bluuebunny/crossref_metadata_2025 huggingface dataset:
Antioxidant vitamins and their effect on immune system
Authors: Khadim R M , Al-Fartusie F S
Published: April 2021

Antioxidant vitamins are organic molecules that have vital roles in the human body. They are essential nutrients and mainly found in the food and are required in small quantities for the maintenance of the human body. Antioxidant vitamins play a major role in reducing and controlling oxidative stress and infectious diseases. This review aims to broaden the knowledge of the antioxidant vitamins (C, E, and A) and their effect on the immune system. These vitamins protect the immune system and enhance resistance against infectious microbes such as bacteria, viruses, and parasites. It is highly recommended to include antioxidants in the daily diet, as their lack can weaken the immune system and ultimately its ability in defending the body against diseases. Although vitamins are required in specific doses to maintain functional immune system, they mostly used as supplements. Vitamin C supplementation, for example, has been shown to reduce the duration and severity of upper respiratory infections, including colds. Moreover, it has been suggested that there is a close relationship between the deficiency of vitamins and infectious diseases such as tuberculosis, AIDS and CoV-2 SARS, as well as infectious diseases spread through the respiratory and digestive systems.
Laser treatment of chitosan/biopolymer materials of different molecular weight coated with ZnO for antimicrobial surface development
Authors: Angelova L , Daskalova A , Kalinkevich A , Kalinkevich O , Karpenko A , Petrov L , Trifonov A , Buchvarov I
Published: April 2021

Creating novel temporary “platforms” for effective integration of engineered tissues has been extensively researched and innovated in order to obtain scaffolds fulfilling all requirements for seeding different types of cell cultures and improving the cells’ adhesion, proliferation and differentiation. A perfect scaffold should mimic the native porous environment of the cells – interconnected pores with well-defined sizes providing the normal functioning of the cells, as they can significantly influence not only the cells behavior, but also the integration of the implants with the surrounding “host” tissues. The biopolymer-based scaffolds still need additional modification in order to impart complete biological cellular functioning and communication. In this study, a femtosecond laser-based method for surface modification was applied to improving the morphological properties of chitosan-based ZnO magnetron sputtered blends and chitosan matrices of different molecular weight, thus achieving different levels of morphological structures for creation of enhanced antibacterial cell surface environment. The microstructured scaffolds were investigated by SEM, EDX and FTIR. Wettability measurements were performed in order to determine the hydrophilicity of the treated surfaces. Changes in the water contact angle (WCA) values were monitored in the range from 120° to 70° by introducing diverse laser patterning conditions. Modifying the topography/morphology of the sputtered biopolymer blends can essentially improve their bioactivity properties; moreover, creating hierarchical porosity will affect its antibacterial features, which will enable their successful applications in tissue engineering.
Nanostructures based on ZnO and TiO2 oxides produced by PLD in open air
Authors: Dikovska A , Gancheva M , Nikov Ru , Avdeev G , Iordanova R , Nedyalkov N
Published: April 2021

We report on the fabrication of highly porous metal-oxide nanostructures by pulsed laser deposition performed in air at atmospheric pressure. The depositions were performed by ablating synthesized ZnO and a mixed target of ZnO and TiO2 as initial materials. The attention was focused on the structure, morphology, optical and photocatalytic properties of the as-deposited nanostructures of the ZnO-TiO2 system. The technology applied leads to the formation of nanostructures composed of nanoparticles. The as-prepared samples possess a high transmittance in the visible and near IR region with an optical band gap in the range of 3.1 - 3.2 eV. It was demonstrated that the as-deposited ZnO and ZnO-TiO2 nanostructures degrade Rhodamine B up to 43 - 44% after 300-min UV irradiation.
Modification of thin carbon films by UVc light
Authors: Milenov T , Dikovska A , Avramova I , Karaivanova D , Avdeev G , Terziyska P , Dimov D , Karashanova D , Georgieva B , Kolev S , Valcheva E
Published: April 2021

We modified single- and few-layer nano-sized graphene films deposited by pulsed laser deposition (PLD) on ∼300-nm-thick SiO2/Si substrates. The samples were irradiated by a UVC lamp (wavelength of 254 nm) for 15 and 30 minutes in air atmosphere. The light was directed almost parallel (about 2 - 2.5 arcdeg declination angle) to the films’ surface. The influence of the modification was evaluated by ellipsometry and X-ray photoelectron and Raman spectroscopies. The results showed that the irradiation caused an increase of the C-O/C-OH bond and a decrease or the complete disappearance of the -C=O/-COOH bond. The structural quality of the graphene films was improved moderately after a 15-min UVC treatment.
Pulsed laser co-deposition in air: a way of fabricating composite nanostructures
Authors: Nedyalkov N , Nikov Ru , Nikov Ro , Dikovska A
Published: April 2021

