An adaptive neuro-fuzzy inference system white-box model for real-time multiphase flowing bottom-hole pressure prediction in wellbores

The majority of published empirical correlations and mechanistic models are unable to provide accurate flowing bottom-hole pressure(FBHP)predictions when real-time field well data are used.This is because the empirical correlations and the empirical closure correlations for the mechanistic models were developed with experimental datasets.In addition,most machine learning(ML)FBHP prediction models were constructed with real-time well data points and published without any visible mathematical equation.This makes it difficult for other readers to use these ML models since the datasets used in their development are not open-source.This study presents a white-box adaptive neuro-fuzzy inference system(ANFIS)model for real-time prediction of multiphase FBHP in wellbores.1001 real well data points and 1001 normalized well data points were used in constructing twenty-eight different Takagi eSugeno fuzzy inference systems(FIS)structures.The dataset was divided into two sets;80%for training and 20%for testing.Statistical performance analysis showed that a FIS with a 0.3 range of influence and trained with a normalized dataset achieved the best FBHP prediction performance.The optimal ANFIS black-box model was then translated into the ANFIS white-box model with the Gaussian input and the linear output membership functions and the extracted tuned premise and consequence parameter sets.Trend analysis revealed that the novel ANFIS model correctly simulates the anticipated effect of input parameters on FBHP.In addition,graphical and statistical error analyses revealed that the novel ANFIS model performed better than published mechanistic models,empirical correlations,and machine learning models.New training datasets covering wider input parameter ranges should be added to the original training dataset to improve the model's range of applicability and accuracy.

An artificial neural network visible mathematical model for real-time prediction of multiphase flowing bottom-hole pressure in wellbores

Accurate prediction of multiphase flowing bottom-hole pressure(FBHP)in wellbores is an important factor required for optimal tubing design and production optimization.Existing empirical correlations and mechanistic models provide inaccurate FBHP predictions when applied to real-time field datasets because they were developed with laboratory-dependent parameters.Most machine learning(ML)models for FBHP prediction are developed with real-time field data but presented as black-box models.In addition,these ML models cannot be reproduced by other users because the dataset used for training the machine learning algorithm is not open source.These make using the ML models on new datasets difficult.This study presents an artificial neural network(ANN)visible mathematical model for real-time multiphase FBHP prediction in wellbores.A total of 1001 normalized real-time field data points were first used in developing an ANN black-box model.The data points were randomly divided into three different sets;70%for training,15%for validation,and the remaining 15%for testing.Statistical analysis showed that using the Levenberg-Marquardt training optimization algorithm(trainlm),hyperbolic tangent activation function(tansig),and three hidden layers with 20,15 and 15 neurons in the first,second and third hidden layers respectively achieved the best performance.The trained ANN model was then translated into an ANN visible mathematical model by extracting the tuned weights and biases.Trend analysis shows that the new model produced the expected effects of physical attributes on FBHP.Furthermore,statistical and graphical error analysis results show that the new model outperformed existing empirical correlations,mechanistic models,and an ANN white-box model.Training of the ANN on a larger dataset containing new data points covering a wider range of each input parameter can broaden the applicability domain of the proposed ANN visible mathematical model.

A Flexible Smart Healthcare Platform Conjugated with Artificial Epidermis Assembled by Three‑Dimensionally Conductive MOF Network for Gas and Pressure Sensing

The rising flexible and intelligent electronics greatly facilitate the noninvasive and timely tracking of physiological information in telemedicine healthcare.Meticulously building bionic-sensitive moieties is vital for designing efficient electronic skin with advanced cognitive functionalities to pluralistically capture external stimuli.However,realistic mimesis,both in the skin’s three-dimensional interlocked hierarchical structures and synchronous encoding multistimuli information capacities,remains a challenging yet vital need for simplifying the design of flexible logic circuits.Herein,we construct an artificial epidermal device by in situ growing Cu_(3)(HHTP)_(2) particles onto the hollow spherical Ti_(3)C_(2)T_(x) surface,aiming to concurrently emulate the spinous and granular layers of the skin’s epidermis.The bionic Ti_(3)C_(2)T_(x)@Cu_(3)(HHTP)_(2) exhibits independent NO_(2) and pressure response,as well as novel functionalities such as acoustic signature perception and Morse code-encrypted message communication.Ultimately,a wearable alarming system with a mobile application terminal is self-developed by integrating the bimodular senor into flexible printed circuits.This system can assess risk factors related with asthmatic,such as stimulation of external NO_(2) gas,abnormal expiratory behavior and exertion degrees of fingers,achieving a recognition accuracy of 97.6%as assisted by a machine learning algorithm.Our work provides a feasible routine to develop intelligent multifunctional healthcare equipment for burgeoning transformative telemedicine diagnosis.

