Miracle-dz/UniFAR-FTUs huggingface dataset:
TF-IDF Keyword Extraction Method Combining Context and Semantic Classification

Keyword extraction plays the same role as the cornerstone in the field of natural language processing. Text classification, information retrieval, abstract generation and text clustering are all based on keyword extraction. This article takes the research of keyword extraction model as the subject. First, it analyzes the principle and limitations of the traditional keyword extraction model TF-IDF when extracting keywords. Secondly, it focuses on the problem of ignoring context and word polysemy in the keyword extraction model. To improve, introduce the concept of context vector, construct a chain-extensible structure for polysemous words, and propose a new keyword extraction method of TF-IDF that combines context and semantic classification. The specific research contents are as follows: TF-IDF extracts keywords based on multiple texts. The target keywords are words that appear frequently in the current text, but appear in other articles that are significantly lower in frequency than the current article. This method takes into account the characteristic content of the article and makes the article distinct. However, this method ignores the influence of the context of the article and the problem of word polysemy. Obviously, the expression of the thought of the article will not only be affected by the context of the words, but also by the semantics of the words. In order to solve the above problems, this paper proposes a TF-IDF keyword extraction method that combines context and semantic classification. First, the text constructs a linked list expandable structure to solve the problem of polysemy; then, the influence of context is introduced. Before inserting a new word into the extended structure, the semantic similarity is calculated through the context vector. Finally, iterate over the entire expandable structure, sum the word frequency with semantic similarity under each semantic, and then sort the results to obtain TopN words as the text keywords of the corresponding article. In this paper, the Sogou news data set is used as the input data of the model, and the accuracy of the experimental results is significantly better than that of TF-IDF.
Managing Amazonian Wildlife: Biological Correlates of Game Choice by Detribalized Hunters

Wildlife management in tropical areas inhabited by rural people requires an understanding of the interactions between game choice of hunters and the biology of game species. This paper presents an analysis that combines information on game use and game biology in an attempt to understand better the game hunting system and its effect on mammalian populations. Game choice by non-tribal inhabitants was studied in the northeastern Peruvian Amazon within the Reserva Comunal Tamshiyacu-Tahuayo (RCTT). The correlation between game preferences and actual harvests was calculated using lvlev's index of selectivity. Hunters preferred large-bodied mammals and mammals with high economic value. Actual harvests did not reflect preferences of hunters, but were correlated with reproductive productivity of game species, measured as the species rmax. Impact of hunting on the biomass of game was not correlated with preferences of hunters or actual harvests. Both game choice by hunters and susceptibility of species to hunting depended mainly on the reproductive productivities of game species. Thus, game management in the RCTT must consider the dominating effects of reproductive biology and consider variance in rmax when determining harvests.
Self-Perception and Relative Increases in Substance Use Problems in Early Adulthood

This study assessed self-perception as a long-term predictor of relative changes in problems related to alcohol and marijuana use in early adulthood. Self-report questionnaires were completed by a community sample of 124 individuals in the Southeastern United States who were followed longitudinally from age 19 to 27. More problems due to substance use at age 27 were predicted by participants’ negative perceptions of their social acceptance, romantic appeal, and self-worth. Predictions remained after accounting for potential confounds including gender, income, and baseline substance use problems at age 19. Social avoidance and distress in new situations at age 19 mediated the relationship between self-perception and relative changes in substance use problems, such that increases in substance use problems from age 19 to 27 were potentially explainable by the linkage of negative self-perceptions to social avoidance and distress in new situations.
Effect of shRNA against NRAGE gene on proliferation and apoptosis of SGC7901 cells

Aim:To study the effect of shRNA against NRAGE gene on proliferation and apoptosis of SGC7901 cells.Methods:The pSuper-EGFP-NRG plasmid was ttransfected into SGC7901 cells and positive clones were screened by G418. The expression of NRAGE mRNA were demonstrated by RT-PCR and proliferation and apoptosis of the transfected cells were probed by FCM and MTT.Results: Compared with SGC7901 cells,the NRAGE gene expression in the SGC7901 cells transfected pSuper-EGFP-NRG was silenced. The ratio of the cells in G0~G1and G2 were lower, but that in S phase was higher.Conclusion: NRAGE gene was closely related to cell proliferation and apoptosis.
Stock Price Prediction Based on Machine Learning Approaches