This work presents results of applying a method based on pulses laser co-deposition of a noble metal and a magnetic iron oxide for fabrication of ordered composite nanostructures. The experimental scheme includes two nanosecond laser beams with a wavelength of 1064 nm that ablate simultaneously two targets – Au and Fe2O3. The process is implemented in air – a condition that leads to a fast condensation of the ablated material into nanoparticles. By choosing different positioning of the ablation plumes and the substrate, composites of iron oxide nanoparticles decorated by noble metal ones can be fabricated. An external magnetic field applied close to the substrate orients and aligns the nanoparticles into chains. The effects of the processing parameters that define the geometry of the experiment are studied and presented. The method can be optimized for fabrication of complex composite nanostructures with applications in sensorics, optics and magneto-optics.
Integrated interference wedged structures as a basis for creating compact light beam splitters with improved parameters
Authors: Deneva M , Nenchev M
Published: April 2021

Based on the idea of combining in one plate and in a puzzle-type of properly designed parts of planar sheet-like interference wedged structures (IWS), including the composed interference wedged structure (CIWS) developed by us, we show the possibility of realizing new integrated light beam splitters (for laser beams) that have useful competitive properties. Using our theoretical approach for analysis of IWS, simulations and test experiments, we show that such optimized splitters ensure a smooth splitting in an increased linear variation range of the transmission T (from ∼ 80 - 95% to 5%) and reflection (R = 1 - T), in combination with a large working range (linear splitting by sliding to more than ∼15 - 20 mm). The low slope of the transmission variation permits one to split beams of diameter 3 - 5 mm; focusing the beam increases this range to more than ∼15 mm. The control is performed by sliding the splitter in its plane (dimensions ∼ 5×2×0.1 cm3) thus conserving the direction of the formed beams. No specific polarization is required and the use is possible of high-power laser beams. A suitable practical realization is the use of a mask technology for sequential deposition of necessary layers in integrated circuits.
Dual-beam electromagnetically-induced absorption resonance in a 87Rb cell with antirelaxation coating
Authors: Taskova E , Alipieva E
Published: April 2021

Destruction of the laser-induced coherence in the ground state of alkali atoms manifests itself as an ultra-narrow resonance in the atomic spectrum. Depending on the geometry of irradiation and observation, the coherent spectroscopy studies CPT (coherent population trapping), EIT (electromagnetically-induced transparency) or EIA (electromagnetically-induced absorption). In the present work, we investigated EIA on the D1
87Rb line by applying a counter-propagating dual-beam scheme. The main advantage of this scheme is the high resonance contrast – an important parameter for many applications. In our previous work performed in a paraffin-coated cell we observed that, unlike the resonance in buffer gas cell detected in the same experimental scheme, the EIA signal has a complex form, because it is formed by two atomic sub-ensembles in the vapor cell with different relaxation rates determined by the laser excitation conditions. We focused on the narrow component, since it has a higher amplitude-width ratio, making it preferable for applications. We investigate the influence of the atomic vapor density and the pump laser intensity on the resonance parameters in order to optimize the amplitude ratio of the wide and narrow components and achieve the highest amplitude-width ratio value for the narrow component of the EIA resonance.
Riga Group’s recent results on laser applications for skin diagnostics
Authors: Spigulis J , Lukinsone V , Oshina I , Kviesis-Kipge E , Tamosiunas M , Lihachev A
Published: April 2021

The laser-related activities are reviewed of the Biophotonics Laboratory at UL Institute of Atomic Physics and Spectroscopy following the previous ICSQE-2018 conference. Four recent research projects are considered, including one EC Horizon-2020 project, two European Regional Development Fund (ERDF) projects and one project funded by the Latvian Council of Science (LCS). The projects are generally aimed at developing new optical methods and technologies for non-invasive in-vivo skin assessment to facilitate the early diagnostics of skin malformations (including cancers). Most of the projects explore novel approaches of camera-based biomedical imaging to the clinical diagnostics and recovery monitoring.
Low-level laser therapy in cases of dentin hypersensitivity
Authors: Balcheva G , Balcheva M , Panov V , Madjova C
Published: April 2021

Dentin hypersensitivity is characterized by short, sharp pain in response to a stimulus – thermal, drying, tactile, osmotic or chemical. These stimuli cause an unpleasant sensation lasting from seconds to minutes after provocation. Many alternative treatment methods have been introduced. One of the options is the use of low-level laser therapy, whose effect is based on the increase of the excitability threshold of the free nerve endings causing an analgesic effect, as well as on the stimulation of pulp mesenchymal cells to differentiate into odontoblasts and produce reparative dentin. To investigate the efficacy of low-level laser therapy in treating dentin hypersensitivity, we selected a group of patients complaining of sensitivity at the cervical area of teeth provoked by stimuli such as air, touch and cold spray. The patients were with good oral hygiene and with intact teeth or small non-carious cervical lesions. They confirmed the absence of serious systematic and psychological diseases; some of them smoked electronic cigarettes, which may aggravate the pain. The patients were submitted to six sessions of treatment of dentin hypersensitivity with intervals from 48 to 72 hours, as indicated by the manufacturer of the low-intensity laser device. The painful sensation was measured before each of the six sessions and immediately after the treatment. The results were evaluated by using a four-point scale. The answers assessment showed good results – there was a decrease of the pain sensation from 3-4 level to 1-2 level. Yet, we have to interpret this carefully, because pain evaluation is subjective and some of the smokers have quit smoking electronic cigarettes during the treatment. As a method of curing dentin hypersensitivity, the low-level laser therapy shows a good treatment efficacy, but needs further continuous follow-up.
Technological parameters optimization in picosecond laser texturing of titanium surfaces
Authors: Dikova T D , Kulinich S A , Iwamori S , Yamaguchi S
Published: April 2021

