bluuebunny/crossref_metadata_2025_split huggingface dataset:
An Application of Deep Learning in Remote Sensing: Automatic Change Detection in Urban Area
Authors: Yang Chun , Zhou Bowen 
Published: February 2021

Abstract
Change detection of earth’s appearance is a core task for urban management, whose main goal is to identify the change of physical materials via comparing remote sensed images (satellite images, aerial images etc.) of many time epochs. For instance, due to urbanization bare soil can be changed to building; river or lake can be filled up by soil for greening purpose. Recently, deep learning based methods for change detection have been widely applied. The standard pipeline here is: given two images of two epochs and the corresponding changed binary labels, they are feed into a deep learning model to learn the change incidents. However, this kind of change detection focuses solely separating change from no-change, yet ignores the information of “from-to”. For example, the change from soil to building is different to the change from water to soil, but in the standard pipeline they are taken as the same changed incident. In this paper, we propose a deep learning method to tackle this problem, i.e. not only detecting the change incidents, but also predict the change types (the “from-to” information). Our methods are evaluated on the competition dataset released by the SenseTime, and achieve promising detection results i.e. >70% in terms of OA.
Auto Whole Heart Segmentation from CT images Using an Improved Unet-GAN
Authors: Le Kening , Lou Zeyu , Huo Weiliang , Tian Xiaolin 
Published: February 2021

Abstract
The development of deep learning is rapid, and convolutional neural network especially U-Net plays an important role in the medical image segmentation tasks, which is lack of data. Lots of models and methods are proposed to segment cardiac CT images. In this paper, we proposed a new network architecture. The network architecture is based on a traditional architecture called conditional generative adversarial network (cGAN), where R2U-Net acts as the generative network and FCN as the discriminative network. The performance of this model running on the dataset from MICCAI 2017 Multi-Modality Whole Heart Segmentation Challenge (MM-WHS 2017) is good.
Study on Improved YOLO_v3-based Algorithm for Identifying Open Windows on Building Facades
Authors: Sun Guangmin , Lin Pengfei , Li Yu 
Published: February 2021

Abstract
In order to ensure the security of building facades in key areas and improve the security detection efficiency of security personnel on building facades, this paper proposes an improved YOLO v3-based algorithm for open window detection recognition in building facade images, which extracts open window features from images to make predictions on full images by convolutional neural network. Firstly, due to the absence of publicly available window datasets in the network and the high number of window types that exist in reality, a self-constructed window dataset containing 13573images of open windows is used to train and test the window detection model. The data set is then clustered by the K-Means clustering algorithm to select an Anchor Box more suitable for window detection, which draws on the ShuffleNet idea to strengthen the feature extraction method, and then optimizes the network structure of YOLO v3. Finally, a block detection mechanism is introduced to effectively enhance the network’s ability to detect small dense targets; experimental results show that the method improves the accuracy and speed of window detection and reduces the workload of security personnel in key areas to manually check for windows on both sides of the street. Fire detection, missing and falling bricks on the floor, and overhead throw detectionare of great importance.
Empirical Research on the Quality of Environmental Accounting Information Disclosure based on SPSS
Authors: Lin Huijuan , Zhang Yan , Liu Xueying 
Published: February 2021

Abstract
In the era of big data, data is undergoing rapid expansion, determining future corporate development. Although many enterprises may not realize the hidden dangers of the explosive data growth, people will become increasingly aware of the importance of data to enterprises. Faced with excessive data, how to disclose accounting information is an issue worth studying. Survival and development of energy industry as a key emerging industry in the country are inextricably linked with natural resources and ecological environment. Appearance of environmental accounting can greatly alleviate a series of environmental problems caused by the development of energy industry. Disclosure of environmental accounting information is a must task to prevent and manage complex environmental problems in society. However, there are still many loopholes in the development of environmental accounting information disclosure in China: the government lacks targeted standards and regulations; companies face no mandatory system requirements for environmental information disclosure, lack the initiative to disclose relevant information, and the disclosure form and content also lack standardization. With the listed energy companies in Guangdong Province as the research objects, this paper selects 19 listed companies from CNINFO that meet the conditions of listing on the Shenzhen Stock Exchange or the Shanghai Stock Exchange with A-share stocks. The four-year data of 2015-2018 is taken as a sample size to analyze the status of environmental accounting information disclosure, the content and method of disclosure, and the quality of disclosure. SPSS is taken to perform descriptive analysis, multiple linear regression analysis and other empirical analysis on the data, and the analysis results are summarized. It can be seen from empirical analysis that the four factors including the proportion of tradable shares, the proportion of independent directors, ISO environmental certification and government environmental protection subsidies, have the most significant impact on the quality of environmental accounting information disclosure of listed energy companies in Guangdong. Where, government environmental protection subsidies have the greatest impact on enterprises’ environmental accounting information disclosure. Finally, referring to the experience of environmental accounting information disclosure in other countries, this paper believes that China should improve relevant regulations and standards for environmental accounting information disclosure, while strengthening internal and external supervision of enterprises to promote the development of environmental accounting, thereby contributing to the goal of socialism with Chinese characteristics: development of circular economy, development of resource economizing type, environment-friendly low-carbon economy and society.
Research on Prediction of the Cash Usage in Banks Based on LSTM of Improved Grey Wolf Optimizer
Authors: Rong Jingfeng , Wang Di 
Published: February 2021

