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Quality of life issues in rural settlements:assessment by social media users

data Science and Management 2024年第4期7卷 283-292页

作者： Evgeniy V.Shchekotin Viacheslav L.Goiko Darya O.Dunaeva Polina A.Basina Julia K.Aleksandrova Department of Philosophy and History Siberian State University of Telecommunications and Information ScienceNovosibirsk630102Russia Laboratory of Applied Big Data Analysis Tomsk State UniversityTomsk634050Russia

Disparity in the quality of life of people living in rural and urban areas is among the major problems,often leading to greater depression among the rural population compared to those in urban areas who have access to a high standard of *** goal of this study was to identify the issues of the quality of life that are fundamental from the point of view of rural settlements and typical for the rural population in various regions of the Russian Federation with diverse geographical,socioeconomic,and demographic *** practical relevance of our study is based on the identification of the scope of typical problems related to the quality of life in rural *** fundamental value of this study is the significance of digital tracks,which can serve as a source of *** data sources included messages and posts discussing various quality of life aspects of the rural population published by *** this study,we used messages and posts with negative implications from communities in rural settlements in ten Russian *** issues include housing infrastructure and utilities,transportation,the environment,telecommunications,banking,healthcare,and education.

关键词： Rural settlements Quality of life Social media Russia Digital footprint

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Coevolution of Opinions and Ties in a Social Network: A Differential Equations Approach 5

Coevolution of Opinions and Ties in a Social Network: A Diff...

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5th International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency, SUMMA 2023

作者： Gubanov, Alexander V. Goiko, Vyacheslav Saifullin, Eduard Kozitsin, Ivan V. Moscow Institute of Physics and Technology Department of Mathematical Modelling of Compex Systems and Optimization Dolgoprudny Russia Tomsk State University Laboratory of Applied Big Data Analysis Tomsk Russia Institute of Control Sciences of RAS Laboratory of Active Systems Moscow Russia

In this paper we propose an original model, describing a coevolution of ties and opinions in large-scale social networks. The model is a system of determined differential equations for the mean values of some network ... 详细信息

ISBN: (纸本)9798350309126

关键词： Differential equations

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Preferential attachment network model with aging and initial attractiveness

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Communications in Theoretical Physics 2022年第3期74卷 97-103页

作者： Xiao-Long Peng Complex Systems Research Center Shanxi UniversityTaiyuan 030006ShanxiChina Shanxi Center for Applied Mathematics Shanxi UniversityTaiyuan 030006ShanxiChina Shanxi Key Laboratory of Mathematical Techniques and Big Data Analysis on Disease Control and Prevention Shanxi UniversityTaiyuan 030006ShanxiChina

In this paper,we generalize the growing network model with preferential attachment for new links to simultaneously include aging and initial attractiveness of *** network evolves with the addition of a new node per unit time,and each new node has m new links that with probability Π_(i) are connected to nodes i already present in the *** our model,the preferential attachment probability Π_(i) is proportional not only to k_(i)+A,the sum of the old node i's degree ki and its initial attractiveness A,but also to the aging factor τ_(i)^(−α),whereτi is the age of the old node *** is,Π_(i)∝(k_(i)+A)τ_(i)^(−α).Based on the continuum approximation,we present a mean-field analysis that predicts the degree dynamics of the network *** show that depending on the aging parameter α two different network topologies can *** α<1,the network exhibits scaling behavior with a power-law degree distribution P(k)∝k^(−γ) for large k where the scaling exponent γ increases with the aging parameter α and is linearly correlated with the ratio A/***,the average degree k(ti,t)at time t for any node i that is added into the network at time ti scales as k(t_(i),t)∝t_(i)^(−β) where 1/β is a linear function of A/*** α>1,such scaling behavior disappears and the degree distribution is exponential.

关键词： complex network model aging preferential attachment power-law behavior

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Cost Effective and Sensitive Electrochemical Detection of Dopamine on Laser Driven Sensor

Cost Effective and Sensitive Electrochemical Detection of Do...

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International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Mrinal Vashisth Amrit L. Hui Department of Psychology National Research Tomsk State University Tomsk Russian Federation Research Laboratory of Applied Big Data Analysis National Research Tomsk State University Tomsk Russian

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

In this research, a cost-effective sensor was developed for the electrochemical detection for dopamine in the biological samples. The optimal laser parameters were used to fabricate graphene oxide (GO) material to obtain the highly stable laser induced graphene oxide (LIGO) sensor. The surface behavior of the sensor was studied by scanning electron microscope (SEM), Raman spectroscopy (RS) and electrochemical characterization were carried out vis cyclic voltammetry. The electroactive surface area of sensor was studied which shows the remarkable electron transfer mobility. The sensor shows promising linear concentration range (1–100 μM) and lower limit of detection (0.1μM). The LIGO sensor also demonstrates good reproducibility and stability. Moreover, the LIGO sensor showed the sensitive detection for dopamine concentration in serum and urine with good regain. However, low cost and highly efficient LIGO sensor shows tremendous application on real detection of dopamine.