The present paper deals with studying the surface morphology of commercially pure (cp) Ti Gr-2 and Ti6Al4V treated by a picosecond laser and with determining the optimal technological parameters for fabricating cavities with a regular shape and a depth of about 10 μm. The surfaces of disk-shaped samples were treated by a commercial picosecond laser with a power of 0.2 W, 0.5 W and 1 W and a number of pulses between 1,000 and 20,000. The samples’ surface was investigated by SEM and a non-contact 3D surface profiler. The technological parameters were optimized by regression analysis using the QStatLab software. It was found that after laser treatment with the lowest values of the above parameters only the surface roughness changed in the case of cp-Ti, while its Ti6Al4V counterpart had a “melted zone” with a 5.5-μm depth. Raising the values of the regime parameters mainly led to an increased cavity depth on the cp-Ti surface, while those on Ti6Al4V increased in all dimensions. The surface roughness of both materials was higher after the laser treatment and grew bigger as the laser power and the number of pulses were increased. The optimal technological parameters for treatment of cp-Ti and Ti6Al4V were found. The results obtained can be used for picosecond laser texturing of titanium surfaces using linear or dimple types of patterns.
Endogenous and exogenous fluorescence diagnosis of tumors in the lower part of the gastrointestinal tract
Authors: Genova Ts. , Valkov H. , Vladimirov B. , Kolev B. , Angelov I. , Avramov L. , Borisova E.
Published: April 2021

Endogenous fluorescence measurements using UV-VIS excitation wavelengths revealed a variety of natural fluorophores, including the amino acids tyrosine and tryptophan, coenzymes – NADH and flavin, collagen and elastin. Deep minima in the tumor fluorescence signals were observed in the region 540 – 575 nm related to re-absorption of hemoglobin. Such high haemoglobin content was also found as an indication of the tumor’s vascularization and it was clearly pronounced in all dysplastic and tumor sites investigated ex vivo. A photosensitizer from the family of porphyrins was applied as an exogenous fluorescent marker, namely, delta-aminolevulinic acid/protoporphyrin IX (5-ALA/PpIX). The 5-ALA was administered per os six hours before endoscopic observation and spectral measurements at a dose of 20 mg/kg. A high-power light-emitting diode at 405 nm was used as an excitation source (LED-405, 25 mW, CW, Polironik Ltd., Russia). A fiber was introduced through an endoscopic instrumental channel in order to retrieve information about the fluorescence to a USB4000 micro-spectrometer (OceanOptics Inc., Dunedin, USA). The fluorescence detected from in vivo tumor sites has very complex spectral origins. It consists of autofluorescence, fluorescence from exogenous fluorophores and re-absorption from the chromophores accumulated in the investigated tissue. However, the fluorescence of 5-ALA/PpIX was clearly pronounced in the 630 – 710 nm region, having a significant contrast with the surrounding normal mucosa pale fluorescence in the blue-green spectral region. The precancerous mucosa also revealed a red light signal, but with lower intensity levels than the carcinoma lesions observed in situ. False-positive signals were recognized in case of inflammations in the colon and rectum areas, where 5-ALA/PpIX was accumulated as well.
Infrared thermography in the diagnostics of hidden infections – case reports
Authors: Grozdanova-Uzunova R
Published: April 2021

Two interesting case reports are presented that prove the many advantages of infrared thermography as a new diagnostic tool that can be successfully used in detecting hidden pathological processes. The first case report is about a 40-year-old female patient with alopecia areata – head lesion. The dermatological treatment gave no results, so the patient was sent for focal diagnostics. The infrared thermography showed two sites with hidden infection. The first one was of dental origin – periapical granuloma on the first mandibular left molar; the second site was in the genital area – endometriosis. A root canal retreatment of the molar and a gynecological operation were performed. After elimination of the focal infection, the hairless lesion recovered fully. The second case report is of a 34-year-old healthy male with alopecia areata of the beard and no other complaints. Infrared thermography detected active sinusitis of the ethmoidal left sinus. A microbiology test confirmed the presence of Staphylococcus Aureus and after an antibiotic course the beard recovered fully. These cases clearly illustrate the advantages of infrared thermography as a novel promising, non-invasive and objective diagnostic tool for clarification of difficult-to-diagnose cases.
Near-infrared autofluorescence spectroscopy and photobleaching detection of melanin-pigmented cutaneous neoplasia
Authors: Mircheva V , Zaharieva L , Ilyov S , Troyanova P , Lihacova I , Lihacovs A , Bratchenko I , Bratchenko L , Khristoforova Yu , Zakharov V , Avramov L , Borisova E
Published: April 2021

