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The Association between Sugar-Sweetened Beverages and Hair Loss in Young Women

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Food science and Human Wellness 2024年

作者： Xiaojin Shi Yucheng Yang Haibing Yang Juwei Zhang Shuran Zhang Xiaona Na Dongying Fan Yulin Zhang Menglu Xi Yuefeng Tan Celi Yang Junhan Zhang Hsiaohan Tuan Ai Zhao Vanke School of Public Health Tsinghua University School of Health in social science The University of Edinburgh department of Dermatology School of Clinical Medicine Beijing Tsinghua Changgung Hospital Tsinghua University Institute for Healthy China Tsinghua University

The association between sugar-sweetened beverages （SSBs） intake and hair loss condition might give scientific support for better dietary practices to prevent hair loss. We aimed to explore the association of SSBs intake with the hair loss condition in women. This online cross-sectional study， conducted from January to April 2022， recruited 1169 women aged 18 - 45 years from 31 provinces on mainland of China. We used the updated version of the 15-item Beverage Intake Questionnaire （BEVQ-15） to evaluate the usual beverage consumption over the past month and the Hair-Shedding Visual Scale for Asian Women （HSVS-A） to evaluate the extent of hair shedding. We utilized binary logistic regression to assess the association between SSBs intake and hair loss. Among 1169 female participants with an average age of 27.2， there were 211 （18.0%） participants with hair loss. Compared with participants who never drank SSBs， those who drank more than 7 times/week or 1000 mL/weeks were more likely to have hair loss， with an adjusted odds ratio （OR） of 2.25 （95% confidence interval [CI] = 1.32，3.83） or 2.22 （95% CI = 1.28，3.85）. Furthermore， the mediation effect of anxiety status on the association between SSBs and female hair loss was observed， with a significant indirect effect (βindirect =0.0199， 95% CI = 0.0032，0.0418). In conclusion， our study showed that women who consumed higher amounts of SSBs had an increased risk of hair loss， which might attract more attention from young women and benefit from reducing SSBs consumption. Further longitudinal and interventional studies are still needed to confirm the current association and provide evidenced-based health education.

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An Intelligent Big Data Security Framework Based on AEFS-KENN Algorithms for the Detection of Cyber-Attacks from Smart Grid Systems

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Big Data Mining and Analytics 2024年第2期7卷 399-418页

作者： Sankaramoorthy Muthubalaji Naresh Kumar Muniyaraj Sarvade Pedda Venkata Subba Rao Kavitha Thandapani Pasupuleti Rama Mohan Thangam Somasundaram Yousef Farhaoui department of Electrical and Electronics Engineering CMR College of Engineering&TechnologyHyderabad 501401India department of Electronics and Communication Engineering Vardhaman College of Engineering KacharamShamshabad 501218India department of Electronics and Communication Engineering Sreenidhi Institute of Science and TechnologyHyderabad 501301India department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai 600062India department of Electrical and Electronics Engineering Bharat Institute of Engineering and TechnologyHyderabad 501510India department of Computer science and Engineering Amrita School of ComputingAmrita Vishwa VidhyapeethamBengaluru 560035India T-IDMS Department of Computer ScienceFaculty of Sciences and TechniquesMoulay Ismail UniversityErrachidia 52000Morocco

Big data has the ability to open up innovative and ground-breaking prospects for the electrical grid,which also supports to obtain a variety of technological,social,and financial *** is an unprecedented amount of heterogeneous big data as a consequence of the growth of power grid technologies,along with data processing and advanced *** main obstacles in turning the heterogeneous large dataset into useful results are computational burden and information *** original contribution of this paper is to develop a new big data framework for detecting various intrusions from the smart grid systems with the use of AI ***,an AdaBelief Exponential Feature Selection(AEFS)technique is used to efficiently handle the input huge datasets from the smart grid for boosting ***,a Kernel based Extreme Neural Network(KENN)technique is used to anticipate security vulnerabilities more *** Polar Bear Optimization(PBO)algorithm is used to efficiently determine the parameters for the estimate of radial basis ***,several types of smart grid network datasets are employed during analysis in order to examine the outcomes and efficiency of the proposed AdaBelief Exponential Feature Selection-Kernel based Extreme Neural Network(AEFS-KENN)big data security *** results reveal that the accuracy of proposed AEFS-KENN is increased up to 99.5%with precision and AUC of 99%for all smart grid big datasets used in this study.