Research of quantitate investment on stock price prediction is effective to help investors increase profits. Recently, technologies of machine learning have been well applied to explore the issue of stock trading. In this paper, Logistic Regression and Support Vector Machines (SVM) were adopted to solve the problem of predicting the trend of stock movements. The experiment showed that these two models could be effectively used in the stock market of China. Returns based on strategies we constructed were significantly better than the HS300 index. In different models, we analyzed the relationship between stock returns and different models. It found that the SVM model results are optimal. The annual return of the strategy based on SVM reached 17.13% and the maximum Drawdown was 0.32. In the future, we will not only focus on the stock market, but also plan to apply this strategy to other investment fields, such as trading of digital currency. We will also use other algorithms for research and comparison, such as andom forests, XGBoost.
A pattern-based approach for an early detection of popular Twitter accounts

Social networks (SN) are omnipresent in our lives today. Not all users have the same behaviour on these networks. If some have a low activity, rarely posting messages and following few users, some others at the other extreme have a significant activity, with many followers and regularly posts. The important role of these popular SN users makes them the target of many applications for example for content monitoring or advertising. It is therefore relevant to be able to predict as soon as possible which SN users will become popular. In this work, we propose a technique for early detection of such users based on the identification of characteristic patterns. We present an index, H2M, which allows a scaling up of our approach to large social networks. We also describe our first experiments that confirm the validity of our approach.
High-resolution brain fMRI reconstruction via double cooperative network learning

For the problem of resolution enhancement of function Magnetic Resonance Imaging(fMRI), traditional methods based on prior knowledge are difficult to choose appropriate regularization terms to solve it effectively. In this work, we proposed a double cooperative network without regularization items for super-resolution(SR) reconstruction. The experimental results show that the proposed method could effectively enhance the resolution of functional magnetic resonance images and rebuild fine texture. Also we demonstrate the proposed method is robustness on various tasks fMRI.
Environmental Impact Evaluation of Flexible Plastic Packaging using Life Cycle Assessment Approach

The increasing plastic packaging consumption causes impacts to the environment. The environmental impacts that produced are greenhouse gases, acidification, eutrophication, and other environmental impacts. In this study, an environmental impact assessment carried out to evaluate the plastic packaging life cycle using the life cycle assessment (LCA) method. The purpose of this study is to analyze the environmental impact obtained in the manufacturing process of flexible plastic packaging composed of PET, LLDPE, and Aluminum. The study conducted with a functional unit of 1 m2 of flexible plastic packaging produced. The results show that the highest impact category that produced during the manufacturing process is non-renewable energy, global warming, and respiratory inorganics.
Exploring the Instructional Efficiency of Representation and Engagement in Online Learning Materials

Using two 2 x 3 in-situ experiments in an introductory programming course, we study the effect of representation and engagement on the instructional efficiency of learning materials. In the experiments, we controlled for used representation and the level of engagement and accounted for prior experience and prior cognitive effort. We observe that analogical representations with little engagement are more beneficial for those already familiar with the topic. No significant effect from engagement or prior experience was observed when students studied using traditional representations. Low cognitive effort before studying was related to studying being less cognitively demanding, regardless of the condition. No single way of presenting information seems to work better or worse universally for all participants.
Network Topology Change Detection Based on Statistical Process Control