In the current study, an excitation laser source emitting at 785 nm (100 mW, CW) was used to obtain ex vivo fluorescence of endogenous melanin in pigmented skin with benign, dysplastic and malignant cutaneous lesions. The samples were obtained after surgical removal during standard excision procedure and split for spectral analysis and histological verification. The samples of benign (BN-5) and dysplastic (DN-3) nevi and of malignant melanoma (MM-7) were used as representative for harmless/harmful cutaneous neoplasia with a similar melanin pigmentation. Emission in the range 800 – 1100 nm was detected and compared using a USB4000 micro-spectrometer (Ocean Optics Inc., USA). The photobleaching dynamics was observed for the emission maxima at 825 nm for 10 minutes with a step of 20 s. These near-IR autofluorescence spectra may be assigned to a single fluorophore if appropriate excitation wavelength is applied, which simplifies the rapid analysis related to the appearance and concentration evaluation of a given type of endogenous fluorophore, as well as allows the evaluation of some parameters, such as photobleaching dynamics, as a diagnostic indicator assessing the tissue state. A diagnostic accuracy of 93.3% was achieved for MM lesions validation when the NIR fluorescence intensity and photobleaching rate values were used to discriminate between nevi and melanoma lesions.
Assessing temperature increase during photodynamic therapy: a simulation model
Authors: Kareliotis G , Kalkou M , Tsigaridas G , Makropoulou M , Serafetinides A A
Published: April 2021

Photodynamic therapy (PDT) is a non-invasive procedure mainly used for treatment of malignancies. It is based on cytotoxic products generation after the excitation of absorbed photosensitizing drugs by non-ionizing radiation. The wavelength chosen is usually in the 620 - 700 nm region, resulting in limited tissue penetration depth. The relatively low irradiance values and the treatment time that, in most cases, lasts for a few minutes result in limited photothermal effects, which are usually overlooked. Therefore, in this study we computationally assess the temperature distribution during PDT in a cancer bearing mouse model. A user-friendly application is created that could be used in the treatment planning step of PDT. It receives as input various parameters, such as laser power, beam radius, irradiation time, body temperature and returns the maximum tissue temperature and irradiance values. Furthermore, it offers visualization of the generated effects through the spatial distribution of temperature, irradiance, acute necrosis and damaged tissue percentage that are presented in the form of interactive 3D plots. The conducted simulations reveal that 43 °C, which are in the hyperthermia range, are difficult to be excessed in PDT clinical practice, although topical thermal effects are observed.
Minimization of spikes by pulse doubling pump in Q-CW operation of a Nd:YAG laser with flash lamp pumping
Authors: Bouadjemine R , Louhibi D
Published: April 2021

In this paper, we examine the factors minimizing the transient spikes in the evolution of a laser pulse. Our study is based on a mathematical model simulation of the laser implemented in Matlab Simulink. A model of the flash lamp excitation circuit is also included in the overall laser model. It allows us to show the correlation between the characteristics of the Q-CW laser pulse mode and the various physical parameters of the oscillator. The model proposed is applicable to the case of a single- or multiple-mesh pulse-forming network (PFN) of the flash lamp excitation current. The spikes in the laser pulse are substantially reduced by using the pulse doubling pump (PDP) technique. The simulations results give a clean laser pulse in a single mesh and in five meshes.
Numerical study of a ring-array-concentrator for Nd:YAG solar laser pumping
Authors: Boutaka R , Bouadjemine R , Liang D , Hendaoui N , Kellou A
Published: April 2021

In this paper, we report a numerical study of a Nd:YAG solar laser side-pumped by a ring-array concentrator (RAC) system. We used the ZEMAX© and LASCAD© software to optimize a 1.5-m diameter RAC system, a 3V-shaped pump cavity within which a Nd:YAG single-crystal laser rod (4-mm diameter, 30-mm length) is mounted, and the laser resonator parameters. The multimode solar laser output power was numerically calculated to be 34.8 W in a continuous-wave mode, corresponding to a solar laser collection efficiency of 19.69 W/m2, a slope efficiency of 3.07%, and 2.3% solar-to-laser conversion efficiency, indicating an improvement of 2.05, 1.31, and 1.5 times, respectively, relative to that of the previous results for a side-pumped Nd:YAG solar laser. Moreover, this study is the first numerical analysis of a Nd:YAG solar laser side-pumped by a RAC concentrator.
RF magnetron sputtering of Bi12TiO20 thin films on various substrates
Authors: Balchev I , Nurgaliev T , Kostadinov I. , Lakov L , Aleksandrova M , Avdeev G , Valcheva E , Russev S , Genkov K , Milenov T
Published: April 2021