关键词： smart grid Machine Learning(ML) big data analytics AdaBelief Exponential Feature Selection(AEFS) Polar Bear Optimization(PBO) Kernel Extreme Neural Network(KENN)

The Emerging Big Data Ecosystem and Digital Transformation of Poverty Governance in China

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social sciences in China 2024年第2期45卷 152-183页

作者： Wang Zhongyuan Su Yun Woo Daniel Kibler Fudan Institute for Advanced Study in social sciences MOE Laboratory for National Development and Intelligent GovernanceFudan UniversityChina department of Political science University of TwenteNetherlands department of Political science University of ZurichSwitzerland

Big data technology has become increasingly prevalent in facilitating the delivery of government services and public goods in China and *** article seeks to examine how big data can be leveraged in poverty governance in rural China,and what the political implications *** phenomenon of big data-driven welfare governance is particularly salient considering the broader context of governmental digital transformation,both within China and *** research sheds light on how big data usage functions in poverty alleviation,highlighting general motivations and developments in Chinese data-centric welfare *** importantly,this article introduces the novel concept of"digital cybernetic capacity"to examine public sector modernization as big data transforms the landscape of welfare delivery and *** delving into the intersection between technology and social welfare,we explicate how the use of big data in social welfare policies can go beyond merely improving information capacity to redefine state *** article argues that such a new governance ecosystem,driven as it is by data technologies,not only provides insights about the transformative resilience of Chinese governance but also opens a new theoretical frontier for research into other countries.

关键词： big data ecosystem targeted poverty alleviation(TPA) social welfare state capacity China

A Weakly-Supervised Crowd Density Estimation Method Based on Two-Stage Linear Feature Calibration

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 965-981页

作者： Yong-Chao Li Rui-Sheng Jia Ying-Xiang Hu Hong-Mei Sun the College of Computer science and Engineering Shandong University of Science and TechnologyQingdao 266590 the Faculty of Information science and Engineering Ocean University of ChinaQingdao 266000China the College of Computer science and Engineering Shandong University of Science and TechnologyQingdao 266590China the College of Computer science and Engineering Nanjing University of Science and TechnologyNanjing 210000China

In a crowd density estimation dataset,the annotation of crowd locations is an extremely laborious task,and they are not taken into the evaluation *** this paper,we aim to reduce the annotation cost of crowd datasets,and propose a crowd density estimation method based on weakly-supervised learning,in the absence of crowd position supervision information,which directly reduces the number of crowds by using the number of pedestrians in the image as the supervised *** this purpose,we design a new training method,which exploits the correlation between global and local image features by incremental learning to train the ***,we design a parent-child network(PC-Net)focusing on the global and local image respectively,and propose a linear feature calibration structure to train the PC-Net simultaneously,and the child network learns feature transfer factors and feature bias weights,and uses the transfer factors and bias weights to linearly feature calibrate the features extracted from the Parent network,to improve the convergence of the network by using local features hidden in the crowd *** addition,we use the pyramid vision transformer as the backbone of the PC-Net to extract crowd features at different levels,and design a global-local feature loss function(L2).We combine it with a crowd counting loss(LC)to enhance the sensitivity of the network to crowd features during the training process,which effectively improves the accuracy of crowd density *** experimental results show that the PC-Net significantly reduces the gap between fullysupervised and weakly-supervised crowd density estimation,and outperforms the comparison methods on five datasets of Shanghai Tech Part A,ShanghaiTech Part B,UCF_CC_50,UCF_QNRF and JHU-CROWD++.

