penalty factor seeking of SVM to find out the optimized parameters to construct the recognition model, and the recognition status of several other intelligent algorithms is compared. The experimental results show that the proposed method can effectively improve the accuracy of insulator fouling level recognition and has certain engineering application value.
Sentinel-2 Research on the Detection and Classification Methods of Maritime Ship Targets from Remote Sensing Images
Authors: He Junjie , Lin Yinan , Shi Fangzhe , Fu Jiajun , Chen Boning 
Published: February 2023

Abstract
There are problems such as low recognition accuracy and large classification error in the existing classification methods for ship identification based on optical remote sensing images. In this paper, we will analyze the characteristics of ships and determine the indicative factors for applying remote sensing to monitor ships in combination with optical remote sensing images. Using optical remote sensing image data, combined with U-Net and AttU-Net deep neural network models, we assist in extracting new remote sensing indices with strong generality and clear physical meaning, and establishing rules for monitoring ships, so as to establish a more general and clear physical meaning of the monitoring and identification method of remote sensing satellite images. The method is applied and evaluated with port optical remote sensing image data. The data show that compared with traditional machine learning methods, the accuracy of ship monitoring using U-Net and AttU-Net deep learning models in this paper reaches 89.04%, and the recall rate and accuracy rate are better than SVM. it shows that the model can detect ships effectively.
Bayes-based air pollution computer measurement and big data outlier detection model
Authors: Liu Fuquan , Yu Tao , Song Guangjia , Yang Jie 
Published: February 2023

Abstract
In today’s rapidly changing social environment, high-dimensional data abound, but not all data are meaningful. In order to reduce costs and reduce consumption, people are more interested in some important variables. Therefore, variable selection of high-dimensional data is an important research direction. Based on the Bayesian method, it stands out among many variable selection methods with effective estimation efficiency and flexible mechanism, and it is also suitable for parametric and non-parametric models. Thanks to the flexibility of the statistical conclusion inference process, combined with prior information, the actual results are more accurate. At present, many indoor pollution problems, including decoration pollution, are getting more and more attention, and its air pollution calculations are obviously very important. Indoor air quality not only directly affects the comfort of the human body, but also significantly affects the work efficiency of family members. As a result of the development of the new digital economy, digital mining technology based on big data has brought unprecedented new development methods to various fields. This paper establishes an outlier detection model, identifies data processes that are significantly different from the normal data set or expected value, and cleans the data to improve the accuracy of the integrated detection model. This paper proposes a solution to this problem by combining big data mining technology with the establishment of an outlier detection model. At the same time, it optimizes the design of the model to reduce errors, so as to realize its accurate application in the measurement of air pollutants, and aims to promote its development.
Simulation Analysis of Inflatable Film Truss Structure Unfolding Process
Authors: Hong Yihong , Zhang Jinyu 
Published: February 2023

Abstract
Inflatable film truss structures have been widely employed in spaceship unfolding sections and supporting structures, and the membrane structure’s folding scheme has a substantial impact on the unfolding effect. In this research, a parametric model of a thin film structure is built using the collaborative fold design approach. The dynamic simulation study of the inflatable film structure was performed using the enhanced spring-particle system and the expansion kinetic behavior and parameters were determined. The results of the study reveal that the simulation process is robust and trustworthy, with no component collisions, evaluating the efficacy of the folding scheme design and providing technical support for such engineering applications..
Research on forecasting approach of key parameters of PWR pressurizer water level
Authors: Xing Ji , Li Wanting , Deng Shiguang , Duan Pengfei , Ma Xiangfeng 
Published: February 2023

Abstract
Nuclear power has special safety requirements and must be generated on the premise of nuclear safety. It is necessary to forecast the abnormal situation of system and equipment as soon as possible, so we can control the accident beforehand and prevent the nuclear safety risk caused by the failure and accident. At present, the pre-accident control method in nuclear power plant mainly relies on the subjective experience of personnel. In contrast, intelligent forecasting and timely reasoning of the change trend and possible impact of the key safety-related parameters in nuclear power plant can bring data reference to the staff of the power plant, help to control the accident before it happens and effectively reduce the probability of accident occurrence. In PWR(Pressurized Water Reactor), safety is closely related to the key parameters of primary loop such as pressurizer water level. Adequate water level of pressurizer is the key to ensure the normal discharge of reactor heat, and abnormal changes of pressurizer water level may lead to nuclear accidents. Therefore, our paper analyzes and models the water level control function of the pressurizer in PWR, the operation history of nuclear power plant related to this function is collected as well. Then a network model is built based on transformer to do information mining, which can successfully forecast the trend of key parameters related to pressurized water level and provides reference information for staff.
Dynamic analysis and vibration control for overhead hoist transport
Authors: Xu Tao 
Published: February 2023

Abstract
The OHT system is a transport device used for intra- or inter-plant transport in a fab or other factory, the load-bearing part of the goods stored under the trolley generates longitudinal vibration and lateral oscillation due to inertia force When the OHT in the air starts or stops. Firstly, the trolley dynamic model under the action of lateral thrust and longitudinal tensile force need to be established. secondly, the coupling analysis of horizontal oscillation and longitudinal oscillation on the model is carried out. Thirdly, aiming at the lateral swing and longitudinal vibration of the carriage, a double closed-loop vibration control strategy with tension control as inner loop and speed control as outer loop and carry out the simulation analysis of longitudinal vibration control is proposed. In the experiment of the ground trolley, the control strategy is applied. The simulation and experimental results show that the control method can quickly restrain the longitudinal vibration of the trolley bearing plate, reduce the pulling force of the trolley on the lifting steel belt, and avoid the large displacement of the bearing plate in the air.
Application exploration of building deep learning model by adjusting algorithm combination relationship
Authors: Liu Xiangju , Liu Yuan , Fan Yanfeng 
Published: February 2023