Using RF magnetron sputtering, we deposited Bi12TiO20 (BTO) thin films on various substrates (glass, quartz, stainless steel (SS304), (001) and (111) Si and sital-ceramics, Al and Cu foils). The films had a constant thickness of 1.3 μm. The as-deposited films were studied by Raman spectroscopy, scanning electron microscopy (SEM) and energy dispersive X-ray analysis (EDX), as well as by grazing incidence X-ray diffractometry (GIXRD). The GIXRD results reveal that the films are amorphous, while the Bi/Ti ratio varies between 9.5/1 and 11.8/1, as shown by the energy dispersive X-ray analysis (EDX). Further, the films deposited on glass and SS304 substrates were modified by laser irradiation (CuBr laser with a wavelength λ = 511 nm) to obtain an ordered cubic phase. The films deposited on quartz and on (001) and (111) Si substrates were thermally annealed at about 510 oC for three hours in ambient atmosphere. The modified films were characterized by Raman spectroscopy, scanning electron microscopy (SEM), EDX and X-ray diffractometry.
Alternative technique for direct immobilization of biomolecules
Authors: Belina E , Karadjov J S , Shindarova K , Pavlova E , Dimitrowa T L , Kisov H , Dyankov G
Published: April 2021

The conventional approach to immobilization of biomolecules on the transducer of biosensors requires their embedding into the so-called embedding matrix. The procedure is complicated and laborious and performed by chemical means. As a results, not only biomolecules, but also other substances are deposited on the desired surface. This leads to reduced sensitivity and specificity due to the non-specific reactions with the molecules of the embedding matrix. Immobilization without embedding matrix (the so-called direct immobilization) guarantees the specificity of the biosensor and increases the sensitivity and accuracy. The main challenge in implementing this strategy is to preserve the bioactivity of the ligand. We have successfully solved the problem by using the matrix-assisted pulsed laser evaporation (MAPLE) technique. Here we report the properties of hemin directly immobilized on the transducer of a biosensor based on surface plasmon resonance (SPR).
Deep learning-based noise reduction for seismic data
Authors: Li Ying , Ma Zhonghua
Published: April 2021

An improved noise reduction algorithm based on feedforward denoising neural network (DnCNN) is proposed for the noise removal problem of noisy seismic data. The previous DnCNN originally used for noise reduction of seismic data had the problem of large network depth and thus reduced training efficiency. The improved DnCNN algorithm was first proposed for the noise reduction of natural data sets, and this paper applies the algorithm to the noise reduction of seismic data after adjusting the relevant parameters. The analysis and comparison of the experimental results show that the DUDnCNN algorithm can remove noise with high efficiency, and the algorithm has certain feasibility and significance for further research in seismic data noise reduction.
Study on Prediction of Course Failure Based on Improved Bagging-C4.5 Algorithm
Authors: Luo Jing
Published: April 2021

At present, the research of academic early warning system needs to know the situation of course failing. There are many research methods of course failing, but there are defects of low accuracy and long prediction time. In order to improve the accuracy and accuracy of course failing prediction, the improved Bagging-C4.5 algorithm is designed to predict the course failing. Firstly, the research progress of current social talent demand and course failure prediction is analyzed, then the test data set is determined, and the feature combination with the most influence is selected as the feature set of course failure prediction. Finally, the improved Bagging-C4.5 algorithm is used to realize the failure prediction, and the comparison test is conducted with other algorithms. The results show that the algorithm in this paper has high accuracy and accuracy in predicting course failure.
Prioritizing User Requirements based on Analytic Hierarchy Process
Authors: Tang Jiahui
Published: April 2021

As users have higher and higher requirements for products and competition in the product market becomes increasingly greater, how to use company resources more scientifically and quickly develop products that meet user needs becomes more and more important. Therefore, in this study, we apply the analytic hierarchy process for user requirement analysis, and clarify the advantages of the analytic hierarchy process compared with the existing commonly used models for prioritizing user needs and present the principles, methods, and results of the analytic hierarchy process in detail.
Application of SOM, BP Neural Network in Physical Health Assessment of Students
Authors: Ning Miao , Yong Lan , Ming Lu , Yun Liu
Published: April 2021

At present, the physical fitness measurement carried out by the school is an important way to detect the students’ physical health. How to use the students’ physical fitness scores to scientifically and reasonably evaluate the students’ physical fitness and health. This paper attempts to apply BP neural network to evaluate students’ physical health, use software to study and train the designed network, establish the corresponding neural network model, and use SOM neural network clustering to analyze the results of physical health. And realize the physical health evaluation of physical test results. Analysis of the results shows that the BP neural network can evaluate the physical health of the physical test results and show the quality of the students’ physical quality. By using BP neural network to evaluate physical health, the method is operable and has wide practicability, and provides the basis for the credibility of physical health.
Research on Portrait of Online Public Opinion Subject Based on Big Data of Public Opinion—A Case Study of Notre Dame
Authors: Jia Nan , Xia Yixue , Jia Ling
Published: April 2021

