bluuebunny/crossref_metadata_2025_split huggingface dataset:
Behavior Trend Analysis Method of Software Runtime Environment Elements Based on ARIMA
Authors: Li Qiuying , Lu Minyan , Gu Tingyang 
Published: February 2021

Abstract
Behavior trend analysis method based on auto-regressive integrated moving average (ARIMA) is presented for software runtime environment elements (REEs). The history data of environment element behavior is collected to build behavior trend analysis model. Then, behavior trend of environment elements is predicted before the environment elements actually change. Thus, according to the analysis results, we can determine the corresponding adaptive strategy before the environment elements actually change, which provides a reference for the selection and implementation of active adaptive strategy for the self-adaptive software.
Integrated Computational Learning Algorithm for Undergraduates’ Academic Early Warning
Authors: Sheng Xiaoguang , Yang Qirui , Han Yu , Wang Ying 
Published: February 2021

Abstract
With the development of education big data, it is helpful for education managers to use machine learning method to predict students’ academic warning status, in order to ensure that students pass the course and graduate on time. Although significant progress on predicting academic warning status has been achieved in recent years, existing methods are often lack of generalization and are difficult to be applied in real scenarios. In this study, we divide students’ scores into five coarse-grained dimensions: mathematics, foreign language, humanities, major and total score, and innovatively use machine learning ensemble model to predict college students’ academic status. Experiments show that the coarse-grained dimension division is not only conducive to the generalization of the model, but also improves the accuracy of prediction academic warning status by 9.708%. Meanwhile, experimental results show that the multi machine learning model ensemble technique can effectively improve the prediction accuracy of college students’ academic warning status. With only a small number of samples, the accuracy of predicting students’ academic warning status by three months in advance reaches 97.52%, and that by the previous semester reaches above 97.93%.
Image Segmentation Based on Improved Unet
Authors: Li Xiaojin , Qian Wenhua , Xu Dan , Liu Chunyu 
Published: February 2021

Abstract
In order to help doctors diagnose and treat liver lesions and accurately segment liver images, this paper proposes an improved Unet network, which adds compression extraction modules and full-scale connection blocks, extracts input image features, and achieves accurate segmentation of liver images. The compression extraction module distributes weights to convolutional layers of different sizes, which is conducive to the extraction of image spatial information and context information. Full-scale blocks are connected by skipping,combining the higher semantic information from the decoder and corresponding the lowwer semantic information from the encoder to strengthen the ability to extract tumor edge information. This article includes 25 cases from the Lits liver dataset. The dataset is classified as the training dataset and the test dataset, and the image blocks are extracted after gray-scale normalization and input to the network to acquire the final segmentation results. The segmentation result is evaluated by F1 score. Comparing multiple sets of experiments, compared with general network structures such as Unet and AttenUnet, it shows that the network architecture proposed in the Dissertation improves the accuracy and efficiency of liver image segmentations.
An Image Processing Method: Edge-guided Directional Walk Mean Smoothing
Authors: Wang Chengxue , Qian Wenhua , Xu Dan , Li Ke , Pu Yuanyuan 
Published: February 2021

Abstract
Image denoising is an important computer vision task, it is also the basis of many other high-level vision tasks. Recently, various image denoising algorithms have proposed, such as BM3D, sparse representation and deep learning, that have achieved impressive performance in AWGN denoising or blind denoising tasks. However, when dealing with Dongba painting (A traditional painting from China’s ethnic minorities), there are problems that Dongba painting have blurred edges and faded colors, it is important to retain its original style when denoising. In this paper, we propose a novel image post-processing technique termed edge-guided directional walk mean smoothing (EDWMS), it can effectively calculate the mean of pixels along different directions then obtain denoised image with sharp edge. Experiments show that the proposed EDWMS outperforms compared algorithms in both edge sharpness and color saturation.
Management of MicroGrid in Power System in the Perspective of Communication Process of Multi-agent System
Authors: Sun Shumin , Cheng Yan , Wei Dajun , Wang Nan 
Published: February 2021

Abstract
Nowadays, The microgrid(MG) provides a way for the stable access of many kinds of distributed energy to power grid, and multi-agent system(MAS) is an important control form and general utility tool of it. MAS can separate large, complex systems into small ones that can communicate and coordinate with each other and can be easy to manage. This paper starts from the concept and characteristics of MAS and microgrid, analyzes the wide range of MAS applications in micro-grid and its necessity. Then this paper focuses on the communication process of MAS in different scenarios (power restoration and power dispatch), and clearly demonstrates the convenience and practicality of MAS in MG applications. Finally, the paper looks forward to the application of MAS in MG, and believes that the emergence of 5G technology will greatly expand the application field of MAS.
Design of Ion Implantation and Deposition Signal Generator Based on FPGA and Touch Screen
Authors: Shu Weiquan , Zheng Yanhua 
Published: February 2021