关键词： Crowd density estimation linear feature calibration vision transformer weakly-supervision learning

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Travel-Limiting Disabilities in the United States: Why Accessibility Matters?

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Journal of Transportation Technologies 2024年第3期14卷 336-357页

作者： Oluwaseun Ibukun Bhuiyan Alam Spatially Integrated social science Program Department of Geography and Planning University of Toledo Toledo United States department of Geography and Planning University of Toledo Toledo United States

This study evaluates the distribution of travel-limiting disabilities across genders and geographic locations in the United States. This study aims to describe and compare the socioeconomic and demographic variables of the people with and without travel-limiting disabilities across geographic locations and gender. The study further evaluates the trip purpose and impact of Covid-19 fourth wave pandemic on the use of public transit and travel to physical workplace for the people with and without travel-limiting disabilities across gender and geographic locations. The study uses the 2022 weighted National Household Travel Survey dataset and employs descriptive statistics. Results reaffirm the findings from previous literature that there are more people with travel-limiting disabilities in urban areas and among women. Over 50 percent of people aged 65 and above have a form of travel-limiting disabilities. The most trip for people with travel-limiting disabilities is made for shopping and medical purposes. Across all categories, rural areas, urban areas, male and female for the people without travel-limiting disabilities, COVID-19 fourth wave did not change the pattern of trips made to physical workplace as pre-COVID-19 era. This pattern is also observable for the people with travel-limiting disabilities in rural and urban areas. Females with travel-limiting disabilities reported making less trips to physical workplaces while male reported doing the same as before COVID-19 era. The study concludes that the quantification of travel-limiting disabilities across geographic location and gender is vital in disability study and could drive policy implementation for improved accessibility for the vulnerable population.

关键词： National Household Travel Survey United States Travel-Limiting Disabilities Descriptive Statistics Covid-19 Disability Study

Optimizing ASReview Simulations:A generic Multiprocessing Solution for“Light-data'and“Heavy-data'Users

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Data Intelligence 2024年第2期6卷 320-343页

作者： Sergei Romanov Abel Soares Siqueira Jonathan de Bruin Jelle Teijema Laura Hofstee Rens van de Schoot Applied Data science Department of Information and Computing ScienceFaculty of ScienceUtrecht University Netherlands escienceCenter AmsterdamNL department of Research and Data Management Services Information Technology ServicesUtrecht UniversityUtrechtthe Netherlands department of Methodology and Statistics Faculty of Social and Behavioral SciencesUtrecht University

Active learning can be used for optimizing and speeding up the screening phase of systematic *** simulation studies mimicking the screening process can be used to test the performance of different machine-learning models or to study the impact of different training *** paper presents an architecture design withamultiprocessing computational strategyforrunningmanysuch simulation studiesinparallel,using the ASReview Makita workflow generator and Kubernetes software for deployment with cloud *** provide a technical explanation of the proposed cloud architecture and its *** addition to that,we conducted 1140 simulations investigating the computational time using various numbers of CPUs and RAM *** analysis demonstrates the degree to which simulations can be accelerated with multiprocessing computing *** parallel computation strategy and the architecture design that was developed in the present paper can contribute to future research with more optimal simulation time and,at the same time,ensure the safe completion of the needed processes.

关键词： Active learning Systematic review Simulation study Multiprocessing Cloud architecture

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Experimental and computational study of annealed nickel sulfide quantum dots for catalytic and antibacterial activity

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Nano Materials science 2024年第3期6卷 355-364页

