经鼓室口咽鼓管球囊扩张术的疗效及咽鼓管功能的影响

目的探讨经鼓室口咽鼓管球囊扩张(balloon dilatation Eustachian tuboplasty,BET)联合腺样体消融术对慢性分泌性中耳炎(chronic secretory otitis media,CSOM)患儿的疗效及咽鼓管功能的影响。方法选取2020年6月~2022年12月间在中山市小榄人民医院就诊的CSOM患儿82例作为研究对象,采用随机数字表法分为对照组和观察组,每组41例。对照组接受腺样体消融术+鼓膜切开置管术治疗,观察组接受腺样体消融术+鼓膜切开置管术+BET术。比较两组手术相关指标及住院时间、临床症状、咽鼓管功能、临床疗效及随访6个月并发症及复发情况。结果两组术中出血量比较,差异无统计学意义(P>0.05),与对照组相比,观察组手术时间更长,住院时间更短(P<0.05)。与对照组相比,观察组中耳积液消失时间、耳鸣消失时间、鼓膜愈合时间、听力恢复时间均更短(P<0.05)。与治疗前相比,治疗后两组咽鼓管评分(Eustachian tube score,ETS)升高、咽鼓管功能障碍评分量表(Eustachian tube dysfunction questionnaire-7,ETDQ-7)评分降低(P<0.05),观察组ETS评分高于对照组,ETDQ-7评分则低于对照组(P<0.05)。观察组临床总有效率高于对照组,差异具有统计学意义(P<0.05)。两组并发症发生率比较,差异无统计学意义(14.63%vs.9.76%,P>0.05),与对照组相比,观察组复发率更低,差异具有统计学意义(21.95%vs.2.44%,P<0.05)。结论经鼓室口BET联合腺样体消融术有助于提高CSOM患儿的临床疗效,改善咽鼓管功能,促进术后恢复,减少复发率,且并发症发生率低。

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.

A stable implicit nodal integration-based particle finite element method(N-PFEM)for modelling saturated soil dynamics

In this study,we present a novel nodal integration-based particle finite element method(N-PFEM)designed for the dynamic analysis of saturated soils.Our approach incorporates the nodal integration technique into a generalised Hellinger-Reissner(HR)variational principle,creating an implicit PFEM formulation.To mitigate the volumetric locking issue in low-order elements,we employ a node-based strain smoothing technique.By discretising field variables at the centre of smoothing cells,we achieve nodal integration over cells,eliminating the need for sophisticated mapping operations after re-meshing in the PFEM.We express the discretised governing equations as a min-max optimisation problem,which is further reformulated as a standard second-order cone programming(SOCP)problem.Stresses,pore water pressure,and displacements are simultaneously determined using the advanced primal-dual interior point method.Consequently,our numerical model offers improved accuracy for stresses and pore water pressure compared to the displacement-based PFEM formulation.Numerical experiments demonstrate that the N-PFEM efficiently captures both transient and long-term hydro-mechanical behaviour of saturated soils with high accuracy,obviating the need for stabilisation or regularisation techniques commonly employed in other nodal integration-based PFEM approaches.This work holds significant implications for the development of robust and accurate numerical tools for studying saturated soil dynamics.

探究电影教育的发展策略:基于欧盟“媒体计划”的实证分析

电影教育的目标是通过影像形式提升国民认知、审美、创造和社会参与能力。欧盟的目标、政策措施和资金支持体系为电影教育的开展提供了重要保障。在欧盟“媒体计划”的支持下,电影教育的实践覆盖欧洲各国,具有弘扬欧洲优秀文化传统、鼓励跨国合作交流、培养欧洲公民媒体素养的特征。借鉴欧盟的经验,中国电影教育的发展策略可以从加强配套制度设计、进行资源高效整合,以及全面提升国民媒体素养方面寻求进一步突破。

Volumetric lattice Boltzmann method for pore-scale mass diffusionadvection process in geopolymer porous structures