Forecasting multiphase flowing bottom-hole pressure of vertical oil wells using three machine learning techniques

Flowing bottom-hole pressure(FBHP)is a key metric parameter in the evaluation of performances of oil and gas production wells.An accurate prediction of FBHP is highly required in the petroleum industry for many applications,such the hydrocarbon production optimization,oil lifting cost,and assessment of workover operations.Production and reservoir engineers rely on empirical correlations and mechanistic models exist in open resources to estimate the FBHP.Several empirical models have been developed based on simulation and laboratory results that involved many assumptions that reduce the model's accuracy when they are applied for the field applications.The technologies of machine learning(ML)are one discipline of Artificial Intelligence(AI)techniques provide promising tools that help solving human's complex problems.This study develops machine-learning based models to predict the multiphase FBHP using three machine learning techniques that are Random forest,K-Nearest Neighbors(KNN),and artificial neural network(ANN).Results showed that using an artificial neural network model give error of 2.5%to estimate the FBHP which is less than the random forest and K-nearest neighbor models with error of 3.6%and 4%respectively.The ML models were developed based on a surface production data,which makes the FBHP is predicted using actual field data.The accuracy of the proposed models from ML was evaluated by comparing the results with the actual dataset values to ensure the effectiveness of the work.The results of this study show the potential of artificial intelligence in predicting the most complex parameter in the multiphase petroleum production process.

A personalized electronic textile for ultrasensitive pressure sensing enabled by biocompatible MXene/ PEDOT:PSS composite

Flexible,breathable,and highly sensitive pressure sensors have increasingly become a focal point of interest due to their pivotal role in healthcare monitoring,advanced electronic skin applications,and disease diagnosis.However,traditional methods,involving elastomer film-based substrates or encapsulation techniques,often fall short due to mechanical mismatches,discomfort,lack of breathability,and limitations in sensing abilities.Consequently,there is a pressing need,yet it remains a significant challenge to create pressure sensors that are not only highly breathable,flexible,and comfortable but also sensitive,durable,and biocompatible.Herein,we present a biocompatible and breathable fabric-based pressure sensor,using nonwoven fabrics as both the sensing electrode(coated with MXene/poly(3,4-ethylenedioxythiophene):polystyrene sulfonate[PEDOT:PSS])and the interdigitated electrode(printed with MXene pattern)via a scalable spray-coating and screen-coating technique.The resultant device exhibits commendable air permeability,biocompatibility,and pressure sensing performance,including a remarkable sensitivity(754.5 kPa^(−1)),rapid response/recovery time(180/110 ms),and robust cycling stability.Furthermore,the integration of PEDOT:PSS plays a crucial role in protecting the MXene nanosheets from oxidation,significantly enhancing the device's long-term durability.These outstanding features make this sensor highly suitable for applications in fullrange human activities detection and disease diagnosis.Our study underscores the promising future of flexible pressure sensors in the realm of intelligent wearable electronics,setting a new benchmark for the industry.

Mechanical Performance of Bio-inspired Bidirectional Corrugated Sandwich Pressure Shell Under External Hydrostatic Pressure