关键词： Scanning electron microscopy Costs Graphene Power lasers Surface morphology Surface emitting lasers Laser stability Reproducibility of results Surface treatment Substrates

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Laser Driven Flexible Ultra-Robust Sensor for Bioelectronic Applications

Laser Driven Flexible Ultra-Robust Sensor for Bioelectronic ...

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International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE)

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

Nowadays there is an importance of flexible carbon-based sensors for sensitive detection of human skin interference signal to develop biomedical applications. In this research, the cost effective, flexible sensor has been fabricated based on laser induced graphene oxide (LIG) on green substrate for the acquisition of biomedical signal. The ecofriendly laser reduction process was used to develop highly stable sensor, which enhanced the biomedical signal. The newly fabricated flexible dry sensor is studied to overcome the limitation of currently available sensor. The sensor surface morphology was characterized by Scanning Electron Microscopy (SEM), and Fourier Transform Infrared Spectroscopy (FTIR) was performed to analyze the absorption processes bands of Graphene oxide (GO). These sensors are flexible and adhere more compatibly to human skin. In terms of performance, the fabricated LIG sensor show low impedance as compared to commercial sensor for bio-signal detection. The fabricated sensor has recorded low signal to noise ratio (SNR) as compared to commercial sensor. Furthermore, the developed sensor was tested for temperature, strain, and breathing sensing. The results indicate that the developed sensor is reusable without gel. Finally, the developed dry sensor provides good biocompatibility, excellent skin and sensor contact and no significant irritation on skin.

关键词： Temperature sensors Surface impedance Scanning electron microscopy Graphene Surface morphology Sensor phenomena and characterization Skin Biosensors Surface treatment Signal to noise ratio

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Sensitive EEG Brain Signal Detection for RGB Color Using Laser Integrated Electrode

Sensitive EEG Brain Signal Detection for RGB Color Using Las...

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International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Amrit L. Hui Mrinal Vashisth Research Laboratory of Applied Big Data Analysis National Research Tomsk State University Tomsk Russian Federation Department of Psychology National Research Tomsk State University Tomsk Russian Federation

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

The sensor technology has allowed for sensitive detection of Electroencephalography (EEG) signals. In this work, the ecofriendly flexible laser induced graphene oxide (LIG) electrode was fabricated for Red, Green and Blue (RGB) neural signal detection. The flexible recycle substrate was used for the fabrication of the electrodes. The laser engraving process was performed on the graphene oxide electrodes which enhanced the surface area of electrode. The laser optimization was done to obtain suitable conductive electrode. The electrode surface morphology was studied by impedance method and scanning electron microscopy (SEM). The integrated circuit was fabricated in order to achieve highly sensitive neural signals for RGB colors. The 18 dry LIG electrode setup was employed to investigate RGB color neural signals in 15 participants. The obtained RGB neural signals are smooth due to good conductivity of dry LIG electrodes as compared to commercial electrodes. The performance of dry LIG electrodes is promising than commercial electrode. The fabrication cost of dry LIG electrode is ten times cheaper than commercially manufactured electrode.

关键词： Electrodes Scanning electron microscopy Surface impedance Image color analysis Graphene Measurement by laser beam Surface morphology Electroencephalography Surface treatment Signal detection

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A Multilayer Network Constructed for Herb and Prescription Efficacy analysis

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Computer Systems Science & Engineering 2024年第3期48卷 691-704页

作者： Xindi Huang Liwei Liang Sakirin Tam Hao Liang Xiong Cai Changsong Ding School of Informatics Hunan University of Chinese MedicineChangsha410208China Faculty of Science and Information Technology Phnom Penh International UniversityPhnom Penh12253Cambodia Institute of TCM Diagnostics Hunan University of Chinese MedicineChangsha410208China Institute of Innovation and Applied Research in Chinese Medicine Hunan University of Chinese MedicineChangsha410208China Big Data Analysis Laboratory of Traditional Chinese Medicine Hunan University of Chinese MedicineChangsha410208China

Chinese Medicine(CM)has been widely used as an important avenue for disease prevention and treatment in China especially in the form of CM prescriptions combining sets of herbs to address patients’symptoms and ***,the selection and compatibility of herbs are complex and abstract due to intrinsic relationships between herbal properties and their overall *** analysis is applied to demonstrate the complex relationships between individual herbal efficacy and the overall function of CM *** illustrate their connections and correlations,prescription function(PF),prescription herb(PH),and herbal efficacy(HE)intranetworks are proposed based on CM theory to identify relationships between herbs and *** three networks are then connected by PF-PH and PH-HE interlayer networks adopting herb dosage to form a multidimensional heterogeneous network,a Prescription-Herb-Function Network(PHFN).The network is applied to 112 classic prescriptions from Treatise on Exogenous Febrile and Miscellaneous Diseases to illustrate the application of *** PHFN is constructed including 146 functions in PF intra network,89 herbs in the PH intra network,and 163 herbal efficacies in the HE intra *** results show that herb pairs with synergistic actions have stronger relevance,such as licorice-cassia twig,licorice-Chinese date,fresh ginger-Chinese date,*** integration of dosage to the network helps to indicate the main herbs for cluster analysis and automatic *** also reveals the internal relationships between the functions of prescriptions and composed herbal efficacies.