Porous materials present significant advantages for absorbing radioactive isotopes in nuclear waste streams.To improve absorption efficiency in nuclear waste treatment,a thorough understanding of the diffusion-advection process within porous structures is essential for material design.In this study,we present advancements in the volumetric lattice Boltzmann method(VLBM)for modeling and simulating pore-scale diffusion-advection of radioactive isotopes within geopolymer porous structures.These structures are created using the phase field method(PFM)to precisely control pore architectures.In our VLBM approach,we introduce a concentration field of an isotope seamlessly coupled with the velocity field and solve it by the time evolution of its particle population function.To address the computational intensity inherent in the coupled lattice Boltzmann equations for velocity and concentration fields,we implement graphics processing unit(GPU)parallelization.Validation of the developed model involves examining the flow and diffusion fields in porous structures.Remarkably,good agreement is observed for both the velocity field from VLBM and multiphysics object-oriented simulation environment(MOOSE),and the concentration field from VLBM and the finite difference method(FDM).Furthermore,we investigate the effects of background flow,species diffusivity,and porosity on the diffusion-advection behavior by varying the background flow velocity,diffusion coefficient,and pore volume fraction,respectively.Notably,all three parameters exert an influence on the diffusion-advection process.Increased background flow and diffusivity markedly accelerate the process due to increased advection intensity and enhanced diffusion capability,respectively.Conversely,increasing the porosity has a less significant effect,causing a slight slowdown of the diffusion-advection process due to the expanded pore volume.This comprehensive parametric study provides valuable insights into the kinetics of isotope uptake in porous structures,facilitating the development of porous materials for nuclear waste treatment applications.

Navigating nephrotoxic waters:A comprehensive overview of contrast-induced acute kidney injury prevention

Contrast-induced acute kidney injury(CI-AKI)is the third leading cause of acute kidney injury deriving from the intravascular administration of contrast media in diagnostic and therapeutic procedures and leading to longer in-hospital stay and increased short and long-term mortality.Its pathophysiology,although not well-established,revolves around medullary hypoxia paired with the direct toxicity of the substance to the kidney.Critically ill patients,as well as those with pre-existing renal disease and cardiovascular comorbidities,are more susceptible to CI-AKI.Despite the continuous research in the field of CI-AKI prevention,clinical practice is based mostly on periprocedural hydration.In this review,all the investigated methods of prevention are presented,with an emphasis on the latest evidence regarding the potential of RenalGuard and contrast removal systems for CI-AKI prevention in high-risk individuals.

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.

Concise extraction and characterization of the pore-throat network in unconventional hydrocarbon reservoirs: A new perspective

In this study,a new image-based method for the extraction and characterization of pore-throat network for unconventional hydrocarbon storage and exploitation is proposed.“Pore-throat solidity”,which is analogous to particle solidity,and a new method for automatic identification of pores and throats in tight sandstone oil reservoirs are introduced.Additionally,the“pore-throat combination”and“pure pore”are defined and distinguished by drawing the cumulative probability curve of the pore-throat solidity and by selecting an appropriate cutoff point.When the discrete grid set is recognized as a pore-throat combination,Legendre ellipse fitting and minimum Feret diameter are used.When the pore and throat grid sets are identified as pure pores,the pore diameter can be directly calculated.Using the new method,the analytical results for the physical parameters and pore radius agree well with most prior studies.The results comparing the maximum ball and the new model could also prove the accuracy of the latter's in micro and nano scales.The new model provides a more practical theoretical basis and a new calculation method for the rapid and accurate evaluation of the complex processes of oil migration.

Synthesis of waterborne polyurethane-humic acid cross-linked biomass porous materials for the adsorption of methylene blue

A series of adsorbent materials(WPU-HAx-y)with a three-dimensional porous structure,green sustainability,and excellent performance were prepared and evaluated for the removal of methylene blue using nontoxic and environmentally friendly waterborne polyurethane as the matrix material and humic acid,a biomass material,as the functional material.The newly synthesized adsorbents were characterized by infrared spectroscopy,scanning electron microscopy,specific surface area,and thermogravimetric.The effects of contact time(0-8 h),starting concentration(10-100 mg·L^(-1)),pH(3-11),solution temperature(30-60℃),and coexisting ions(Ca2+,Na+,K+,Mg2+)on the performance were investigated.Pseudo-first-order,pseudo-second-order,elovich,and intra-particle diffusion models were used to analyze the adsorption kinetics;the Langmuir,Freundlich,Temkin,and Dubin-Radushkovich adsorption isotherms were evaluated;and the adsorption behavior of the adsorbent materials was found to be more appropriate for the pseudo-second-order model for chemical pollutant removal than the Langmuir model,which depends on monolayer adsorption.WPU-HA2-3 stood out with a maximum adsorption capacity of 813.0081 mg·g^(-1) fitted to the pseudo-second-order and 309.2832 mg·g^(-1) fitted to the Langmuir model,showing superior adsorption performance and regenerability.

