The impact of heterogeneity and pore network characteristics on single and multi-phase fluid propagation in complex porous media:An X-ray computed tomography study

This study investigates the impact of pore network characteristics on fluid flow through complex and heterogeneous porous media,providing insights into the factors affecting fluid propagation in such systems.Specifically,high-resolution or micro X-ray computed tomography(CT)imaging techniques were utilized to examine outcrop stromatolite samples of the Lagoa Salgada,considered flow analogous to the Brazilian Pre-salt carbonate reservoirs.The petrophysical results comprised two distinct stromatolite depositional facies,the columnar and the fine-grained facies.By generating pore network model(PNM),the study quantified the relationship between key features of the porous system,including pore and throat radius,throat length,coordination number,shape factor,and pore volume.The study found that the less dense pore network of the columnar sample is typically characterized by larger pores and wider and longer throats but with a weaker connection of throats to pores.Both facies exhibited less variability in the radius of the pores and throats in comparison to throat length.Additionally,a series of core flooding experiments coupled with medical CT scanning was designed and conducted in the plug samples to assess flow propagation and saturation fields.The study revealed that the heterogeneity and presence of disconnected or dead-end pores significantly impacted the flow patterns and saturation.Two-phase flow patterns and oil saturation distribution reveal a preferential and heterogeneous displacement that mainly swept displaced fluid in some regions of plugs and bypassed it in others.The relation between saturation profiles,porosity profiles,and the number of fluid flow patterns for the samples was evident.Only for the columnar plug sample was the enhancement in recovery factor after shifting to lower salinity water injection(SB)observed.

Multi-modal knowledge graph inference via media convergence and logic rule

Media convergence works by processing information from different modalities and applying them to different domains.It is difficult for the conventional knowledge graph to utilise multi-media features because the introduction of a large amount of information from other modalities reduces the effectiveness of representation learning and makes knowledge graph inference less effective.To address the issue,an inference method based on Media Convergence and Rule-guided Joint Inference model(MCRJI)has been pro-posed.The authors not only converge multi-media features of entities but also introduce logic rules to improve the accuracy and interpretability of link prediction.First,a multi-headed self-attention approach is used to obtain the attention of different media features of entities during semantic synthesis.Second,logic rules of different lengths are mined from knowledge graph to learn new entity representations.Finally,knowledge graph inference is performed based on representing entities that converge multi-media features.Numerous experimental results show that MCRJI outperforms other advanced baselines in using multi-media features and knowledge graph inference,demonstrating that MCRJI provides an excellent approach for knowledge graph inference with converged multi-media features.

A multi-modal clustering method for traditonal Chinese medicine clinical data via media convergence

Media convergence is a media change led by technological innovation.Applying media convergence technology to the study of clustering in Chinese medicine can significantly exploit the advantages of media fusion.Obtaining consistent and complementary information among multiple modalities through media convergence can provide technical support for clustering.This article presents an approach based on Media Convergence and Graph convolution Encoder Clustering(MCGEC)for traditonal Chinese medicine(TCM)clinical data.It feeds modal information and graph structure from media information into a multi-modal graph convolution encoder to obtain the media feature representation learnt from multiple modalities.MCGEC captures latent information from various modalities by fusion and optimises the feature representations and network architecture with learnt clustering labels.The experiment is conducted on real-world multimodal TCM clinical data,including information like images and text.MCGEC has improved clustering results compared to the generic single-modal clustering methods and the current more advanced multi-modal clustering methods.MCGEC applied to TCM clinical datasets can achieve better results.Integrating multimedia features into clustering algorithms offers significant benefits compared to single-modal clustering approaches that simply concatenate features from different modalities.It provides practical technical support for multi-modal clustering in the TCM field incorporating multimedia features.

An inverse analysis of fluid flow through granular media using differentiable lattice Boltzmann method

This study presents a method for the inverse analysis of fluid flow problems.The focus is put on accurately determining boundary conditions and characterizing the physical properties of granular media,such as permeability,and fluid components,like viscosity.The primary aim is to deduce either constant pressure head or pressure profiles,given the known velocity field at a steady-state flow through a conduit containing obstacles,including walls,spheres,and grains.The lattice Boltzmann method(LBM)combined with automatic differentiation(AD)(AD-LBM)is employed,with the help of the GPU-capable Taichi programming language.A lightweight tape is used to generate gradients for the entire LBM simulation,enabling end-to-end backpropagation.Our AD-LBM approach accurately estimates the boundary conditions for complex flow paths in porous media,leading to observed steady-state velocity fields and deriving macro-scale permeability and fluid viscosity.The method demonstrates significant advantages in terms of prediction accuracy and computational efficiency,making it a powerful tool for solving inverse fluid flow problems in various applications.

