The Media Logic and Cultural Impact of WeChat

Through its media logic, which is defined by programmability, pervasiveness, connectivity, and datafication, WeChat establishes a non-linear, interactive, and user-dominated media environment. This media logic enhances the influence of each user within WeChat’s narrative landscape and revolutionises traditional media narration methods by enabling them to generate and distribute content. In such an environment, users are able to edit, receive, and send information without constraints of time or distance, which enables delayed interactions that broaden the potential for human communication and dissemination. Additionally, WeChat partially replaces social behaviours and institutions, thereby modifying their original structures and characteristics. As individuals become more accustomed to this media environment, they progressively adjust to the forms that are appropriate for media representation on WeChat, thereby completing the mediatization of culture and society. In this process, individuals are becoming more dependent on media and media logic, with WeChat’s influence permeating social and cultural activities through its media logic. This interaction has a substantial impact on society and culture, thereby aiding in their reconstruction.

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.

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.

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.

Exploring the Reasons for Changes in National Reading Behaviour from the Perspective of Media Environmentalism

In this paper, through the perspective of Merowitz’s media environmentalism, following the research path of media-scene-behaviour, and based on the results of the national reading survey released by the China Press and Publication Research Institute in the past ten years (2013-2022), we analyse the characteristics of national reading behaviours, explore the reasons for the changes in national reading behaviour in terms of technology, media, and scenarios. It also discusses the future trend of national reading behaviour and the promotion of national reading activities, and puts forward certain countermeasures from the government, schools and the society to make joint efforts to promote national reading.

Media Sporting Events as Platforms for‘Soft Power’and Digital Activism

Mediatic sporting events are increasingly used in the geopolitical arena to gain consensus, at the same time emerging as platforms for the discussion of social issues, such as human rights and sustainability. This article presents an investigation into the role of mediatic sporting events and digital platforms in sparking political debate and social engagement to bring about change, including discussions on global and local interests and the performances of activist fans to promote debate on specific issues. The investigation employs a quantitative methodology. The initial inductive analysis uses a matrix of cultural key-frames of the empowerment and protest that accompany media sporting events, verifying their presence and effectiveness in different communication milieus. An analysis of the representation of protests on social media was performed through content analysis. The results of the investigation show that mediatic sporting events are to be understood as a diverse range of generators of multiple political effects: from the exercise of power to mass mobilization and media criticism.

Exploring International Media and Information Literacy Initiatives: Insights From DW Akademie’s MIL Model

Media and Information Literacy(MIL)is one of the most important topics in today’s mediatized world.Under the leadership of United Nations Educational,Scientific and Cultural Organization(UNESCO),many international organizations in the world,as foreign donors,annually announce many projects and grants for the promotion and development of the field of MIL in the countries of the world.One of the main actors of this movement is DW Akademie with different media and MIL projects several countries of the world.This research paper delves into the role of DW Akademie’s MIL model in shaping a media-savvy generation.The study explores the theoretical underpinnings and practical applications of Deutsche Welle(DW)Akademie’s MIL model,analysing its effectiveness in fostering media literacy skills.The research employs a multi-faceted approach,incorporating case studies to assess the model’s impact across diverse demographics.The paper also considers the model’s alignment with global educational policies and proposes recommendations for its integration into broader frameworks.By investigating DW Akademie’s MIL model,this research contributes to the ongoing discourse on media literacy education,providing valuable insights for educators,policymakers,and researchers.The findings offer a nuanced understanding of the model’s position in cultivating a media-savvy generation poised to navigate the complexities of the information age.

Hierarchical Media Ethics in the Digital Age: An Analysis of the “Primary School Student Lost Homework in Paris” Incident

In the digital age,traditional media ethics norms lack sufficient explanatory power and binding force in practice.This paper uses the“Primary School Student Lost Homework in Paris”incident as a case study to explore the ethical controversies surrounding the dissemination of this event.By adopting a hierarchical media ethics perspective,the paper dynamically examines the ethical conduct of various communicators,elucidates the ethical responsibilities of each party involved,and reveals the complexity of communication ethics in the digital age.

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.

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.

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.

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.

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.

Exploring impacts of COVID-19 on spatial and temporal patterns of visitors to Canadian Rocky Mountain National Parks from social media big data

COVID-19 posed challenges for global tourism management.Changes in visitor temporal and spatial patterns and their associated determinants pre-and peri-pandemic in Canadian Rocky Mountain National Parks are analyzed.Data was collected through social media programming and analyzed using spatiotemporal analysis and a geographically weighted regression(GWR)model.Results highlight that COVID-19 significantly changed park visitation patterns.Visitors tended to explore more remote areas peri-pandemic.The GWR model also indicated distance to nearby trails was a significant influence on visitor density.Our results indicate that the pandemic influenced tourism temporal and spatial imbalance.This research presents a novel approach using combined social media big data which can be extended to the field of tourism management,and has important implications to manage visitor patterns and to allocate resources efficiently to satisfy multiple objectives of park management.