Network topology is one of the most important parts in network security situation awareness tasks. Considering that the topology may change due to network intrusion, adjustment of routing policies, etc., the traditional static topology analysis methods cannot capture the dynamic change of network topology with time, which leads to the problem of weak early warning ability. To find out the small trend changes of the network topology over time, a network topology change detection method is proposed based on statistical process control: 1) to simplify the continuous dynamics of network topology, the observation network is regarded as a sampling sequence of the topological networks that dynamically change with time, a longitudinal topological network is constructed; 2) to quantify the differences of network structure from multiple perspectives, network structure parameters of each period are selected and measured; 3) to find out the trendy structural change in the longitudinal topological network, the cumulative sum method in industrial control is introduced to evaluate the change of topological parameters and further track to the starting time of change. Extensive experiments are performed on the simulation data, showing that compared with simple parameter statistics method, the method proposed can be sensitive to network changes and trace back to the beginning of the trend change.
Lipid transport mechanism in the fish. III. Plasma transport form and metabolism of dietary fatty alcohol and wax ester in carp.

To study the mechanism of non-glyceride lipids transport and metabolism in fish, the fate of dietary fatty alcohols and wax esters were investigated by force-feeding [1-14C] oleyl alcohol and [1-4C] oleyl pahmitate to carp. When [1-14C] oleyl alcohol was fed, the total radioactivities in plasma lipoproteins increased gradually, the maximum being around 28h. When [1-14C] oleyl palmitate was given, it was hydrolyzed largely in the intestinal lumen and the total radioactivities in plasma lipoproteins increased with the maximum being around 12h. In both cases, almost all the radioactivities were found in HDL fraction as the forms of mainly triglyceride (TG) and phosphatidylcholine (PC). After 72h of feeding, the radioactivities from [1-14C] oleyl alcohol and [1-14C] oleyl palmitate were found mostly in muscle, intestine, hepatopancreas and gill as mainly in the forms of TG or PC. Tn either cases, a little amount of radioactivity was detected in the class of wax ester or fatty alcohol in each tissue. The dietary fatty alcohol and alcohol moiety of wax ester were largely oxidized to the corresponding acids in the intestinal tissue, which then behaved as in the case of dietary acid, and some were reesterified to form wax esters. Then, most of them were transported as TG and PC with HDL in plasma. Finally, it is suggested that the fatty alcohol in various tissues of carp, which seems to be a substrate of acyl CoA: alcohol acyltransferase, is one of the most important key precursor to form wax esters. Moreover the circulating lecithin: alcohol acyltransferase (LAAT) in plasma also seems to relate to wax ester synthesis.
Screening of bacteriocin producing lactic acid bacteria and the analysis of bacteriocin related genes

In order to obtain a bacteriocin producing strain,lactic acid bacteria were separated and screened with dissolve calcium circle and oxford cup double-plate method from premium fermentation meat.Morphological,physiological biochemistry method,16SrDNA and specific gene PCR amplification were used to identify the strain to species,and the bacteriocin related gene was analyzed to determine bacteriocin types.The results showed that,after eliminating organic acid,hydrogen peroxide and other interference factors,L-ZS9still had antibacterial activity among the 92strains and its antibacterial substances were sensitive to protease(acid protease,protease K,trypsin,pepsin,neutral protease),while insensitive to alpha amylase.Therefore,L-ZS9 was recognized as bacteriocin producing bacteria and identified as Lactobacillus paraplantarum.Further bacteriocin related gene PCR amplification showed that L-ZS9contained Ⅱb plantaricins EF,JK,Ⅱc plantaricin A and plantaricin N encoding gene.The mature peptide sequences of plnF,plnK were identical with Lactobacillus plantarum C11(X94434)and only one amino acid mutation occurred in plnE,plnJ mature peptide sequences.Therefore,the Lactobacillus paraplantarumL-ZS9was a nature multibacteriocinogenic strain.
Data science for healthcare predictive analytics

