A new approach for recycling arsenic and tin from low-grade tin middlings using a self-sulfurization roasting

Massive amounts of low-grade tin middlings have been produced from tin tailings,in which arsenic and tin are worthy to be recycled.Owing to high sulfur content in these tin middlings,a novel self-sulfurization roasting was proposed to transform,separate and recover arsenic and tin in this research.There was no extra curing agent to be added,which decreased the formation of pollutant S-containing gas.The self-sulfurization process involved a two-stage roasting of reduction followed by sulfurization.First in reduction roasting,FeAsS decomposed to FeS and As and the As then transformed to As_(4)(g)and As_(4)S_(4)(g),via which the arsenic was separated and recovered.The arsenic content in the first residue could be decreased to 0.72 wt.%.Accompanied with it,the FeS was firstly oxidized to Fe_(1−x)S and then to SO_(2)(g)by the coexisted Fe_(2)O_(3),and finally reduced and combined with the independent Fe_(2)O_(3)to form Fe_(1−x)S.In the followed sulfurization roasting,the Fe_(1−x)S sulfurized SnO_(2)to SnS(g),due to which tin could be recovered and its content in the second residue decreased to 0.01 wt.%.This study provided an efficient method to separate and recover arsenic and tin from low-grade tin middlings.

Asymptomatic low-grade appendiceal mucinous neoplasm:A case report

BACKGROUND Low-grade appendiceal neoplasms(LAMN)are characterized by low incidence and atypical clinical presentations,often leading to misdiagnosis as acute or chronic appendicitis before surgery.The primary diagnostic tool for LAMN is abdominal computed tomography(CT)imaging.Surgical resection remains the cornerstone of LAMN management,necessitating en bloc tumor excision to minimize the risk of iatrogenic rupture.Laparoscopy,known for its minimal invasiveness,reduced postoperative discomfort,and expedited recovery,is a safe and reliable approach for LAMN treatment.Despite the possibility of pseudomyxoma peritonei development,appendectomy and partial appendectomy generally result in negative tumor margins and favorable outcomes,which can be attributed to the disease’s slow growth and lower malignancy.CASE SUMMARY A 71-year-old male patient was admitted to our hospital with a pelvic spaceoccupying lesion detected 1 mo prior.Physical examination showed a soft abdomen without tenderness or rebound and no palpable masses.No shifting dullness was noted,and digital rectal examination revealed no palpable mass.Enteroscopy revealed a raised,smooth-surfaced mass measuring 3.0 cm in the cecum.Abdominal contrast-enhanced CT showed a markedly thickened and dilated appendix with visible cystic shadows.Laparoscopic surgery was performed and revealed a significantly dilated appendix,leading to laparoscopic resection of the appendix and part of the cecum.Post-surgical pathologic analysis confirmed LAMN.The patient received symptomatic and supportive post-operative care and was discharged on postoperative day 4 without complications such as abdominal bleeding,intestinal obstruction,or incision infection.No tumor recurrence was observed during a 7-mo follow-up period.CONCLUSION LAMN is a rare disease that lacks specific clinical manifestations.Abdominal CT plays a crucial role in diagnosing LAMN,and laparoscopic surgery is a safe and effective diagnostic and therapeutic approach.

Low-grade myofibrosarcoma of the maxillary sinus:Two case reports

BACKGROUND Low-grade myofibroblastic sarcoma(LGMS)is an extremely rare tumor characterized by the malignant proliferation of myofibroblasts.LGMS most commonly develops in adults,predominantly in males,in the head and neck region,oral cavity,especially on the tongue,mandible,and larynx.This article presents 2 cases of LGMS localized to the maxillary sinus and provides an overview of the available literature.CASE SUMMARY Two patients with LGMS located in the maxillary sinus underwent surgery at the Department of Head and Neck Surgery.Case 1:A 46-year-old patient was admitted to the clinic with suspected LGMS recurrence in the right maxillary sinus(rT4aN0M0),with symptoms of pain in the suborbital area,watering of the right eye,thick discharge from the right nostril,and augmented facial asymmetry.After open biopsy-confirmed LGMS,the patient underwent expanded maxillectomy of the right side with immediate palate reconstruction using a microvascular skin flap harvested surgically from the middle arm.The patient qualified for adjuvant radiotherapy for the postoperative bed,with an additional margin.Currently,the patient is under 1.5 years of observation with no evidence of disease.Case 2:A 45-year-old man was admitted to our clinic with facial asymmetry,strabismus,exophthalmos,and visual impairment in the right eye.Six months earlier,the patient had undergone partial jaw resection at another hospital for fibromatosis.A contrast-enhanced computed tomography scan revealed a tumor mass in the postoperative log after an earlier procedure.An open biopsy confirmed lowgrade fibrosarcoma(rT4aN0M0).The patient qualified for an extended total right maxillectomy with orbital excision and right hemimandibulectomy with immediate microvascular reconstruction using an anterolateral thigh flap.The patient subsequently underwent adjuvant radiotherapy to the postoperative area.After 9 months,recurrence occurred in the right mandibular arch below the irradiated area.The lesion infiltrated the base of the skull,which warranted the withdrawal of radiotherapy and salvage surgery.The patient qualified for palliative chemotherapy with a regimen of doxorubicin+dacarbazine+cyclophosphamide and palliative radiotherapy for bone metastases.The patient died 26 months after surgical treatment.The cases have been assessed and compared with cases in the literature.CONCLUSION No specific diagnostic criteria or treatment strategies have been developed for LGMS.The treatment used for LGMS is the same as that used for sinonasal cancer radical tumor excision;adjuvant radiotherapy or chemoradiotherapy should also be considered.They have low malignant potential but are highly invasive,tend to recur,and metastasize to distant sites.Patients should undergo regular follow-up examinations to detect recurrence or metastasis at an early stage.Patients should be treated and observed at the highest referral centers.