Data inversion of multi-dimensional magnetic resonance in porous media

Since its inception in the 1970s,multi-dimensional magnetic resonance(MR)has emerged as a powerful tool for non-invasive investigations of structures and molecular interactions.MR spectroscopy beyond one dimension allows the study of the correlation,exchange processes,and separation of overlapping spectral information.The multi-dimensional concept has been re-implemented over the last two decades to explore molecular motion and spin dynamics in porous media.Apart from Fourier transform,methods have been developed for processing the multi-dimensional time-domain data,identifying the fluid components,and estimating pore surface permeability via joint relaxation and diffusion spectra.Through the resolution of spectroscopic signals with spatial encoding gradients,multi-dimensional MR imaging has been widely used to investigate the microscopic environment of living tissues and distinguish diseases.Signals in each voxel are usually expressed as multi-exponential decay,representing microstructures or environments along multiple pore scales.The separation of contributions from different environments is a common ill-posed problem,which can be resolved numerically.Moreover,the inversion methods and experimental parameters determine the resolution of multi-dimensional spectra.This paper reviews the algorithms that have been proposed to process multidimensional MR datasets in different scenarios.Detailed information at the microscopic level,such as tissue components,fluid types and food structures in multi-disciplinary sciences,could be revealed through multi-dimensional MR.

Making the case for the evolving role of social media in health professions education and a literature review on the application of some in ophthalmology

Background and Objective:Social media(SoMe)has emerged as a tool in health professions education(HPE),particularly amidst the challenges posed by the coronavirus disease 2019(COVID-19)pandemic.Despite the academia’s initial skepticism SoMe has been gaining traction in supporting learning communities,and offering opportunities for innovation in HPE.Our study aims to explore the integration of SoMe in HPE.Four key components were outlined as necessary for a successful integration,and include designing learning experiences,defining educator roles,selecting appropriate platforms,and establishing educational objectives.Methods:This article stemmed from the online Teaching Skills Series module on SoMe in education from the Ophthalmology Foundation,and drew upon evidence supporting learning theories relevant to SoMe integration and models of education.Additionally,we conducted a literature review considering Englishlanguage articles on the application of SoMe in ophthalmology from PubMed over the past decade.Key Content and Findings:Early adopters of SoMe platforms in HPE have leveraged these tools to enhance learning experiences through interaction,dialogue,content sharing,and active learning strategies.By integrating SoMe into educational programs,both online and in-person,educators can overcome time and geographical constraints,fostering more diverse and inclusive learning communities.Careful consideration is,however,necessary to address potential limitations within HPE.Conclusions:This article lays groundwork for expanding SoMe integration in HPE design,emphasizing the supportive scaffold of various learning theories,and the need of furthering robust research on examining its advantages over traditional educational formats.Our literature review underscores an ongoing multifaceted,random application of SoMe platforms in ophthalmology education.We advocate for an effective incorporation of SoMe in HPE education,with the need to comply with good educational practice.

Learning-based real-time imaging through dynamic scattering media

Imaging through dynamic scattering media is one of the most challenging yet fascinating problems in optics,with applications spanning from biological detection to remote sensing.In this study,we propose a comprehensive learning-based technique that facilitates real-time,non-invasive,incoherent imaging of real-world objects through dense and dynamic scattering media.We conduct extensive experiments,demonstrating the capability of our technique to see through turbid water and natural fog.The experimental results indicate that the proposed technique surpasses existing approaches in numerous aspects and holds significant potential for imaging applications across a broad spectrum of disciplines.

Retraction:YEATS domain containing 4 promotes gastric cancer cell proliferation and mediates tumor progression via activating the Wnt/β-Catenin signaling pathway

Following the publication,concerns have been raised about a number of figures in this article.An unexpected area of similarity was identified in terms of the cellular data,where the results from differently performed experiments were intended to have been shown,although the areas immediately surrounding this area featured comparatively different distributions of cells.In addition,the western blots in this article were presented with atypical,unusually shaped and possibly anomalous protein bands in many cases.