Big data are everywhere nowadays. Many businesses possess big data for their success because big data are very useful and are considered as new oil. For instance, big data are very important in predicting the trends on what will happen in the future. Many researchers have generated or gathered data to further enhance their research and to apply them to numerous real-life applications. Examples of big data include healthcare patient data. To improve the detection of illnesses and diseases, researchers have gathered healthcare patient data, examined the diagnosis on healthcare patient data (e.g., cells, blood count, antibodies count), and compared with previous data to determine if a specific illness or disease exist. Having an automatic predictive method for healthcare and disease analytics would be desirable. In this paper, we focus on healthcare mining, which aims to computationally discover knowledge from healthcare data. In particular, we present a data science framework with two predictive analytic algorithms for accurate prediction on the trends of cancer cases. The algorithms predict cancerous cells based on the information of the cell data from some data samples. Evaluation results on several real-life datasets related to the breast cancer demosntrate the effectiveness of our data science framework and predictive algorithms in healthcare data analytics.
Soil greenhouse gas emission in winter wheat/summer maize rotation ecosystem as affected by nitrogen fertilization in the Piedmont Plain of Mount Taihang,China

The effect of soil nutrient augmentation via nitrogen deposition or fertilization on the processes of storing,releasing and re-absorbing of methane(CH4) carbon dioxide(CO2) and nitrous oxide(N2O) greenhouse gases(GHG) in soil ecosystems are poorly understood.This study described an original field experiment for measuring soil GHG flux at nitrogen application rates of N0 [0 kg(N)-hm-2],N200 [200 kg(N)-hm-2],N400 [400 kg(N)-hm-2]and N600 [600 kg(N)-hm-2] in winter wheat/summer maize double cropping system in the Piedmont Plains of Mount Taihang.CH4,CO2 and N2O emissions from soils under the winter wheat/summer maize rotation system were measured from July 2008 through June 2010 using the static transparent chamber and gas chromatogra-phy technique.The results showed that soils under winter wheat/summer maize ecosystems served as CH4 sink,and CO2 and N2O sources.With increasing nitrogen application,CH4 absorption rate decreased and CO2 and N2O flux rates increased.In winter wheat season,CH4 absorption rate under N0 treatment was significantly higher than under other nitrogen treatments.Both CO2 and N2O emission flux rates under N600 treatment were significantly higher(P0.05) than under N0 treatment.Nitrogen fertilization and irrigation sharply induced CO2 and N2O peak flux emissions and decreased CH4 peak absorption.Rising soil temperature,precipita-tion or alternating wet/dry weather increased soil N2O and CO2 emission flux rates.Negative N2O flux rates were observed under low temperature and high evaporation conditions in N0 application treatments in winter.CH4 emission accumulation for 2008-2009 un-der N0,N200,N400 and N600 treatments was -1.42,-0.75,-0.82 and -0.92 kg-hm-2-a-1,respectively.For 2009-2010,it was -2.60,-1.47,-1.35 and -1.76 kg-hm-2-a-1,respectively.CO2 emission accumulation for 2008-2009 under N0,N200,N400 and N600 treatments was 15 598,19 346,21 456 and 29 013 kg-hm-2-a-1,respectively.For 2009-2010,it was 10 318,11 474,13 984 and 20 639 kg-hm-2-a-1,respectively.Then N2O emission accumulation for 2008-2009 under N0,N200,N400 and N600 treatments was 1.05,2.16,5.27 and 6.98 kg-hm-2-a-1,respectively.Also for 2009-2010,it was 1.49,2.31,4.42 and 5.81 kg-hm-2-a-1,respectively.
Recommendations Concerning the Selection of Artificial Intelligence Methods for Increasing of Cyber-Security

In the course of the implementation of a project funded by the Research Fund under the Ministry of Education and Science, on the base of multi-factor analysis of Cyber-security Threats and the adoption of military theory into Cyber-security procedures, the authors have formulated a new classification of the stages of the Cyber Defense and the classes of tasks that can be solved using Artificial Intelligence methods. Based on theoretical models and experiments with specific prototypes, the authors make recommendations for the selection of Artificial Intelligence methods appropriate for the various phases of the Cyber Defense
Rice Plant Nitrogen Concentration Monitoring by Unmanned Aerial Vehicle-Based Imagery