Abstract
In the real production and operation, it is impossible to predict the amount of cash in daily use. Therefore, the prediction model of improved LSTM neural network is proposed to cope with the problem for preparing excessive cash. Hence, the improved Grey Wolf Optimizer is most effective in searching for the optimal solution by optimizing the impact factors of Grey Wolf Optimizer. Combining the improved Grey Wolf Optimizer with LSTM neural network, the neural networking learning rate parameters are set reasonably by optimizing the algorithm to reduce the impact of inappropriate parameters on the prediction results of either over-fitting or under-fitting. What’s more, the neural network topological structure, weighing the number of LSTM network layers and the number of the neural units in each layer, determines the neural network’s description of data. If the network topology is too simple, the prediction results may not be enough to describe the real data. However, if it is too complex, it will not only waste the computing resources, but also make the prediction results over-fitting with only good description of training data. Therefore, it avoids the problem of large errors in predicting results caused by the parameters of neural network and realizes the prediction the daily cash usage. Finally, the test is completed on the data of a sub-branch network of bank with mean square error (MSE) 0.016. Compared with the traditional time series model ARAM and the unimproved LSTM, the improved LSTM predicts cash usage more accurately and efficiently.
Research on the Cave Exploration Technology of Overhead Transmission Line Tower Foundation
Authors: Wang Huaan , Xiao Bo , Zeng Liang , Chu Taoyu , Lian Xiaocong 
Published: February 2021

Abstract
Overhead transmission lines are the main form of high-voltage and UHV long-distance transmission. When the line path passes through limestone areas, mining areas and others, the possible existence of karst caves and mined-out areas under the tower base will have a greater impact on the stability of the foundation. According to previous detection experience, different underground cave forms and detection requirements need to choose suitable detection technology to obtain better detection results. This paper will explain the different detection technologies, combine multiple overhead line engineering practices, study the detection technology of underground caves, and guide the subsequent exploration of the tower foundation underground caves. Finally, carry out three-dimensional geological modeling of the exploration data, and study the application of three-dimensional data processing and display technology in the exploration of the line tower base cave. Through the above summary, analysis and experimental research, a complete set of technical solutions for the exploration of tower foundation caves on overhead transmission lines has been completed, laying the foundation for the further development of subsequent detection technologies.
Tandem Prediction Research on LSTM-DT
Authors: Wang Min , Liu Xin 
Published: February 2021

Abstract
In order to effectively avoid the problem of bus tandem, the factors and prediction model of bus tandem based on multi-source data are studied. Due to the problems of complex attributes and low prediction accuracy, this paper presents a prediction method based on long short time memory and decision tree (LSTM-DT). Based on the existing historical GPS data, a variety of input data schemes are formed by different combinations of static spatial data and dynamic spatial data. In the process of tandem prediction, the long short time memory (LSTM) model is used to predict the time series characteristics of stations with different intervals, and the attribute and weight are adjusted by combining with decision tree. Through the LSTM-DT model, it gives the comparison of the tandem prediction in different time periods and different station intervals. The experimental results show that the LSTM-DT prediction model has better generalization ability than the traditional model in the case of more input factors, and has higher prediction accuracy for the tandem at a certain interval.
Research on Zero-trust Security Protection Technology of Power IoT based on Blockchain
Authors: Chen Zhiyu , Yan Longchuan , Lü Zitong , Zhang Yanling , Guo Yonghe , Liu Wenjing , Xuan Jiaxing 
Published: February 2021