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.

Modeling solubility of oxaprozin and irbesartan in biorelevant complex solutions based on a combination of pH-dependent and micellar solubilization models

Biological solubility is one of the important basic parameters in the development process of poorly soluble drugs,but the current measurement methods are mainly based on a large number of experiments,which are time-consuming and cost-intensive.There is still a lack of effective theoretical models to accurately describe and predict the biological solubility of drugs to reduce costs.Therefore,in this study,osaprazole and irbesartan were selected as model drugs,and their solubility in solutions containing surfactants and biorelevant media was measured experimentally.By calculating the parameters of each component using the perturbed-chain statistical associating fluid theory(PC-SAFT)model,combined with pH-dependent and micellar solubilization models,the thermodynamic phase behavior of the two drugs was successfully modeled,and the predicted results were in good agreement with the experimental values.These results demonstrate that the model combination used provides important basic parameters and theoretical guidance for the development and screening of poorly soluble drugs and related formulations.

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.

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.

Chord length sampling correction analysis for dispersion fuel in Monte Carlo simulation

Dispersion fuels,knowned for their excellent safety performance,are widely used in advanced reactors,such as hightemperature gas-cooled reactors.Compared with deterministic methods,the Monte Carlo method has more advantages in the geometric modeling of stochastic media.The explicit modeling method has high computational accuracy and high computational cost.The chord length sampling(CLS)method can improve computational efficiency by sampling the chord length during neutron transport using the matrix chord length?s probability density function.This study shows that the excluded-volume effect in realistic stochastic media can introduce certain deviations into the CLS.A chord length correction approach is proposed to obtain the chord length correction factor by developing the Particle code based on equivalent transmission probability.Through numerical analysis against reference solutions from explicit modeling in the RMC code,it was demonstrated that CLS with the proposed correction method provides good accuracy for addressing the excludedvolume effect in realistic infinite stochastic media.

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 Capillary Fringe Flow:Quasilinear Modeling with Kirchhoff Transforms and Gardner Model

Recent studies have underscored the significance of the capillary fringe in hydrological and biochemical processes.Moreover,its role in shallow waters is expected to be considerable.Traditionally,the study of groundwater flow has centered on unsaturated-saturated zones,often overlooking the impact of the capillary fringe.In this study,we introduce a steady-state two-dimensional model that integrates the capillary fringe into a 2-D numerical solution.Our novel approach employs the potential form of the Richards equation,facilitating the determination of boundaries,pressures,and velocities across different ground surface zones.We utilized a two-dimensional Freefem++finite element model to compute the stationary solution.The validation of the model was conducted using experimental data.We employed the OFAT(One_Factor-At-Time)method to identify the most sensitive soil parameters and understand how changes in these parameters may affect the behavior and water dynamics of the capillary fringe.The results emphasize the role of hydraulic conductivity as a key parameter influencing capillary fringe shape and dynamics.Velocity values within the capillary fringe suggest the prevalence of horizontal flow.By variation of the water table level and the incoming flow q0,we have shown the correlation between water table elevation and the upper limit of the capillary fringe.

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.

Modeling Method of C/C-ZrC Composites and Prediction of Equivalent Thermal Conductivity Tensor Based on Asymptotic Homogenization

This article proposes a modeling method for C/C-ZrC composite materials.According to the superposition of Gaussian random field,the original gray model is obtained,and the threshold segmentation method is used to generate the C-ZrC inclusion model.Finally,the fiber structure is added to construct the microstructure of the three-phase plain weave composite.The reconstructed inclusions can meet the randomness of the shape and have a uniform distribution.Using an algorithm based on asymptotic homogenization and finite element method,the equivalent thermal conductivity prediction of the microstructure finite element model was carried out,and the influence of component volume fraction on material thermal properties was explored.The sensitivity of model parameters was studied,including the size,mesh sensitivity,Gaussian complexity,and correlation length of the RVE model,and the optimal calculation model was selected.The results indicate that the volume fraction of the inclusion phase has a significant impact on the equivalent thermal conductivity of the material.As the volume fraction of carbon fiber and ZrC increases,the equivalent thermal conductivity tensor gradually decreases.This model can be used to explore the impact of materialmicrostructure on the results,and numerical simulations have studied the relationship between structure and performance,providing the possibility of designing microstructure based on performance.