Detecting the distribution of and public attitudes to charismatic alien species based on media coverage

Charismatic species are often reported by the media,providing information for detecting population status and public perception.To identify the number and distribution of free-living Black Swan(Cygnus atratus),a charismatic alien species in Chinese mainland and to detect the public and the media attitudes to the species,we analyzed the reports and emotional tendency from media coverage in 2000-2022 using manual reading,crawler extraction and latent Dirichlet allocation.A total of 6654 Black Swans were reported at 711 sites,including 147 individuals at 30 nature reserves.Successful breeding was reported at one-fourth of the total sites,including five nature reserves.The proportion of positive emotional tendency to Black Swans was overwhelming in the reports and was higher than that to alien species in general,suggesting that the public and the media are unaware of the risk of biological invasion.Effective management of invasive species requires the media clarifies the invasion risk of charismatic alien species.Promoting the unity between the harmfulness of abstract concept of alien species and the charisma of a specific alien species among the public help effective management.

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.

Co-infection with Neisseria mucosa in a patient with tuberculous otitis media

Tuberculous otitis media(TOM) is a rare manifestation caused by Mycobacterium tuberculosis with low incidence rates among extrapulmonary tuberculosis cases. Diagnosis is often delayed because of the presence of several clinical manifestations and the high prevalence of secondary bacterial infections. Few reports have attributed secondary bacterial infections in patients with TOM to commensal Neisseria. Thus, understanding the pathogenic mechanisms and clinical features of commensal Neisseria is important, considering its recent presentation as an infection-causing pathogen. Neisseria mucosa is a commensal inhabitant in humans and is generally considered non-pathogenic but can cause infection in rare cases. Here, we report an atypical secondary infection caused by Neisseria mucosa in an 81-year-old woman with TOM being treated for pulmonary tuberculosis. Direct purulent otorrhea smear microscopy revealed no acid-fast bacilli using Ziehl-Neelsen staining, whereas the phagocytosis of gram-negative cocci by white blood cells was confirmed using Gram staining. Otorrhea culture revealed the growth of N. mucosa. Subsequently, M. tuberculosis infection in the otorrhea was identified using a culture-based method. Vigilance is critical for the early detection of TOM to prevent further complications. This report raises awareness regarding TOM and provides insight into the pathogenicity of N. mucosa in otitis media.

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%.

Effects of confining pressure and pore pressure on multipole borehole acoustic field in fluid-saturated porous media

In-situ stress is a common stress in the exploration and development of oil reservoirs. Therefore, it is of great significance to study the propagation characteristics of borehole acoustic waves in fluid-saturated porous media under stress.Based on the acoustoelastic theory of fluid-saturated porous media, the field equation of fluid-saturated porous media under the conditions of confining pressure and pore pressure and the acoustic field formula of multipole source excitation in open hole are given. The influences of pore pressure and confining pressure on guided waves of multipole borehole acoustic field in fluid-saturated porous media are investigated. The numerical results show that the phase velocity and excitation intensity of guided wave increase significantly under the confining pressure. For a given confining pressure, the phase velocity of the guided wave decreases with pore pressure increasing. The excitation intensity of guided wave increases at low frequency and then decreases at high frequency with pore pressure increasing, except for that of Stoneley wave which decreases in the whole frequency range. These results will help us get an insight into the influences of confining pressure and pore pressure on the acoustic field of multipole source in borehole around fluid-saturated porous media.

Anisotropic dynamic permeability model for porous media

Based on the tortuous capillary network model,the relationship between anisotropic permeability and rock normal strain,namely the anisotropic dynamic permeability model(ADPM),was derived and established.The model was verified using pore-scale flow simulation.The uniaxial strain process was calculated and the main factors affecting permeability changes in different directions in the deformation process were analyzed.In the process of uniaxial strain during the exploitation of layered oil and gas reservoirs,the effect of effective surface porosity on the permeability in all directions is consistent.With the decrease of effective surface porosity,the sensitivity of permeability to strain increases.The sensitivity of the permeability perpendicular to the direction of compression to the strain decreases with the increase of the tortuosity,while the sensitivity of the permeability in the direction of compression to the strain increases with the increase of the tortuosity.For layered reservoirs with the same initial tortuosity in all directions,the tortuosity plays a decisive role in the relative relationship between the variations of permeability in all directions during pressure drop.When the tortuosity is less than 1.6,the decrease rate of horizontal permeability is higher than that of vertical permeability,while the opposite is true when the tortuosity is greater than 1.6.This phenomenon cannot be represented by traditional dynamic permeability model.After the verification by experimental data of pore-scale simulation,the new model has high fitting accuracy and can effectively characterize the effects of deformation in different directions on the permeability in all directions.