Abstract
With the access of massive terminals in the power Internet of things (IoT), the efficiency of information sharing is improved, but the grid boundary is increasingly blurred. The traditional centralized security management mode is facing a severe security situation, which is difficult to meet the high security requirements of the system operation. There are three main risks: 1) the execution authority of control information is not strictly controlled at the equipment level; 2) Data and authority are not separated, which makes the security management of data layer have hidden dangers; 3) Power supply chain is faced with the problems of data island, data trust and performance guarantee. Based on the characteristics of blockchain technology (BcT), such as non-tampering, collective maintenance and transaction non repudiation, this paper constructs a data protection architecture covering device layer, data layer and business layer, realizes high-level security protection of data flow in all links of power network, and improves the security management and control ability of the system. Through the scheme proposed in this paper, the implementation of control information is strictly managed, the use rights management of data is more standardized, and the credibility and interaction rate of business data are greatly improved.
Bisection of Hamilton Plane Graphs under Girth Constraints
Authors: Chen Tao 
Published: February 2021

AbstractA bisection of a graphGis a bipartitionV1andV2 ofV(G) such that ‖V1| – |V2‖ ≤ 1. The minimum bisection problem asks for a bisection minimizinge(V1,V2), wheree(V1,V2) is the number of edges joiningV1andV2. In this paper, we prove that every Hamilton plane graphGwith girth at least 4, |V(G)|=n, admits a bisectionV1,V2such thate(V1,V2)≤n2+1.
Research Status and Method of Aviation Sensor Performance Evaluation
Authors: Hua Hongyan , Zhang Zhen , Feng Huachen 
Published: February 2021

Abstract
Aviation sensor is an important guarantee for aircraft safety flight, ground test and flight test accuracy and reliability. In this paper, through the analysis of the problems existing in the actual working environment of aviation sensors, the necessity of evaluating the performance of aviation sensors in the actual working environment is clarified. Combined with the comparison of the research status of the performance evaluation methods of aviation sensors at home and abroad, the development direction of the performance evaluation research of aviation sensors in the actual working environment in the future is proposed.
Research on Evaluation Model of Regional Financial Science and Technology Development Based on Local Variable Weight
Authors: Xie Zongju , Chen Yongquan , Yu Bei , Wu Li , Chen Sian , Li Yutian 
Published: February 2021

Abstract
With the development of financial science and technology, how to accurately evaluate the development level of regional financial science and technology has become particularly important. This paper first establishes the evaluation index system of regional financial science and technology development, which is composed of six first-class indicators and fourteen second-class indicators, which comes from the identification of influencing factors of financial science and technology development. Then, based on the idea of local variable weight, a comprehensive evaluation method of regional financial science and technology based on local variable weight is designed, and the scientificity and feasibility of the method are verified by data. The local variable weight comprehensive evaluation method can realize the objective and flexible weight setting, and the future research can do more in-depth research on parameter optimization and vector pattern construction.
Image Denoising Based on Adaptive Sector Rotation Median Filter
Authors: Wang Yuan-Bin , Huang Hai-Long 
Published: February 2021

Abstract
By analyzing the characteristics of different median filtering algorithms, an adaptive fan rotation median filtering method based on the standard median filtering method is proposed to restore the details of the image to the maximum extent for achieving better pepper and salt noise removal effect. The proposed method can change the window size adaptively according to the pollution degree in the window, and calculate the gray difference value for different areas, judge the correlation between the center point and different areas according to the gray difference value, and take the median value of the highest correlation area as output, which can restore the details of the image while removing the noise. The experimental results show that the new algorithm has a high similarity with the original image structure and a high peak signal-to-noise ratio after filtering, and can better recover the image details. Compared with other methods, the noise removal effect and detail recovery effect are better.
Research on Optimized Storage and Analysis System of Web Log Based on Django’s MVC Framework
Authors: Tian Huan , Zhao Jing , Shen Jiaqi 
Published: February 2021

Abstract
Association rule analysis algorithm is widely used in Web log analysis, but the existing association rule analysis algorithm will significantly reduce the analysis and mining performance when the amount of Web log is relatively large. This paper proposes an improved clustering algorithm, which first clusters users with the same interests and hobbies, and then mines association rules for users in the same category, thereby reducing data dispersion. Based on Django’s MVC framework, it optimizes the storage and storage of Web logs. In the analysis part, users can configure the support and confidence of association rule mining through the front-end, and at the same time query the results of mining through Hive, and use encryption algorithms in the data transmission process to ensure data security.
Natural products based nanoformulations for cancer treatment: current evolution in Indian research
Authors: Kundu Moumita , Majumder Ranabir , Das Chandan Kanta , Mandal Mahitosh 
Published: February 2021

