Jet formation and penetration performance of a double-layer charge liner with chemically-deposited tungsten as the inner liner

This paper proposes a type of double-layer charge liner fabricated using chemical vapor deposition(CVD)that has tungsten as its inner liner.The feasibility of this design was evaluated through penetration tests.Double-layer charge liners were fabricated by using CVD to deposit tungsten layers on the inner surfaces of pure T2 copper liners.The microstructures of the tungsten layers were analyzed using a scanning electron microscope(SEM).The feasibility analysis was carried out by pulsed X-rays,slug-retrieval test and static penetration tests.The shaped charge jet forming and penetration law of inner tungsten-coated double-layer liner were studied by numerical simulation method.The results showed that the double-layer liners could form well-shaped jets.The errors between the X-ray test results and the numerical results were within 11.07%.A slug-retrieval test was found that the retrieved slug was similar to a numerically simulated slug.Compared with the traditional pure copper shaped charge jet,the penetration depth of the double-layer shaped charge liner increased by 11.4% and>10.8% respectively.In summary,the test results are good,and the numerical simulation is in good agreement with the test,which verified the feasibility of using the CVD method to fabricate double-layer charge liners with a high-density and high-strength refractory metal as the inner liner.

Discovery and Significance of the Triassic–Late Paleozoic Double-layered Basement in the Songliao Basin:Based on the Complete Coring Data from ICDP Borehole SK2

The Songliao Basin(SLB)covers an area of approximately 260,000 km2in northeastern Asia and preserves a continuous and complete Cretaceous terrestrial record(Wang et al.,2021).The region is the most important petroliferous sedimentary basin in China because of its continual annual oil and gas equivalent production of tens of millions of tons(ca.220–440 million barrels per year)since 1959.The SLB was previously thought to have developed on Hercynian basement and accumulated continuous sedimentary deposits during the Late Jurassic and Cretaceous(Wan et al.,2013;Wang et al.,2016).

Performance and Application of Double-layered Microcapsule Corrosion Inhibitors

Double-layered microcapsule corrosion inhibitors were developed by sodium monofluorophosphate as the core material,polymethyl methacrylate as the inner wall material,and polyvinyl alcohol as the outer wall material combining the solvent evaporation method and spray drying method.The protection by the outer capsule wall was used to prolong the service life of the corrosion inhibitor.The dispersion,encapsulation,thermal stability of microcapsules,and the degradation rate of capsule wall in concrete pore solution were analyzed by ultra-deep field microscopy,scanning electron microscopy,thermal analyzer,and sodium ion release rate analysis.The microcapsules were incorporated into mortar samples containing steel reinforcement,and the effects of double-layered microcapsule corrosion inhibitors on the performance of the cement matrix and the actual corrosion-inhibiting effect were analyzed.The experimental results show that the double-layered microcapsules have a moderate particle size and uniform distribution,and the capsules were completely wrapped.The microcapsules as a whole have good thermal stability below 230 ℃.The monolayer membrane structure microcapsules completely broke within 1 day in the simulated concrete pore solution,and the double-layer membrane structure prolonged the service life of the microcapsules to 80 days in the simulated concrete pore solution before the core material was completely released.The mortar samples containing steel reinforcement incorporated with the double-layered microcapsule corrosion inhibitors still maintained a higher corrosion potential than the monolayer microcapsule corrosion inhibitors control group at 60 days.The incorporation of double-layered microcapsules into the cement matrix has no significant adverse effect on the setting time and early strength.

Time-sequenced damage behavior of reactive projectile impacting double-layer plates

The time-sequenced damage behavior of the reactive projectile impacting double-layer plates is discussed.The analytical model considering the combined effect of kinetic and chemical energy is developed to reveal the damage mechanism.The influences of impact velocity and reactive projectile chemical characteristics on the damage effect are decoupled analyzed based on this model.These analyses indicate that the high energy releasing efficiency and fast reaction propagation velocity of the reactive projectile are conducive to enhancing the damage effect.The experiments with various reactive projectiles impact velocity increasing from 702 to 1385 m/s were conducted to verify this model.The experimental results presented that,the damage hole radius of the rear-plate increases with the increase of impact velocity.At the impact velocity of 1350 m/s,the radius of damage hole formed by PTFE/Al/Bi_(2)O_(3),PTFE/Al/MoO_(3),PTFE/Al/Fe_(2)O_(3)projectile on the rear-plate become smaller in sequence.These results are consistent with the analytical model prediction,demonstrating that this model can predict the damage effect quantitatively.This work is of constructive significance to the application of reactive projectiles.