The Impact of Social Media on Dietary Behaviors and Body Image of College Students: A Qualitative Approach

Background: The use of social media platforms for health and nutrition information has become popular among college students. Although social media made information readily accessible in different formats, nutritional misinformation promoted by influencers and non-experts caused negative impact on diet behavior and perception of body image. Previous research indicated that extensive use of social media was positively linked to disordered eating behaviors. Social media platforms like Facebook and Instagram that allow users to follow celebrities intensified exposure to influencers’ messages and images and resulted in negative moods and body dissatisfaction. Objective: This paper aims to explore the impact of social media on college students’ dietary behaviors and body image. Participants: 18 undergraduate students from a public university in the Southern United States were recruited through a mass email. Methods: A cross-sectional qualitative study of three focus groups was conducted. The focus groups were based on guiding open-ended questions. Atlas.ti was used to code and analyze the data using inductive and deductive codes. Results: Three main themes were identified. The conditions theme included elements that explain why and how social media influences the participants’ actions. The actions theme included eating behavior, physical activity, and dietary supplement intake. The consequences theme describes anticipated or actual outcomes of actions such as body image and ideal weight. Conclusions: Social media has had a negative influence on diet behaviors and a positive influence on physical activity. Evidence-based nutrition and weight management information is needed to thwart potential misinformation.

Potential use of large language models for mitigating students’problematic social media use:ChatGPT as an example

The problematic use of social media has numerous negative impacts on individuals'daily lives,interpersonal relationships,physical and mental health,and more.Currently,there are few methods and tools to alleviate problematic social media,and their potential is yet to be fully realized.Emerging large language models(LLMs)are becoming increasingly popular for providing information and assistance to people and are being applied in many aspects of life.In mitigating problematic social media use,LLMs such as ChatGPT can play a positive role by serving as conversational partners and outlets for users,providing personalized information and resources,monitoring and intervening in problematic social media use,and more.In this process,we should recognize both the enormous potential and endless possibilities of LLMs such as ChatGPT,leveraging their advantages to better address problematic social media use,while also acknowledging the limitations and potential pitfalls of ChatGPT technology,such as errors,limitations in issue resolution,privacy and security concerns,and potential overreliance.When we leverage the advantages of LLMs to address issues in social media usage,we must adopt a cautious and ethical approach,being vigilant of the potential adverse effects that LLMs may have in addressing problematic social media use to better harness technology to serve individuals and society.

Associations between Social Media Use and Sleep Quality in China:Exploring the Mediating Role of Social Media Addiction

Sleep quality is closely linked to people’s health,and during the COVID-19 pandemic,the sleep patterns of residents in China were notably poor.The lockdown in China led to an increase in social media use,prompting questions about its impact on sleep.Therefore,this study investigates the association between social media use and sleep quality among Chinese residents during the COVID-19 outbreak,highlighting the potential mediating role of social media addiction.Data were collected via questionnaires through a cross-sectional survey with 779 valid responses.Variance analysis was used to test for differences in social media use among different demographic variables.Bivariate correlation analysis was employed to explore the relationships between variables,while regression analysis investigated the correlations between various media factors and sleep quality.Additionally,Bootstrap sampling was utilized to analyze the potential mediating influence of social media addiction in the relationship between social media use and sleep.The study's findings reveal a significant correlation between social media use,particularly before bedtime,and sleep quality(p<0.01),with pre-sleep activity notably linked to poorer overall sleep scores(β=0.141,p=0.004).Although the daily use of social media did not directly impact most individuals’sleep quality,specific platforms like news apps,short video apps,dating apps,and content community platforms were associated with higher levels of social media addiction,subsequently negatively affecting sleep quality.Specifically,the use of news apps(B=0.068,95%CI[0.000,0.019]),short video apps(B=0.112,95%CI[0.001,0.031]),dating apps(B=0.147,95%CI[0.000,0.028]),and content community platforms(B=0.106,95%CI[0.001,0.028])was found to increase the risk of social media addiction,subsequently leading to adverse effects on sleep quality.The study underscores a notable link between social media use and sleep quality,suggesting that mindful social media habits,particularly before bedtime,and reducing addiction-associated apps could enhance sleep quality.

A News Media Bias and Factuality Profiling Framework Assisted by Modeling Correlation

News media profiling is helpful in preventing the spread of fake news at the source and maintaining a good media and news ecosystem.Most previous works only extract features and evaluate media from one dimension independently,ignoring the interconnections between different aspects.This paper proposes a novel news media bias and factuality profiling framework assisted by correlated features.This framework models the relationship and interaction between media bias and factuality,utilizing this relationship to assist in the prediction of profiling results.Our approach extracts features independently while aligning and fusing them through recursive convolu-tion and attention mechanisms,thus harnessing multi-scale interactive information across different dimensions and levels.This method improves the effectiveness of news media evaluation.Experimental results indicate that our proposed framework significantly outperforms existing methods,achieving the best performance in Accuracy and F1 score,improving by at least 1%compared to other methods.This paper further analyzes and discusses based on the experimental results.