Abstract
The use of medicinal plants is as ancient as human civilization. The development of phytochemistry and pharmacology facilitates the identification of natural bioactive compounds and their mechanisms of action, including against cancer. The efficacy and the safety of a bioactive compound depend on its optimal delivery to the target site. Most natural bioactive compounds (phenols, flavonoids, tannins, etc) are unable to reach their target sites due to their low water solubility, less cellular absorption, and high molecular weight, leading to their failure into clinical translation. Therefore, many scientific studies are going on to overcome the drawbacks of natural products for clinical applications. Several studies in India, as well as worldwide, have proposed the development of natural products-based nanoformulations to increase their efficacy and safety profile for cancer therapy by improving the delivery of natural bioactive compounds to their target site. Therefore, we are trying to discuss the development of natural products-based nanoformulations in India to improve the efficacy and safety of natural bioactive compounds against cancer.
Up in smoke: characterizing the population exposed to flaring from unconventional oil and gas development in the contiguous US
Authors: Cushing Lara J , Chau Khang , Franklin Meredith , Johnston Jill E 
Published: February 2021

Abstract
Due to advances in unconventional extraction techniques, the rate of fossil fuel production in the United States (US) is higher than ever before. The disposal of waste gas via intentional combustion (flaring) from unconventional oil and gas (UOG) development has also been on the rise, and may expose nearby residents to toxic air pollutants, light pollution and noise. However, little data exists on the extent of flaring in the US or the number of people living near UOG flaring activity. Utilizing nightly sattelite observations of flaring from the Visible Infrared Imaging Radiometer Suite Nightfire product, 2010 Census data and a dataset of remotely sensed building footprints, we applied a dasymetric mapping approach to estimate the number of nightly flare events across all oil shale plays in the contiguous US between March 2012 and February 2020 and characterize the populations residing within 3 km, 5 km and 10 km of UOG flares in terms of age, race and ethnicity. We found that three basins accounted for over 83% of all UOG flaring activity in the contiguous US over the 8 year study period. We estimated that over half a million people in these basins reside within 5 km of a flare, and 39% of them lived near more than 100 nightly flares. Black, indigenous, and people of color were disproportionately exposed to flaring.
A new link between El Niño—Southern Oscillation and atmospheric electricity
Authors: Slyunyaev N N , Ilin N V , Mareev E A , Price C G 
Published: February 2021

Abstract
The global electric circuit (GEC) is a unique atmospheric system driven by the global distribution of thunderstorms and electrified shower clouds. The GEC unites electric fields and currents in the entire atmosphere and is characterized by the permanent production and dissipation of huge amounts of electrical energy. In this study, aimed at investigating the links between the GEC and El Niño—Southern Oscillation (ENSO), the GEC variability during 2008–2018 is simulated on the basis of reanalysis meteorological data using the Weather Research and Forecasting model and a parameterization of the ionospheric potential (IP), which is a natural measure of the GEC intensity. Modelling shows that strong El Niño and La Niña events influence the global distribution of electrified clouds over the Earth’s surface, thereby consistently affecting the shape of the diurnal variation of the GEC. Further analysis shows that anomalies in the Niño 3.4 sea surface temperature, which characterize the ENSO phase, and anomalies in the relative IP are positively correlated at 9:00–15:00 UTC and negatively correlated at 18:00–23:00 UTC. This correspondence between ENSO and the GEC is most prominent at 13:00 UTC and 21:00 UTC, and most pronounced anomalies in the relative IP around these hours are precisely associated with strong El Niño and La Niña events. In particular, during strong El Niños the relative IP is larger than usual around 13:00 UTC and smaller than usual around 21:00 UTC, whereas during strong La Niñas it behaves oppositely.
Coupled Susy, pseudo-bosons and a deformed

su(1,1)

Lie algebra
Authors: Bagarello F 
Published: February 2021

Abstract
In a recent paper a pair of operators a and b satisfying the equations a
†
a = bb
† + γ

1

and aa
† = b
†
b + δ

1

, has been considered, and their nature of ladder operators has been deduced and analyzed. Here, motivated by the spreading interest in non self-adjoint operators in quantum mechanics, we extend this situation to a set of four operators, c, d, r and s, satisfying dc = rs + γ

1

and cd = sr + δ

1

, and we show that they are also ladder operators. We show their connection with biorthogonal families of vectors and with the so-called

D

-pseudo bosons. Some examples are discussed.
Retraction: Energy storage and dissipation characteristics of red sandstone in triaxial compression tests under constant confining pressure
(IOP Conf. Ser.: Earth Environ. Sci.
570 032053)
Authors: Li Liuliu , Gong Fengqiang 
Published: February 2021

This paper has been retracted by IOP Publishing as it does not meet the terms of the IOP Proceedings Licence.
Retraction published: 23 February 2021