Gas-and plasma-driven hydrogen permeation behavior of stagnant eutectic-solid GaInSn/Fe double-layer structure

Gas-driven permeation(GDP)and plasma-driven permeation(PDP)of hydrogen gas through Ga In Sn/Fe are systematically investigated in this work.The permeation parameters of hydrogen through Ga In Sn/Fe,including diffusivity,Sieverts'constant,permeability,and surface recombination coefficient are obtained.The permeation flux of hydrogen through Ga In Sn/Fe shows great dependence on external conditions such as temperature,hydrogen pressure,and thickness of liquid Ga In Sn.Furthermore,the hydrogen permeation behavior through Ga In Sn/Fe is well consistent with the multilayer permeation theory.In PDP and GDP experiments,hydrogen through Ga In Sn/Fe satisfies the diffusion-limited regime.In addition,the permeation flux of PDP is greater than that of GDP.The increase of hydrogen plasma density hardly causes the hydrogen PDP flux to change within the test scope of this work,which is due to the dissolution saturation.These findings provide guidance for a comprehensive and systematic understanding of hydrogen isotope recycling,permeation,and retention in plasma-facing components under actual conditions.

Preparation and Microwave Absorbing Properties of Double-layer Fine Iron Tailings Cementitious Materials

To develop the microwave absorbing(MA)properties of cementitious material mixed with mine solid waste,the iron tailings cementitious microwave absorbing materials were prepared.The iron tailings was treated into different particle sizes by planetary ball mill,and the physicochemical properties of iron tailings were tested by laser particle size analyzer and scanning electron microscope(SEM).The electromagnetic parameters of iron tailings cementitious materials were characterized by a vector network analyzer and simulated MA properties,and the MA properties of iron tailings-cement composite system with steel fiber as absorber was studied.Based on the design of the single-layer structure,optimum mix ratio and thickness configuration method of double-layer structure were further studied,meanwhile,the mechanical properties and engineering application were analyzed and discussed.The results show that the particle size of iron tailings can afiect its electromagnetic behavior in cementitious materials,and the smaller particles lead the increase of demagnetisation efiect induced by domain wall motion and achieve better microwave absorbing properties in cementitious materials.When the thickness of matching layer and absorbing layer is 5 mm,the optimized microwave absorbing properties of C1/C3 double-layer cementitious material can obtain optimal RL value of-27.61 dB and efiective absorbing bandwidth of 0.97 GHz,which attributes to the synergistic efiect of impedance matching and attenuation characteristics.The double-layer microwave absorbing materials obtain excellent absorbing properties and show great design flexibility and diversity,which can be used as a suitable candidate for the preparation of favorable microwave absorbing cementitious materials.

Double-Layer-Optimizing Method of Hybrid Energy Storage Microgrid Based on Improved Grey Wolf Optimization

To reduce the comprehensive costs of the construction and operation of microgrids and to minimize the power fluctuations caused by randomness and intermittency in distributed generation,a double-layer optimizing configuration method of hybrid energy storage microgrid based on improved grey wolf optimization(IGWO)is proposed.Firstly,building a microgrid system containing a wind-solar power station and electric-hydrogen coupling hybrid energy storage system.Secondly,the minimum comprehensive cost of the construction and operation of the microgrid is taken as the outer objective function,and the minimum peak-to-valley of the microgrid’s daily output is taken as the inner objective function.By iterating through the outer and inner layers,the system improves operational stability while achieving economic configuration.Then,using the energy-self-smoothness of the microgrid as the evaluation index,a double-layer optimizing configuration method of the microgrid is constructed.Finally,to improve the disadvantages of grey wolf optimization(GWO),such as slow convergence in the later period and easy falling into local optima,by introducing the convergence factor nonlinear adjustment strategy and Cauchy mutation operator,an IGWO with excellent global performance is proposed.After testing with the typical test functions,the superiority of IGWO is verified.Next,using IGWO to solve the double-layer model.The case analysis shows that compared to GWO and particle swarm optimization(PSO),the IGWO reduced the comprehensive cost by 15.6%and 18.8%,respectively.Therefore,the proposed double-layer optimizationmethod of capacity configuration ofmicrogrid with wind-solar-hybrid energy storage based on IGWO could effectively improve the independence and stability of the microgrid and significantly reduce the comprehensive cost.

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.

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.

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.

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 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.