作者： Muhammad Ikram Sawaira Moeen Ali Haider Anwar Ul-Hamid Haya Alhummiany Hamoud H.Somaily Souraya Goumri-Said Mohammed Benali Kanoun Solar Cell Applications Research Lab Department of PhysicsGovernment College University LahoreLahore54000PunjabPakistan department of Clinical sciences Faculty of Veterinary and Animal SciencesMuhammad Nawaz ShareefUniversity of Agriculture66000MultanPunjabPakistan Core Research Facilities King Fahd University of Petroleum&MineralsDhahran31261Saudi Arabia department of Physics Faculty of ScienceUniversity of JeddahP.O.Box 13151Jeddah21493Saudi Arabia department of Physics Faculty of ScienceKing Khalid UniversityP.O.Box 9004AbhaSaudi Arabia Physics department College of Science and General StudiesAlfaisal UniversityP.O.Box 50927Riyadh11533Saudi Arabia department of Mathematics and sciences College of Humanities and SciencesPrince Sultan UniversityP.O.Box 66833Riyadh11586Saudi Arabia

This research investigates the hydrothermal synthesis and annealing duration effects on nickel sulfide(NiS_(2) quantum dots(QDs)for catalytic decolorization of methylene blue(MB)dye and antimicrobial *** size increased with longer annealing,reducing catalytic ***–vis,XRD,TEM,and FTIR analyses probed optical structural,morphological,and vibrational *** confirmed NiS2's anorthic structure,with crystallite size growing from 6.53 to 7.81 nm during extended ***–Vis exhibited a bathochromic shift,reflecting reduced band gap energy(Eg)in NiS_(2).TEM revealed NiS_(2)QD formation,with agglomerated QD average size increasing from 7.13 to 9.65 nm with prolonged *** NiS_(2) showed significant MB decolorization(89.85%)in acidic *** NiS_(2) QDs demonstrated notable antibacterial activity,yielding a 6.15mm inhibition zone against Escherichia coli(***)compared to ***-principles computation supported a robust interaction between MB and NiS_(2),evidenced by obtained adsorption *** study highlights the nuanced relationship between annealing duration,structural changes,and functional properties in NiS_(2)QDs,emphasizing their potential applications in catalysis and antibacterial interventions.

关键词： NiS_(2) antibacterial quantum dots dye degradation DFT

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Clean air captures attention whereas pollution distracts:evidence from brain activities

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Frontiers of Environmental science & Engineering 2024年第4期18卷 5-15页

作者： Jianxun Yang Yunqi Liu Berry van den Berg Susie Wang Lele Chen Miaomiao Liu Jun Bi State Key Laboratory of Pollution Control and Resource Reuse School of the EnvironmentNanjing UniversityNanjing 210023China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science&TechnologyNanjing 210044China Nanjing Foreign Language School Nanjing 210008China department of Experimental Psychology University of GroningenGrote Kruisstraat 2/19712 TSGroningenThe Netherlands department of social Psychology University of GroningenGrote Kruisstraat 2/19712 TSGroningenThe Netherlands School of Education science Nantong UniversityNantong 226019China

Awareness of the adverse impact of air pollution on attention-related performance such as learning and driving is rapidly ***,there is still little known about the underlying neurocognitive *** an adapted dot-probe task paradigm and event-related potential(ERP)technique,we investigated how visual stimuli of air pollution influence the attentional allocation *** were required to make responses to the onset of a target presented at the left or right visual *** probable location of the target was forewarned by a cue(pollution or clean air images),appearing at either the target location(attention-holding trials)or the opposite location(attention-shifting trials).Behavioral measures showed that when cued by pollution images,subjects had higher response accuracy in attention-shifting *** analysis results revealed that after the cue onset,pollution images evoked lower N300 amplitudes,indicating less attentioncapturing effects of dirty *** the target onset,pollution cues were correlated with the higher P300 amplitudes in attention-holding trials but lower amplitudes in attention-shifting *** indicates that after visual exposure to air pollution,people need more neurocognitive resources to maintain attention but less effort to shift attention *** findings provide the first neuroscientific evidence for the distracting effect of air *** conclude with several practical implications and suggest the ERP technique as a promising tool to understand human responses to environmental stressors.

关键词： Air pollution Attention Disengagement Performance Event-related potential

Addressing inclusion,innovation,and sustainability challenges through the lens of economic geography:Introducing the hierarchical regional innovation system

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