Abstract
In order to improve the operation precision of ion implantation and deposition signal generator, to fix its arithmetic error and reach 1μs as the pulse adjustment unit, use FPGA and touch screen technology to improve the performance of signal generator. On the one hand, the FPGA board can directly affect the FPGA chip through Verilog to reduce the program operation time, on the other hand, replacing with crystal oscillator with higher frequency can make the program run faster and minimize the arithmetic error. The combination of FPGA and crystal oscillator can easily realize the clock input of MHz level and output pulse with 1μs as the adjustment unit, which has already been applied to signal generator as signal output source.
Computational Research and Implementation of Prediction of Pork Price Based on Deeplearning
Authors: Chen Taotao , Chen Zhongmin , Zhou Zhixuan 
Published: February 2021

Abstract
Social network public opinion has a wide range of social influence, which is one of the main external factors causing the fluctuation of pork market. However, previous studies have considered less factors outside the market, so the prediction accuracy is not high. A model based on LSTM about public opinion is proposed in this paper. Through multi-modal information fusion, the model makes use of the complementary advantages of multi-source information to enhance the estimated performance of the model. In addition, considering the influence of different fusion methods on the estimated performance of the model, six fusion methods are tried: Add, Sub, Max, Min, Con and AVG. For the sake of verify the effect of the model, the multimodal combination estimated model and the traditional regression model are used for comparative prediction. The results show that: in the task of pork price forecasting, the indicators of Multi_BERT_LSTM (CON) combined with Con mode are better than the contrast model, and the average absolute percentage error MAPE is increased by 17% compared with the optimal traditional forecasting model, and better prediction effect is achieved.
An Improved CNN-Based Completion Method for Power Grid Middle Platform Data
Authors: Wu Peng , Xu Mingsheng , Cheng Li 
Published: February 2021

Abstract
The transmission of power data would be likely to be interrupted or interfered, which results in the middle platform data missing. Missing data reconstruction plays a key role in power data processing, based on which the quality and utilization of power data have been enhanced. In traditional power data filling methods, only a single data distribution was considered, the correlation of power data in time and space was ignored. In this paper, an improved Convolutional Neural Network (CNN) method for filling power data was presented, and a CNN structure was designed. Through the unsupervised training of CNN, this method mines the correlation of data from the dimensions of time and space, and efficiently completes the missing data through the constraints of time continuity and space continuity. The completion results show that our method can fill the missing data efficiently, furthermore, experimental evaluations validate the performs of the proposed method.
A Novel Way to Realize Standardization of UAV EMA Controller
Authors: Wu Jiufeng , Yang Tao , Lyu Jianliang 
Published: February 2021

Abstract
UAV (Unmanned Aerial Vehicle) application is becoming wider and wider, its flight safety and performance is quite important. As one of its key components, EMA (Electronic Mechanical Actuator) plays a significant role in deciding UAV performance. In order to reduce cost, shorten design period and save human power, a novel way to realize standardization of the EMA controller is presented in this paper. Based on analysis of the typical EMA with DC (Direct Current) brush motor as its power source used in UAV, the standardization controller is designed. The generalized hardware platform is built with all the functional parts required. And then the communication protocol is made for EMA controller configuration. The upper computer software and the embedded software collaborates to conduct various controller functions. The standardization controller features modularization, combination and seriation, which is better than the traditional EMA controller for it saves cost and time. Several ground tests and flight tests prove its feasibility and validity.
Direct Yaw-moment Control with a F-TDMA Scheduler for Electric Vehicle
Authors: Cao Wanke , Liu Jizhi , He Yong 
Published: February 2021

Abstract
This paper proposed a new scheduling approach for direct yaw-moment control (DYC) of distributed-driven electric vehicles (EVs) over a controller area network (CAN). The insertion of the communication network makes the system a distributed control system, which leads to clock asynchrony among control system components and asynchrony-induced delays. This paper employs a flexible Time Division Multiple Access (F-TDMA) scheduler to manage the communication flows. And the stability of the close-loop system is guaranteed by using the pole placement method. The results of the simulation illustrate that the proposed method can effectively deal with the clock asynchrony problems and guarantee the stability of the system.
The future of invasive terrestrial vertebrates in Europe under climate and land-use change
Authors: Polaina Ester , Soultan Alaaeldin , Pärt Tomas , Recio Mariano R 
Published: February 2021