This paper aims to enhance the compression capacity of underwater cylindrical shells by adopting the corrugated sandwich structure of cuttlebone.The cuttlebone suffers uniaxial external compression,while underwater cylindrical shells are in a biaxial compressive stress state.To suit the biaxial compressive stress state,a novel bidirectional corrugated sandwich structure is proposed to improve the bearing capacity of cylindrical shells.The static and buckling analysis for the sandwich shell and the unstiffened cylindrical shell with the same volume-weight ratio are studied by numerical simulation.It is indicated that the proposed sandwich shell can effectively reduce the ratio between circumferential and axial stress from 2 to 1.25 and improve the critical buckling load by about 1.63 times.Numerical simulation shows that optimizing and adjusting the structural parameters could significantly improve the advantage of the sandwich shell.Then,the hydrostatic pressure tests for shell models fabricated by 3D printing are carried out.According to the experimental results,the overall failure position of the sandwich shell is at the center part of the sandwich shell.It has been found the average critical load of the proposed sandwich shell models exceeds two times that of the unstiffened shell models.Hence,the proposed bio-inspired bidirectional corrugated sandwich structure can significantly enhance the pressure resistance capability of cylindrical shells.

How Does Stacking Pressure Affect the Performance of Solid Electrolytes and All-Solid-State Lithium Metal Batteries?

All-solid-state lithium metal batteries(ASSLMBs)with solid electrolytes(SEs)have emerged as a promising alternative to liquid electrolyte-based Li-ion batteries due to their higher energy density and safety.However,since ASSLMBs lack the wetting properties of liquid electrolytes,they require stacking pressure to prevent contact loss between electrodes and SEs.Though previous studies showed that stacking pressure could impact certain performance aspects,a comprehensive investigation into the effects of stacking pressure has not been conducted.To address this gap,we utilized the Li_(6)PS_(5)Cl solid electrolyte as a reference and investigated the effects of stacking pressures on the performance of SEs and ASSLMBs.We also developed models to explain the underlying origin of these effects and predict battery performance,such as ionic conductivity and critical current density.Our results demonstrated that an appropriate stacking pressure is necessary to achieve optimal performance,and each step of applying pressure requires a specific pressure value.These findings can help explain discrepancies in the literature and provide guidance to establish standardized testing conditions and reporting benchmarks for ASSLMBs.Overall,this study contributes to the understanding of the impact of stacking pressure on the performance of ASSLMBs and highlights the importance of careful pressure optimization for optimal battery performance.

Effects of seepage pressure on the mechanical behaviors and microstructure of sandstone

Surrounding rocks of underground engineering are subjected to long-term seepage pressure,which can deteriorate the mechanical properties and cause serious disasters.In order to understand the impact of seepage pressure on the mechanical property of sandstone,uniaxial compression tests,P-wave velocity measurements,and nuclear magnetic resonance(NMR)tests were conducted on saturated sandstone samples with varied seepage pressures(i.e.0 MPa,3 MPa,4 MPa,5 MPa,6 MPa,7 MPa).The results demonstrate that the mechanical parameters(uniaxial compressive strength,peak strain,elastic modulus,and brittleness index),total energy,elastic strain energy,as well as elastic strain energy ratio,decrease with increasing seepage pressure,while the dissipation energy and dissipation energy ratio increase.Moreover,as seepage pressure increases,the micro-pores gradually transform into meso-pores and macro-pores.This increases the cumulative porosity of sandstone and decreases P-wave velocity.The numerical results indicate that as seepage pressure rises,the number of tensile cracks increases progressively,the angle range of microcracks is basically from 50-120to 80-100,and as a result,the failure mode transforms to the tensile-shear mixed failure mode.Finally,the effects of seepage pressure on mechanical properties were discussed.The results show that decrease in the effective stress and cohesion under the action of seepage pressure could lead to deterioration of strength behaviors of sandstone.

Impact pressure of waves generated by landslides on bank slopes

Impulse waves that are generated by landslides in narrow reservoir areas threaten the stability of buildings and bank slopes.To discuss the action process and evolution law of the wave pressure on bank slopes,a three-dimensional physical model test that considers impulse waves generated by landslides was performed,and factors including landslide width,thickness,slope angles of the sliding surface,and bank slope angle were considered.Based on wave forms on the bank slopes,wave pressure curve characteristics,and peak value,the action process of wave pressure could be divided into the following stages:maximum pulsating pressure stage,wave impact stage(when waves break),and stationary pulsation stage.It was found that wave breaking is dependent on the value of the surf similarity parameterξ.The distribution pattern of impact pressure decays linearly on both sides of the maximum impact pressure point,and the attenuation degree decreases when it attains 40%of the maximum value.Thus,it is proposed that the prediction formula for the maximum effective impact pressure of the bank slope be related to the reciprocal of wave steepness,relative water depth,and slope rate.The prediction formula provides strong theoretical support for early safety warning and for predicting the bank slope under impulse waves generated by landslides.