Application of NSR-AISC Method in Reserve Estimation and Technical Economic Evaluation of Low-Grade Polymetallic Deposits: Taking a Low-Grade Copper-Molybdenum Mine in Guangdong Province as an Example

The development and utilization of low-grade polymetallic deposits with strategic mineral resources is one of the important measures to alleviate the current high dependence on strategic mineral resources in China. However, domestic mining enterprises and most mining consulting and design institutes usually use general industrial indicators to carry out reserve estimation and technical and economic feasibility studies on low-grade polymetallic deposits, which cannot truly reflect the economic value of such deposits. The article expounds on the definitions of net return value (NSR) and on-site total maintenance cost (AISC) of common ore smelters in the evaluation of overseas mineral resources. Taking a low-grade polymetallic copper-molybdenum mine in Guangdong Province as an example, comparing the research results showed the NSR-AISC method and the general industrial index method in low-grade polymetallic deposit. There are huge differences in the results of reserve estimation;through the further introduction of Taylor’s formula and the research results on the relationship between investment intensity and production scale, a more reasonable mine life and investment scale are recommended, and a more in-depth comparative study has been carried out in the dimension of technical and economic indicators. Based on the comparative study of the above two methods in reserve estimation and the evaluation results of technical and economic indicators, the author believes that the NSR-AISC method can better reflect the true value of low-grade polymetallic ore projects, and should be popularized and applied in resource evaluation and development practice. This article further describes the application status of the NSR-AISC method for reserve estimation and the evaluation of technical economic indicators, and suggests the main points that should be paid attention to in the use of the NSR-AISC method.

Thermally Chargeable Proton Capacitor Based on Redox-Active Effect for Energy Storage and Low-Grade Heat Conversion

Thermal energy is abundantly available in our daily life and industrial production,and especially,low-grade heat is often regarded as a byproduct.Collecting and utilizing this ignored energy by low-cost and simple technologies may become a smart countermeasure to relieve the energy crisis.Here,a unique device has been demonstrated to achieve high value-added conversion of low-grade heat by introducing redox-active organic alizarin(AZ)onto N-doped hollow carbon nanofibers(N–HCNF)surface.As-prepared N–HCNF/AZ can deliver a high specific capacitance of 514.3 F g^(-1)(at 1 A g^(-1))and an outstanding rate capability of 60.3%even at 50 A g^(-1).Meanwhile,the assembled symmetric proton capacitor can deliver a high energy density of 28.0 Wh kg^(-1) at 350.0 W kg^(-1) and a maximum power density of 35.0 kW kg^(-1) at 17.0 Wh kg^(-1).Significantly,the thermally chargeable proton capacitors can attain a surprisingly high Seebeck coefficient of 15.3 mV K^(-1) and a power factor of 6.02µW g^(-1).Taking advantage of such high performance,a satisfying open-circuit voltage of 481.0 mV with a temperature difference of 54 K is achieved.This research provides new insights into construction of high value-added energy systems requiring high electrochemical performances.

Anlotinib in combination with pembrolizumab for low-grade myofibroblastic sarcoma of the pancreas: A case report

BACKGROUND Low-grade myofibroblastic sarcoma(LGMS)is a rare spindle cell sarcoma espe-cially in the pancreas,with myofibroblastic differentiation.Hitherto,only a few cases have been reported.CASE SUMMARY Herein,we report a case involving the discovery of a pancreatic mass detected during a routine physical examination.Subsequent imaging and pathological tests of the patient led to the diagnosis of LGMS of the pancreas.Following surgical intervention,the patient experienced recurrence and metastasis.Conventional treatment is not effective for postoperative recurrent pancreatic LGMS with multiple metastases.After communicating with the patients and their families,informed consent was obtained for the treatment of anlotinib combined with pembrolizumab.Evaluation of imaging and clinical symptoms post-treatment revealed a relatively favorable response to the combination of anlotinib and pembrolizumab.CONCLUSION Based on the comprehensive literature review,our report aimed to provide evidence for a better understanding of the disease characteristics,diagnostic criteria,imaging findings,and identification of LGMS.And explore novel treatment strategies for this disease.

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.

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.

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.

Helmholtz decomposition with a scalar Poisson equation in elastic anisotropic media

P-and S-wave separation plays an important role in elastic reverse-time migration.It can reduce the artifacts caused by crosstalk between different modes and improve image quality.In addition,P-and Swave separation can also be used to better understand and distinguish wave types in complex media.At present,the methods for separating wave modes in anisotropic media mainly include spatial nonstationary filtering,low-rank approximation,and vector Poisson equation.Most of these methods require multiple Fourier transforms or the calculation of large matrices,which require high computational costs for problems with large scale.In this paper,an efficient method is proposed to separate the wave mode for anisotropic media by using a scalar anisotropic Poisson operator in the spatial domain.For 2D problems,the computational complexity required by this method is 1/2 of the methods based on solving a vector Poisson equation.Therefore,compared with existing methods based on pseudoHelmholtz decomposition operators,this method can significantly reduce the computational cost.Numerical examples also show that the P and S waves decomposed by this method not only have the correct amplitude and phase relative to the input wavefield but also can reduce the computational complexity significantly.