[Purpose/Significance] As online public opinion gradually becomes an important carrier of public opinion expression, multi-dimensional portrayal of each subject of public opinion based on public opinion big data helps to understand the characteristics of each dimension in an all-round way, thereby enhancing the pertinence of network social governance. This article aims to actively explore the subject of online public opinion subject based on public opinion big data and machine learning algorithms. [Method/Process]Use web crawler technology to crawl the microblog and comment data of the “Notre Dame” event, and describe it from the three dimensions of the basic attributes of microblog users, the microblog comment stand and the topic of microblog comments. In the basic attribute dimension, the reputation index of each user is calculated using information such as the number of fans and followers; in the Weibo comment stand dimension, machine learning algorithms are used to classify the comment stand, and the oversampling technique is used to oversample the data set Processing to improve the classification effect; in the microblog comment subject dimension, the LDA model is used to divide the subject of the review data; finally, a fusion portrait is developed based on the above portrait dimension.[Results/Conclusion]Through the integration of portraits, comprehensive use of image information such as microblog user influence, comment stances, comment topics, comment time, etc., to propose guidance strategies for different scenarios, helps to improve the targeted and effectiveness of network social governance.
Research on Feature Extraction Algorithm Commonly Used in Brain-computer Interface Technology
Authors: Zhang Yifan , Wang Yaojun
Published: April 2021

Brain-computer interface (BCI) is an effective and direct channel of information exchange between human brain and external devices such as computer, which can provide auxiliary information acquisition and treatment means for the medical and other fields in the future. This paper focuses on four kinds of feature extraction algorithms such as power spectral density (PSD), wavelet transform, Hilbert-Huang transform (HHT) and common space pattern (CSP), which are commonly used to process abnormal electroencephalogram (EEG) signals in brain-computer interface technology. This paper also introduces their respective principles, characteristics and application fields, analyzes and compares the advantages and disadvantages of these algorithms, and obtains the development direction of feature extraction algorithms in the future. Finally, it also briefly discusses the ethical issues brought about by brain-computer interface technology.
Big Data Infrastructure for Marine Environment in the Arctic and Ice Silk Road
Authors: Lin Bo
Published: April 2021

In this paper, the big data infrastructure for the marine environment in the Arctic and Ice Silk Road from National Marine Environmental Forecasting Center is taken as the main line, the construction of the Arctic observation system, the basic data construction of the Ice Silk Road and the construction of the high-performance computer system are described in detail, which clearly outlines the technical framework and construction progress of the Marine environmental data sources, data acquisition and transmission, data warehouse of the Arctic and Ice Silk Road. And from the perspective of climate change, the future of the Arctic and Ice Silk Road and the trend of corresponding marine environmental monitoring, warning (alarm), security assurance services and their big data infrastructure construction are prospected roughly.
Difference Analysis and Multiple Regression of College Students’ Network Behavior from the Perspective of Big Data
Authors: Jianjian Wang , Wenqiang Miao , Guofang Sheng , Zeyu Wang
Published: April 2021

With the rapid development of information technology, the Internet has become the main media and important way for college students to obtain information, learn and communicate. The advent of the era of new media has greatly enriched the means and approaches of ideological work in colleges and universities. Therefore, the research on college students’ Internet behavior is particularly important. Through questionnaire survey and using SPSS data processing software, this paper conducts a difference analysis on the network behavior of different groups of college students, carries out multiple regression on the influencing factors of college students’ network behavior, summarizes the existing problems and puts forward reasonable suggestions, so as to explore the leading path of ideological and political education on college students’ network behavior.
A q-rung orthopair fuzzy multiple attribute group decision making method based on generalized Maclaurin symmetry mean and Dombi t-norm and t-conorm
Authors: Xu Hui , Zhang Runtong
Published: April 2021

Generalized Maclaurin symmetry mean (GMSM) operator is an effective tool in the process of multi-attribute group decision making (MAGDM) with the characteristic of capturing the interrelationships between multiple arguments. at the same time, q-rung orthopair fuzzy set (q-ROFS) is a good tool to describe uncertainty and fuzziness. To effectively aggregate q-rung orthopair fuzzy information based on the extension of Dombi operations, some novel operators and ideal properties of the proposed operators are put forward in this study. Further, this research present a novel method to MAGDM based on the proposed operators. Finally, the applicability of new method can be proved by a numerical experiment. A detailed parametric analysis and a comparative analysis are also discussed to highlight the flexibility and superiority of the method proposed in this paper.
Research on Network Intrusion Detection Method Based on Machine Learning
Authors: Xu Guorong
Published: April 2021

After years of development, the Internet has penetrated into all areas of society, and various network intrusions occur almost every day. In order to enhance the security of the network, intrusion detection technology has received more and more attention. Network intrusion detection is a branch of computer security. Its goal is to automatically and effectively detect intrusion traffic in the network and provide timely warnings. Network intrusion detection technology has become an important means to resist network intrusion in recent years, and network intrusion detection systems of various scales have been applied in the network. Due to the diversity of intrusion methods, traditional digital authentication and firewalls and other network security measures are difficult to meet the needs of network intrusion detection. The machine learning boom has gradually emerged, and machine learning methods have begun to be applied to the field of intrusion detection, which has become a research hotspot in this field. This article discusses machine learning methods, discusses adding machine learning algorithms to the network intrusion detection link to achieve effective detection of network intrusion traffic, so as to obtain a good network intrusion detection effect.
GAU-Net: U-Net Based on Global Attention Mechanism for brain tumor segmentation
Authors: Gan Xiuling , Wang Lidan , Chen Qi , Ge Yongjie , Duan Shukai
Published: April 2021