关键词： Chinese medicine herb formula network analysis herb dosage

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Translational analysis of data science and causal learning in real-world clinical evaluation of traditional Chinese medicine

Science of Traditional Chinese Medicine

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Science of Traditional Chinese Medicine 2024年第1期2卷 57-65页

作者： Wei Yang Danhui Yi XiaoHua Zhou Yuanming Leng Institute of Basic Research in Clinical Medicine China Academy of Chinese Medical SciencesBeijingChina School of Statistics Renmin University of ChinaBeijingChina Beijing International Center for Mathematical Research Peking UniversityBeijingChina Department of Biostatistics School of Public HealthPeking UniversityBeijingChina National Engineering Laboratory of Big Data Analysis and Applied Technology Peking UniversityBeijingChina Department of Biostatistics School of Public HealthBoston UniversityBostonMAUSA

Real-world clinical evaluation of traditional Chinese medicine(RWCE-TCM)is a method for comprehensively evaluating the clinical effects of TCM,with the aim of delving into the causality between TCM intervention and clinical *** study explored data science and causal learning methods to transform RWD into reliable real-world evidence,aiming to provide an innovative approach for *** study proposes a 10-step data science methodology to address the challenges posed by diverse and complex data in *** methodology involves several key steps,including data integration and warehouse building,high-dimensional feature selection,the use of interpretable statistical machine learning algorithms,complex networks,and graph network analysis,knowledge mining techniques such as natural language processing and machine learning,observational study design,and the application of artificial intelligence tools to build an intelligent engine for translational *** goal is to establish a method for clinical positioning,applicable population screening,and mining the structural association of TCM characteristic *** addition,the study adopts the principle of real-world research and a causal learning method for TCM clinical *** constructed a multidimensional clinical knowledge map of“disease-syndrome-symptom-prescription-medicine”to enhance our understanding of the diagnosis and treatment laws of TCM,clarify the unique therapies,and explore information conducive to individualized *** causal inference process of observational data can address confounding bias and reduce individual heterogeneity,promoting the transformation of TCM RWD into reliable clinical *** data science improves efficiency and accuracy for implementing *** proposed data science methodology for TCM can handle complex data,ensure high-quality RWD acquisition and analysis,and provide in-depth insights into clinical benefits of *** method supp

关键词： Traditional Chinese medicine Real-world study data science Causal learning Health information system Machine learning Artificial intelligence Treatment effects

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Coevolution of Opinions and Ties in a Social Network: a Differential Equations Approach

Coevolution of Opinions and Ties in a Social Network: a Diff...

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International Conference on Control Systems, Mathematical Modelling, Automation and Energy Efficiency (SUMMA)

作者： Alexander V. Gubanov Vyacheslav Goiko Eduard Saifullin Ivan V. Kozitsin Department of Mathematical Modelling of Compex Systems and Optimization Moscow Institute of Physics and Technology Dolgoprudny Russia Laboratory of Applied Big Data Analysis Tomsk State University Tomsk Russia Laboratory of Active Systems Institute of Control Sciences of RAS Moscow Russia

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Hilbert Curve Projection Distance for Distribution Comparison

arXiv

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arXiv 2022年

作者： Li, Tao Meng, Cheng Xu, Hongteng Yu, Jun Institute of Statistics and Big Data Renmin University of China China Center for Applied Statistics Institute of Statistics and Big Data Renmin University of China China Gaoling School of Artificial Intelligence Renmin University of China Beijing Key Laboratory of Big Data Management and Analysis Methods China School of Mathematics and Statistics Beijing Institute of Technology China

Distribution comparison plays a central role in many machine learning tasks like data classification and generative modeling. In this study, we propose a novel metric, called Hilbert curve projection (HCP) distance, to measure the distance between two probability distributions with low complexity. In particular, we first project two high-dimensional probability distributions using Hilbert curve to obtain a coupling between them, and then calculate the transport distance between these two distributions in the original space, according to the coupling. We show that HCP distance is a proper metric and is well-defined for probability measures with bounded supports. Furthermore, we demonstrate that the modified empirical HCP distance with the Lp cost in the d-dimensional space converges to its population counterpart at a rate of no more than O(n−1/2 max{d,p}). To suppress the curse-of-dimensionality, we also develop two variants of the HCP distance using (learnable) subspace projections. Experiments on both synthetic and real-world data show that our HCP distance works as an effective surrogate of the Wasserstein distance with low complexity and overcomes the drawbacks of the sliced Wasserstein distance. The code of this work is at https://***/sherlockLitao/HCP. Copyright © 2022, The Authors. All rights reserved.

关键词： Probability distributions

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