Abstract
According to the Convention, the basic data type and XML representation algorithm are adopted, and after the organization is completed, it is compiled into Python code.According to the vectors in mathematics, whether the given algorithms share the same type of variables, the forward propagation and back propagation iterative algorithms in deep learning are used to train the model, and a new algorithm is combined according to the value of vectors, which is similar to the forward calculation in deep learning.Using the sample data to run the algorithm, the vector code is adjusted according to the error value in order to achieve convergence, which is similar to the reverse learning process in deep learning.In this way, it is expected to train a suitable model only by adjusting the parameter combination relationship of the algorithm sequence, and the neuron nodes and weight parameters are greatly reduced, which is expected to bring better learning effect.
Smart Enterprises in the Context of Digital Transformation: Basic Characteristics and Development logic
Authors: Wang Xiaohui 
Published: February 2023

Abstract
In recent years, China has attached great importance to the digitalization and intelligent construction of the industry and has made great achievements. A series of plans and programs have been introduced to further promote industrial development and economic growth. This paper analyzes the connotation of an intelligent enterprise based on the industrial digital transformation, which is developed based on the digital transformation of traditional enterprises. It is an enterprise or intelligent operation platform that continuously iterates, develops, innovates, and obtains sustainable competitive advantages by constructing an enterprise innovation network. There are three main characteristics of smart enterprises, which are digitally driven, intelligent operation, and continuous innovation. Furthermore, the development logic of the intelligent enterprise is presented.
Design and Implementation of Project Schedule Management Data Collection and Analysis System
Authors: Hu Xinsheng , Xu Haibo 
Published: February 2023

Abstract
With the development of the information age, the application scope of big data technology is becoming more and more extensive. The rapid development of big data technology provides strong support for the collection and storage of massive data. Mining potential value through data analysis and providing scientific support for decision makers. This paper combines data collection and analysis with project schedule management. First, it introduces the data collection and analysis technology in general, then explains the disadvantages of traditional project schedule management, analyzes the advantages of modern project schedule management, and finally proposes project schedule management data collection and analysis. The design idea of the system is applied to the project management practice of a material acceptance point in a government office, in order to provide a useful reference for the application of data collection and analysis technology in project schedule management.
Model Shielding Efficiency: A Quantitative Index of Evaluating the Influence when Major Data Changes
Authors: Min Yu , Chi Zhang , Min Xu 
Published: February 2023

Abstract
This paper introduces examples, implementation methods and the perspectives of the Star Model and the Dimensional Model applied in the banking. Considering there is no widely-used quantitative indicators to evaluate the influence on the scope of downstream applications when major data changes in engineering practice, this paper proposes a quantitative index ‘Model Shielding Efficiency’, and then compares the actual shielding effect of the Star Model and the Dimensional Model by listing the MSE actually generated during the reconstruction of the Credit Management System and the Accounting System, which are the 2 principal systems in banking. The MSE can be used as a reference in subsequent practical applications.
Highway Bridge Construction Resource Equilibrium Based on Genetic Algorithm Optimization Research
Authors: Li Shengxi 
Published: February 2023

Abstract
At present, highway and bridge projects in China are in full swing. In the process of multi project construction of highway and bridge, it is of great significance to study the project scheduling. On the basis of fully analyzing the specific characteristics of multi projects in highway and bridge construction, this paper studies the problem of “fixed duration resource balance (RB)” based on genetic algorithm (GA). Combined with the specific characteristics of multi projects in highway and bridge construction, it establishes an optimization model, and proposes the process of GA to solve the problem of multi RB in construction projects. When considering the multi project resource balance optimization (RBO) problem of highway and bridge construction, the GA is used to solve the problem. The results show that the model is effective for the multi project RB problem of highway and bridge construction. At the same time, it proves that it has great advantages to consider the RB of multiple projects of highway and bridge construction from the new perspective of resource scheduling cost.
Highway Crack Material Detection Algorithm Based on Digital Image Processing Technology
Authors: Zhang Xiao 
Published: February 2023

Abstract
After entering the 21st century, China’s economic development is changing with each passing day, and the total economic volume has ranked second in the world. After the quantitative problem is solved, the qualitative problem becomes more and more important. In order to make the expressway be used healthily and for a long time, simple and efficient expressway maintenance methods become extremely important. In this paper, based on the study of the predecessors’ related research on road crack identification, based on digital image processing technology, the material detection algorithm for expressway cracks is studied. The advantages and disadvantages of the existing algorithms are analyzed, and the main types of cracks in the expressway are analyzed. The causes and characteristics of each type of cracks are understood, and the problems of current road crack detection and identification algorithms are analyzed. Through the experimental test analysis of the crack material detection algorithm, various types of crack detection images are selected from the horizontal and vertical crack detection results to form a test sample image library. The validity of the classification algorithm is tested, and the test results verify the validity of the algorithm in this paper.
Development of DC Parameter Calibration Module for Digital Integrated Circuit Test System
Authors: Ding Chao 
Published: February 2023

Abstract
Aiming at the on-site measurement requirements of the integrated circuit test system, this paper designs the DC parameter calibration module of the digital integrated circuit test system based on STM32 single-chip microcomputer as the core control, studies the methods of precision improvement based on multiple groups of devices, noise control, and error compensation, reduce the volume of the device, simplifies the measurement loop in the calibration process, and improves the DC parameter calibration accuracy of the digital channel of the integrated circuit test system, improve the portability and ease of use of the calibration device, and lay the foundation for the calibration device of the new generation integrated circuit test system.