Abstract
Predicting suitable locations for invasive alien terrestrial vertebrates (IATV) under different scenarios of global change is essential for local and transboundary management aimed to prevent the spread of invasions. Using a spatial modelling approach adapted to invasive species, we identify range-shifts in suitable areas for 15 of the most harmful IATV in Europe, considering future climate and land-use changes. We predict range contractions for seven of these IATV, expansion for four, and inconclusive outputs for the rest. For most Europe, future aggregated distributions show stable or decreasing trends in total IATV richness. Still, specific regions will increase their suitability for additional IATVs, including some protected and last-of-the-wild areas. Our results are informative for early decision-making and long-term strategies to prevent negative effects of IATV. Our approach is based on publicly available data, so predictions can be revised as new data becomes available.
Variation in aboveground biomass in forests and woodlands in Tanzania along gradients in environmental conditions and human use
Authors: Requena Suarez Daniela , Rozendaal Danaë M A , De Sy Veronique , Gibbs David A , Harris Nancy L , Sexton Joseph O , Feng Min , Channan Saurabh , Zahabu Eliakimu , Silayo Dos Santos , Pekkarinen Anssi , Martius Christopher , Herold Martin 
Published: February 2021

Abstract
Disturbed African tropical forests and woodlands have the potential to contribute to climate change mitigation. Therefore, there is a need to understand how carbon stocks of disturbed and recovering tropical forests are determined by environmental conditions and human use. In this case study, we explore how gradients in environmental conditions and human use determine aboveground biomass (AGB) in 1958 national forest inventory (NFI) plots located in forests and woodlands in mainland Tanzania. Plots were divided into recovering forests (areas recovering from deforestation for <25years) and established forests (areas consistently defined as forests for ⩾25 years). This division, as well as the detection of year of forest establishment, was obtained through the use of dense satellite time series of forest cover probability. In decreasing order of importance, AGB in recovering forests unexpectedly decreased with water availability, increased with surrounding tree cover and time since establishment, and decreased with elevation, distance to roads, and soil phosphorus content. AGB in established forests unexpectedly decreased with water availability, increased with surrounding tree cover, and soil nitrogen content, and decreased with elevation. AGB in recovering forests increased by 0.4 Mg ha−1yr−1 during the first 20 years following establishment. Our results can serve as the basis of carbon sink estimates in African recovering tropical forests and woodlands, and aid in forest landscape restoration planning.
Experimentations on the Transmit Power of a Universal Software Radio Peripheral Using GNU Radio Framework and a Handheld RF Explorer
Authors: Ajala Sunday , Adetiba Emmanuel , Akanle Mathew B. , Obiyemi Obiseye O. , Thakur Surendra , Abolarinwa Joshua 
Published: February 2021

AbstractThe Universal Software Radio Peripheral (USRP) and GNU Radio framework have a wide range of applications and are actively in use for teaching, research and for practical deployment of various trending wireless technologies. In this work, we explore the relationships between the power outputs of a USRP B200 device and its operating frequencies and USRP Hardware Driver (UHD) gains. These relationships are not precisely specified on the device datasheet, thereby hindering prompt design and experimentation decisions among Software Defined and Cognitive Radio (SDCR) researchers. A general purpose handheld spectrum analyser was engaged for recording the power outputs from the USRP B200 device within an experimental testbed that was setup for this study. The B200 device was driven by a GNU radio flowgraph designed for both wired and wireless modes and running on a general purpose host computer in the testbed. The results indicate that the output power decreases while the centre frequency increases, whereas the output power increases as the UHD gain increases from 65 dB until the device reaches the saturation point at 89.9 dB. These results provide a handy toolkit for SDCR researchers and practitioners.
Moringa oleifera as natural coagulant in water treatment and production of antifungal soap
Authors: Ojewumi M.E. , Alagbe E.E. , Abinusawa A.P. , John A.N. , Taiwo S.O. , Bolade O.P. 
Published: February 2021

Abstract
The use of Moringa oleifera seed in water purification has reduced the use of chemical-based coagulants which is detrimental to both human and livestock. This project aimed at testing the microbial properties of M. oleifera seed oil extract on some selected pathogens (Bacterial and fungi). The oil was extracted using Soxhlet apparatus with ethanol as solvents. Gas-chromatography-mass spectrometry (GCMS) analyses were carried out for the identification of active components in the oil extract. The zone of inhibition test carried out showed that this particular plant seed oil extract has antifungal property with Candida albicans and Rizopus stolonifera with highest zone of inhibition. The raffinate was used for water purification and the oil for the production of an antifungal soap.
Knowledge management conceptual framework in Nigeria tertiary institutions
Authors: Ogunbanwo A. S. , Okesola J. O , Sheryl Buckley 
Published: February 2021

Abstract
Knowledge is an essential resource to academic institutions, but its management especially in the developing countries has always been very difficult. A KM model is developed in this study to manageinstitutional resources and makesame available at the right time towards enhancing academic performance. Quantitative research approach was adopted over content analysis to evaluate data obtained through the review of the past related works on KM models. The study framework is based on some established models and is integrated into the university process activities. It links institutional knowledge to capabilities and KM system to ensure best practices and promote academic performance.
Antimicrobial and Antioxidant properties of Petiveria alliacea
Authors: Olomieja A. O. , Olanrewaju I. O. , Ayo-Ajayi J. I. , Jolayemi G. E. , Daniel U. O. , Mordi R. C. 
Published: February 2021