Application of Unmanned aerial vehicles (UAVs) for remote sensing has given many advantages. The measurement of reflectance spectra in plant canopies can be the basis in detecting nitrogen content. Chlorophyll concentrations at different levels resulted in the reflectance spectra measured in the canopy, also being different. This study aimed to develop the method of Nitrogen monitoring using UAV imagery combined with Leaf Chart Color (LCC) to estimate plant nitrogen concentration in rice. To develop the Normalized Difference Vegetation Index (NDVI) indices, the Leaf Chart Color was used to measure the greenness of the leaf crop. A high-resolution digital camera modified with NDVI-7 filter mounted to UAV that sensitive to infrared wavelength. The flight altitude was 3, 10, and 15 m. To measure the strength of a linear association between LCC measurements and NDVI estimations, we used a Pearson one-tailed correlation. The Pearson coefficient indicates that the correlation is a strong negative correlation (-0.822) at 3 m altitude, weak correlation (-0.4562) at 10 m altitude, and no correlation at 15 m altitude.
Patent prior art search using deep learning language model

A patent is one of the essential indicators of new technologies and business processes, which becomes the main driving force of the companies and even the national competitiveness as well, that has recently been submitted and exploited in a large scale of quantities of information sources. Since the number of patent processing personnel, however, can hardly keep up with the increasing number of patents, and thus may have been worried about from deteriorating the quality of examinations. In this regard, the advancement of deep learning for the language processing capabilities has been developed significantly so that the prior art search by the deep learning models also can be accomplished for the labor-intensive and expensive patent document search tasks. The prior art search requires differentiation tasks, usually with the sheer volume of relevant documents; thus, the recall is much more important than the precision, which is the primary difference from the conventional search engines. This paper addressed a method to effectively handle the patent documents using BERT, one of the major deep learning-based language models. We proved through experiments that our model had outperformed the conventional approaches and the combinations of the key components with the recall value of up to '94.29%' from the real patent dataset.
Extracellular Vesicles Reflect the Efficacy of Wheatgrass Juice Supplement in Colon Cancer Patients During Adjuvant Chemotherapy

Introduction Colorectal cancer (CC) is the third most common type of cancer, accounting for 10% of all cancer cases. Adjuvant chemotherapy is recommended in stages II–III CC. Wheatgrass juice (WGJ) from wheat seeds has high nutritional values, may induce synergistic benefits to chemotherapy and may attenuate chemotherapy-related side effects. Extracellular vesicles (EVs) are subcellular membrane blebs. EVs include exosomes (generated in the endosome, in size <150 nm) and microvesicles (shed from the plasma cell membrane) provide information on their parental cells and play a role in intercellular communication. We aimed to elucidate the effects of chemotherapy administration with supportive treatment of WGJ on CC patients’ EVs characteristics. Methods EVs were isolated from the blood samples of 15 healthy controls (HCs) and 50 CC patients post-surgery, treated by chemotherapy, with or without additional daily WGJ. Blood samples were taken before, during, and at the end of chemotherapy. EVs were characterized by size, concentration, membrane antigens and cytokine content using nanoparticle-tracking analysis, western blot, flow cytometry, and protein array methods. Results EVs were found to be similar by size and concentration with reduced levels of exosome markers (CD81) on samples at the end of combined treatment (chemotherapy and WGJ). Higher levels of endothelial EVs, which may indicate impairment of the vascular endothelial cells during treatment, were found in CC patients treated by chemotherapy only compared to those with chemotherapy and daily WGJ. Also, EVs thrombogenicity was lower in patients added WGJ compared to patients who had only chemotherapy (levels of tissue factor p = 0.029 and endothelial protein C receptor p = 0.005). Following treatments, levels of vascular endothelial growth factor receptors (VEGFR-1) and the majority of growth-factors/pro-inflammatory cytokines were higher in EVs of patients treated by chemotherapy only than in EVs obtained from patients with the combined treatment. Conclusion Daily consumption of WGJ during chemotherapy may reduce vascular damage and chemotherapy-related thrombogenicity, growth factors and cytokines, as reflected by the characteristics of patient’s EVs.