Porous-DeepONet:Learning the Solution Operators of Parametric Reactive Transport Equations in Porous Media

Reactive transport equations in porous media are critical in various scientific and engineering disciplines,but solving these equations can be computationally expensive when exploring different scenarios,such as varying porous structures and initial or boundary conditions.The deep operator network(DeepONet)has emerged as a popular deep learning framework for solving parametric partial differential equations.However,applying the DeepONet to porous media presents significant challenges due to its limited capability to extract representative features from intricate structures.To address this issue,we propose the Porous-DeepONet,a simple yet highly effective extension of the DeepONet framework that leverages convolutional neural networks(CNNs)to learn the solution operators of parametric reactive transport equations in porous media.By incorporating CNNs,we can effectively capture the intricate features of porous media,enabling accurate and efficient learning of the solution operators.We demonstrate the effectiveness of the Porous-DeepONet in accurately and rapidly learning the solution operators of parametric reactive transport equations with various boundary conditions,multiple phases,and multiphysical fields through five examples.This approach offers significant computational savings,potentially reducing the computation time by 50–1000 times compared with the finite-element method.Our work may provide a robust alternative for solving parametric reactive transport equations in porous media,paving the way for exploring complex phenomena in porous media.

Selection of media tools for community planning:A study from the perspective of communicative planning

In community planning,due to the lack of evidence regarding the selection of media tools,this study examines how a common but differentiated ideal speech situation can be created as well as how more appropriate media tools can be defined and selected in the community planning process.First,this study describes the concept and theoretical basis of media used in community planning from the perspectives of the multiple effects of media evolution on communicative planning.Second,the classification criteria and typical characteristics of media tools used to support community planning are clarified from three dimensions:acceptability,cost effectiveness,and applicability.Third,strategies for applying media tools in the four phases of communicative planning-namely,state analysis,problem identification,contradictory solution and optimization-are described.Finally,trends in the development of media tools for community planning are explored in terms of multistakeholder engagement,supporting scientific decision-making and multiple-type media integration.The results provide a reference for developing more inclusive,effective,and appropriate media tools for enhancing decision-making capacity and modernizing governance in community planning and policy-making processes.

Analyzing topics in social media for improving digital twinning based product development

Digital twinning enables manufacturers to create digital representations of physical entities,thus implementing virtual simulations for product development.Previous efforts of digital twinning neglect the decisive consumer feedback in product development stages,failing to cover the gap between physical and digital spaces.This work mines real-world consumer feedbacks through social media topics,which is significant to product development.We specifically analyze the prevalent time of a product topic,giving an insight into both consumer attention and the widely-discussed time of a product.The primary body of current studies regards the prevalent time prediction as an accompanying task or assumes the existence of a preset distribution.Therefore,these proposed solutions are either biased in focused objectives and underlying patterns or weak in the capability of generalization towards diverse topics.To this end,this work combines deep learning and survival analysis to predict the prevalent time of topics.We propose a specialized deep survival model which consists of two modules.The first module enriches input covariates by incorporating latent features of the time-varying text,and the second module fully captures the temporal pattern of a rumor by a recurrent network structure.Moreover,a specific loss function different from regular survival models is proposed to achieve a more reasonable prediction.Extensive experiments on real-world datasets demonstrate that our model significantly outperforms the state-of-the-art methods.

Social Media-Based Surveillance Systems for Health Informatics Using Machine and Deep Learning Techniques:A Comprehensive Review and Open Challenges

Social media(SM)based surveillance systems,combined with machine learning(ML)and deep learning(DL)techniques,have shown potential for early detection of epidemic outbreaks.This review discusses the current state of SM-based surveillance methods for early epidemic outbreaks and the role of ML and DL in enhancing their performance.Since,every year,a large amount of data related to epidemic outbreaks,particularly Twitter data is generated by SM.This paper outlines the theme of SM analysis for tracking health-related issues and detecting epidemic outbreaks in SM,along with the ML and DL techniques that have been configured for the detection of epidemic outbreaks.DL has emerged as a promising ML technique that adaptsmultiple layers of representations or features of the data and yields state-of-the-art extrapolation results.In recent years,along with the success of ML and DL in many other application domains,both ML and DL are also popularly used in SM analysis.This paper aims to provide an overview of epidemic outbreaks in SM and then outlines a comprehensive analysis of ML and DL approaches and their existing applications in SM analysis.Finally,this review serves the purpose of offering suggestions,ideas,and proposals,along with highlighting the ongoing challenges in the field of early outbreak detection that still need to be addressed.