Abstract
The leaf, stem and root decoctions of Petiveria alliacea (referred to as Anamu or Apacin (Guatemala), Guinea–Hen weed (English), Awogba or Ojusaju (Yoruba), kanunfari (Hausa) and Akwa-ose (Igbo) have been used as a diuretic, antispasmodic, sedative, analgesic and anti-inflammatory agents in traditional medicine. Cold maceration and Soxhlet extraction techniques were used to obtain oils from the leaf, stem and root of P. alliacea. The oils were analysed for their antimicrobial properties using brothmicrodilution technique while their antioxidant properties were determined by the 2,2-diphenyl-1-picrylhydrazyl (DPPH) tests. The antimicrobial tests on the following micro-organisms Salmonella typhi, Staphylococcus aureus, Bacillus subtilis, Escherichia coli,as well as the anti-fungi test on Rhizopus sp., and Aspergillus niger, showed that the hexane and ethanol cold leaf extracts were most effective in limiting the growth of Salmonella typhi with minimum inhibitory concentration (MIC)value of 3.125 µg/mL and minimum bactericidal concentration (MBC)value of 6.25 µg/mL. The ethanol leaf extracts using Soxhlet extraction technique were most effective at MIC value of 3.125 µg/mL on Staphylococcus aureus, Escherichia coli. Likewise, the hexane and ethanolcold leaf extracts were most effective in limiting the growth of Rhizopus sp. and Aspergillus niger with MIC value of 3.125 µg/mL and minimum fungicidal concentration (MFC) value of 6.25 µg/mL.
Nanoinformatics: Opportunities and challenges in the development and delivery of healthcare products in developing countries
Authors: Nwankwo Wilson , Ukhurebor Kingsley Eghonghon 
Published: February 2021

AbstractThe world is witnessing sustained effects of information technologies across all works of life. Though some of these influences are markedly negative and detrimental to the socio-economic prospects and progress of the society, the positive effects are often impressive especially where they are integrated for the betterment and greater good of the larger society. One of the fastest growing technologies is nanotechnology. Nanotechnology has numerous prospects and applicability across various sectors ranging from electronics, telecommunications, agriculture and food production, biotechnology and genetics, oil prospecting and production, remote sensing, drug production, to name a few. This novel technology readily finds usefulness and several researches are ongoing, geared at developing new tools and techniques that would improve its acceptance across the concerned domains. It is this quest that has culminated into the ongoing effort in nanoinformatics, an interdisciplinary study and a subdomain of informatics simply regarded as the conscientious application of informatics tools and technologies to the analysis, design and development of systems on the broad spectrum of nanomaterials including their physicochemical and environmental characteristics as well as their interactions, interrelationships, and applications within a given domain. This paper presents a review of some opportunities for individuals, experts, and the society especially in the production of cost-effective nanotechnology-based healthcare products. Notwithstanding the aforesaid opportunities which could be harnessed and sustained in any developing country like Nigeria, this paper identifies and buttresses core challenges that could confront the adoption of good nanoinformatics methodologies. This paper concludes that a developing country (Nigeria in perspective) could benefit from nanoinformatics if there are stronger ties among the key stakeholders involved in healthcare products delivery in the society.
Performance evaluation of features for gene essentiality prediction
Authors: Aromolaran Olufemi , Oyelade Jelili , Adebiyi Ezekiel 
Published: February 2021

Abstract
Essential genes are subset of genes required by an organism for growth and sustenance of life and as well responsible for phenotypic changes when their activities are altered. They have been utilized as drug targets, disease control agent, etc. Essential genes have been widely identified especially in microorganisms, due to the extensive experimental studies on some of them such as Escherichia coli and Saccharomyces cerevisiae. Experimental approach has been a reliable method to identify essential genes. However, it is complex, costly, labour and time intensive. Therefore, computational approach has been developed to complement the experimental approach in order to minimize resources required for essentiality identification experiments. Machine learning approaches have been widely used to predict essential genes in model organisms using different categories of features with varying degrees of accuracy and performance. However, previous studies have not established the most important categories of features that provide the distinguishing power in machine learning essentiality predictions. Therefore, this study evaluates the discriminating strength of major categories of features used in essential gene prediction task as well as the factors responsible for effective computational prediction. Four categories of features were considered and k- fold cross-validation machine learning technique was used to build the classification model. Our results show that ontology features with an AUROC score of 0.936 has the most discriminating power to classify essential and non-essential genes. This studyconcludes that more ontology related features will further improve the performance of machine learning approach and also sensitivity, precision and AUPRC are realistic measures of performance in essentiality prediction.