Deep learning has shown great advantages in biomedical image segmentation. The classic model U-Net uses a stacked encoding-decoding structure of convolution operations for feature extraction and pixel-level classification. The stacking of convolutional layers can expand the receptive field, but it is still a local operation and cannot capture long-distance dependence. Therefore, in this work, we propose a Global Attention Mechanism that combines channel attention module and spatial attention module and integrates different convolutions in it. Besides, we design a residual module for the traditional up and down sampling blocks. And finally, we combine them with U-Net to propose a new global attention network GAU-Net. We perform experiments on the dataset BraTS2018. Our model has increased the mIoU from 0.65 to 0.75 with only 5.4% of U-Net parameters. At the same time, the inference time is also significantly shortened with relatively good performance.
GAU-Nets: Graph Attention U-Nets for Image Classification
Authors: Zhao Haoran , Zhang Chengwei
Published: April 2021

Graph neural network is a research hotspot in the field of deep learning recently, and its application has become more and more extensive. A new graph neural network model is proposed, Graph Attention U-Nets(GAU-Nets for short), which has strong processing capabilities for graph structures. In GAU-Nets, the graph data structure is preliminarily processed through GCN, and then extracted the features by the graph pooling and Unpooling blocks. We innovatively added the attention mechanism to GAU-Nets to avoid forgetting the important information. We have done a lot of image classification experiments on MS-COCO and other datasets. The experimental results prove that GAU-Nets performs better than other traditional graph neural network models. Without bells and whistles, our GAU-Nets method has an accuracy of 69.1% on the MS-COCO data set and 82.1% on the VOC 2007 data set, which has surpassed all benchmark methods.
Research on Anti-noise of Image Compression Based on Variational Autoencoder
Authors: Gengzeng Li
Published: April 2021

We introduced the Variational Compression Autoencoder (VCAE) that introduces noise training, and uses KL divergence to balance compression and anti-noise. Variational autoencoders improve the anti-noise performance of the compression framework through noise training and learning decoupled latent representations. Experiments on the Kodak datasets show that our framework is superior to other deep learning compression frameworks such as compressed autoencoders in the MS-SSIM indicator under the same bit error rate.
Research on Gesture Recognition Method Based on Deep Learning
Authors: Li Yizhi
Published: April 2021

Gestures are necessary and indispensable in people’s ordinary life communication. And gestures have become an important part of human-computer interaction. With the gradual maturity of human-computer interaction systems, the role of gestures in human-computer interaction is becoming more and more important. Due to the variety of gestures in practical use, coupled with factors such as environment and light changes, it makes it very difficult for computers to recognize the correct gestures from image information. Deep learning has achieved certain research results through several years of development, which provides new research ideas for gesture recognition. This paper firstly introduces the common methods and processes of gesture recognition, and then proposes a new convolutional neural network structure by studying convolutional neural networks and adding Inception structure to the network to improve the performance of the network. Finally, experimental validation is conducted, and the experimental results show that the new convolutional neural network achieves an average recognition rate of 97.8% on the test set, which is a good result.
Recognition of navel orange image with complex background based on residual network
Authors: Li Xiaoxin , Tan Yumei , Lu Xinxin , Zhang Bo
Published: April 2021

In order to solve the problem that the detection effect of navel orange recognition in complex background is poor, we propose a navel orange recognition method based on residual network. In this study, the navel orange classification dataset was constructed and labeled, and the performance of five classic convolutional neural network models on this dataset was evaluated, including AlexNet, Improved LeNet, SqueezeNet, ResNet-18, GoogLeNet. The results present significant accuracy obtained by the ResNet-18 model, with accuracy of 98.27%, which is more suitable for navel orange image recognition in complex background.
Design of online course knowledge recommendation system based on improved learning diagnosis model
Authors: Gan Baiqiang , Zhang Chi , Dong Qiuping , Sun Wei
Published: April 2021

In order to better manage curriculum resources, give full play to the practical value and efficiency of university curriculum, provide an efficient and convenient interface for the majority of students and curriculum resources, and meet the different needs of different students in learning, this paper aims at the current content-based recommendation algorithm cannot solve the problem of user interest drift, online curriculum resource sharing platform cannot directly communicate with students this paper proposes a learning diagnosis method which combines the similarity of courses and user interest model to calculate the similarity. According to the actual characteristics and attributes of online course resources, and using the improved algorithm, this paper designs and implements a set of online course knowledge recommendation system based on the improved learning diagnosis model, to effectively alleviate the problems of low user interest and direct communication between online and offline classroom users in the current online course learning process.
Multi Physical Field Simulation of Irreversible Electroporation
Authors: Xu Chao , Zhang Yi
Published: April 2021