Integrating Neighborhood Geographic Distribution and Social Structure Influence for Social Media User Geolocation

Geolocating social media users aims to discover the real geographical locations of users from their publicly available data,which can support online location-based applications such as disaster alerts and local content recommen-dations.Social relationship-based methods represent a classical approach for geolocating social media.However,geographically proximate relationships are sparse and challenging to discern within social networks,thereby affecting the accuracy of user geolocation.To address this challenge,we propose user geolocation methods that integrate neighborhood geographical distribution and social structure influence(NGSI)to improve geolocation accuracy.Firstly,we propose a method for evaluating the homophily of locations based on the k-order neighbor-hood geographic distribution(k-NGD)similarity among users.There are notable differences in the distribution of k-NGD similarity between location-proximate and non-location-proximate users.Exploiting this distinction,we filter out non-location-proximate social relationships to enhance location homophily in the social network.To better utilize the location-proximate relationships in social networks,we propose a graph neural network algorithm based on the social structure influence.The algorithm enables us to perform a weighted aggregation of the information of users’multi-hop neighborhood,thereby mitigating the over-smoothing problem of user features and improving user geolocation performance.Experimental results on real social media dataset demonstrate that the neighborhood geographical distribution similarity metric can effectively filter out non-location-proximate social relationships.Moreover,compared with 7 existing social relationship-based user positioning methods,our proposed method can achieve multi-granularity user geolocation and improve the accuracy by 4.84%to 13.28%.

Acute Otitis Media in Children Aged 0-5 Years, Epidemiological Aspects and Management in the Paediatrics Department of the Hospital National Ignace Deen (Conakry)

Introduction: Acute otitis media is an acute inflammation of the mucosa of the middle ear cavities. It is often secondary to nasopharyngitis, which favors the passage of infection through the Eustachian tube to the middle ear. The aim of our study was to improve the management of AOM in the Paediatric Department of the Hospital National Ignace Deen (Conakry). Patients and Methods: This was a prospective descriptive study lasting 6 months from 01 July to 31 December 2011;the study covered 525 cases out of a total of 6276 children, i.e. a frequency of 8.36%. Results: The most affected age group was 6 to 11 months. Males predominated (69.71%). 82.29% had a history of recurrent rhinopharyngitis. The most frequent reason for consultation was incessant crying (66.29%). Rhinopharyngitis and malaria were the most commonly associated pathologies (87.62% and 39.62% respectively). 72.19% of our patients were admitted with congestive AOM and received medical treatment. We recorded one case of otomastoiditis which was treated surgically. Conclusion: AOM is more common in children aged between 6 and 24 months. Good collaboration between paediatricians and ENT specialists is essential to reduce the morbidity of AOM.

A study on the temperature sensitivity of NMR porosity in porous media based on the intensity of magnetization Dedicated to the special issue “Magnetic Resonance in Porous Media”

The measurement of nuclear magnetic resonance(NMR)porosity is affected by temperature.Without considering the impact of NMR logging tools,this phenomenon is mainly caused by variations in magnetization intensity of the measured system due to temperature fluctuations and difference between the temperature of the porous medium and calibration sample.In this study,the effect of temperature was explained based on the thermodynamic theory,and the rules of NMR porosity responses to temperature changes were identified through core physics experiments.In addition,a method for correcting the influence of temperature on NMR porosity measurement was proposed,and the possible factors that may affect its application were also discussed.

Validity,Reliability,and Measurement Invariance of the Thai Smartphone Application-Based Addiction Scale and Bergen Social Media Addiction Scale

Background:In recent years,there has been increased research interest in both smartphone addiction and social media addiction as well as the development of psychometric instruments to assess these constructs.However,there is a lack of psychometric evaluation for instruments assessing smartphone addiction and social media addiction in Thailand.The present study evaluated the psychometric properties and gender measurement invariance of the Thai version of the Smartphone Application-Based Addiction Scale(SABAS)and Bergen Social Media Addiction Scale(BSMAS).Method:A total of 801 Thai university students participated in an online survey from January 2022 to July 2022 which included demographic information,SABAS,BSMAS,and the Internet Gaming Disorder Scale-Short Form(IGDS9-SF).Results:Confirmatory Factor Analyses(CFAs)found that both the SABAS and BSMAS had a one-factor structure.Findings demonstrated adequate psychometric properties of both instruments and also supported measurement invariance across genders.Moreover,scores on the SABAS and BSMAS were correlated with scores on the IGDS9-SF.Conclusion:The results indicated that the SABAS and BSMAS are useful psychometric instruments for assessing the risk of smartphone addiction and social media addiction among Thai young adults.