Irreversible electroporation (IRE) uses non thermoelectric pulse to ablate tumours, which is an effective cancer treatment method. The pulse is transmitted through a minimally invasive needle electrode inserted into the target tissue and causes cell death by creating nanoscale membrane defects. Irreversible electroporation has been shown to be safe and effective in the treatment of tumours of the brain, liver, kidney, pancreas and prostate located near key blood vessels and nerves. Determining the parameters accurately of the applied pulsed electric field to kill all tumour cells and minimize damage to healthy tissue is the key to the success of IRE for the treatment of malignant tumours. In this paper, according to the researches of irreversible electroporation in the treatment of tumour in recent years, the distribution of electric field and temperature in tissue during IRE is calculated by numerical method, which provides a methodological basis for the treatment of tumour ablation with IRE.
Application of Transition probability based on random graph and Markov chain for the Monitoring of Complex Network
Authors: Mao Hanqi , Liu Sihua
Published: April 2021

In recent years, the method of graph theory has been used in the process of solving many practical problems, especially the transition probability of random graphs. Because it can be used to solve the problem of numerical variables with random phenomena, it is also practical. In the middle, the Markov chain provides a transformation and functional relationship from a set of discrete vectors to the state space, and is often used to describe the transition from one initial state to another end state in various state spaces. The process and the transition process are random. The research in this paper is based on the transition probability of the random graph and the Markov chain to establish a mathematical model, so that complex networks can be monitored, including the ability to predict the long-term steady state of complex networks. The required transition time, the quantitative relationship between the number of monitoring points and the reliability and sensitivity of the network were obtained.
Speech Emotion Recognition algorithm based on deep learning algorithm fusion of temporal and spatial features
Authors: An Xu Dong , Ruan Zhou
Published: April 2021

In recent years, human-computer interaction systems are gradually entering our lives. As one of the key technologies in human-computer interaction systems, Speech Emotion Recognition(SER) technology can accurately identify emotions and help machines better understand users’ intentions to improve the quality of human-computer interaction, which has received a lot of attention from researchers at home and abroad. With the successful application of deep learning in the fields of image recognition and speech recognition, scholars have started to try to use it in SER and have proposed many deep learning-based SER algorithms. In this paper, we conducted an in-depth study of these algorithms and found that they have problems such as too simple feature extraction methods, low utilization of human-designed features, high model complexity, and low accuracy of recognizing specific emotions. For the data processing, we quadrupled the RAVDESS dataset using additive Gaussian white noise (AWGN) for a total of 5760 audio samples. For the network structure, we build two parallel convolutional neural networks (CNN) to extract spatial features and a transformer encoder network to extract temporal features, classifying emotions from one of 8 classes. Taking advantage of CNN’s advantages in spatial feature representation and sequence encoding conversion, I obtained an accuracy of 80.46% on the hold-out test set of the RAVDESS data set.
Uterine magnetic resonance image segmentation based on deep learning
Authors: Niu Yu’ang , Zhang Yuanyang , Ying Liping , Li Hong , Chen Wenbo , Miao Hanci , Bao Nan
Published: April 2021

Endometrial diseases have always been a common disease among women worldwide. For the prevention of endometrial diseases and the treatment after illness, there is an urgent need to solve the problem of positioning the uterus, which is also one of the difficult problems that plague clinicians. Research in recent years has shown that neural networks based on deep learning are an important tool in the medical field. This article discusses the uterus magnetic resonance image segmentation method based on deep learning. First, we completed the preprocessing of the data based on the hessian matrix, and expanded the data, then inputted the data into the DenseUNet network. In this article, our method obtained 87.60% Dice Similariy Coefficient(DSC), 86.57% precision, 88.11% sensitivity and 99.75% specificity in 13 different test sets. Finally, our method was compared to UNet and UNet++ networks, and achieved better performance. Our method effectively solved the problems of uterus magnetic resonance image automatic segmentation. It can be used in the pre-operation planning of stem cell surgery to repair the endometrium.
An Attention-based Convolutional Neural Network for Melanoma Recognition
Authors: Chen Qi , Wang Lidan , Gan Xiuling , Duan Shukai
Published: April 2021

Early automatic and accurate melanoma recognition is an important method to reduce melanoma deaths. Existing methods are less sensitive to the position of the lesion areas. Network training may be affected by the uncorrelated noisy parts. In light of this circumstance, an end-to-end attention-based network AF-CNN for accurate melanoma recognition is proposed in this paper, which is mainly composed of pre-trained VGG19, attention blocks and a classification layer. Instead of treating each part of the input dermoscopy images equally, our AF-CNN model has strong discriminative ability to focus on the lesion areas. The AF-CNN was evaluated on the ISIC2017 dataset and concluded the proposed single model achieves the state-of-the-art result in melanoma recognition task.