Ultra-broadband microwave absorber and high-performance pressure sensor based on aramid nanofiber,polypyrrole and nickel porous aerogel

Electronic devices have become ubiquitous in our daily lives,leading to a surge in the use of microwave absorbers and wearable sensor devices across various sectors.A prime example of this trend is the aramid nanofibers/polypyrrole/nickel(APN)aerogels,which serve dual roles as both microwave absorbers and pressure sensors.In this work,we focused on the preparation of aramid nanofibers/polypyrrole(AP15)aerogels,where the mass ratio of aramid nanofibers to pyrrole was 1:5.We employed the oxidative polymerization method for the preparation process.Following this,nickel was thermally evaporated onto the surface of the AP15 aerogels,resulting in the creation of an ultralight(9.35 mg·cm^(-3)).This aerogel exhibited a porous structure.The introduction of nickel into the aerogel aimed to enhance magnetic loss and adjust impedance matching,thereby improving electromagnetic wave absorption performance.The minimum reflection loss value achieved was-48.7 dB,and the maximum effective absorption bandwidth spanned 8.42 GHz with a thickness of 2.9 mm.These impressive metrics can be attributed to the three-dimensional network porous structure of the aerogel and perfect impedance matching.Moreover,the use of aramid nanofibers and a three-dimensional hole structure endowed the APN aerogels with good insulation,flame-retardant properties,and compression resilience.Even under a compression strain of 50%,the aerogel maintained its resilience over 500 cycles.The incorporation of polypyrrole and nickel particles further enhanced the conductivity of the aerogel.Consequently,the final APN aerogel sensor demonstrated high sensitivity(10.78 kPa-1)and thermal stability.In conclusion,the APN aerogels hold significant promise as ultra-broadband microwave absorbers and pressure sensors.

Emulsifying property,antioxidant activity,and bitterness of soybean protein isolate hydrolysate obtained by Corolase PP under high hydrostatic pressure

Enzymatic hydrolysis of proteins can enhance their emulsifying properties and antioxidant activities.However,the problem related to the hydrolysis of proteins was the generation of the bitter taste.Recently,high hydrostatic pressure(HHP)treatment has attracted much interest and has been used in several studies on protein modification.Hence,the study aimed to investigate the effects of enzymatic hydrolysis by Corolase PP under different pressure treatments(0.1,100,200,and 300 MPa for 1-5 h at 50℃)on the emulsifying property,antioxidant activity,and bitterness of soybean protein isolate hydrolysate(SPIH).As observed,the hydrolysate obtained at 200 MPa for 4 h had the highest emulsifying activity index(47.49 m^(2)/g)and emulsifying stability index(92.98%),and it had higher antioxidant activities(44.77%DPPH free radical scavenging activity,31.12%superoxide anion radical scavenging activity,and 61.50%copper ion chelating activity).At the same time,the enhancement of emulsion stability was related to the increase of zeta potential and the decrease of mean particle size.In addition,the hydrolysate obtained at 200 MPa for 4 h had a lower bitterness value and showed better palatability.This study has a broad application prospect in developing food ingredients and healthy foods.

Pressure stimulated current in progressive failure process of combined coal-rock under uniaxial compression:Response and mechanism

Effective monitoring of the structural health of combined coal-rock under complex geological conditions by pressure stimulated currents(PSCs)has great potential for the understanding of dynamic disasters in underground engineering.To reveal the effect of this way,the uniaxial compression experiments with PSC monitoring were conducted on three types of coal-rock combination samples with different strength combinations.The mechanism explanation of PSCs are investigated by resistivity test,atomic force microscopy(AFM)and computed tomography(CT)methods,and a PSC flow model based on progressive failure process is proposed.The influence of strength combinations on PSCs in the progressive failure process are emphasized.The results show the PSC responses between rock part,coal part and the two components are different,which are affected by multi-scale fracture characteristics and electrical properties.As the rock strength decreases,the progressive failure process changes obviously with the influence range of interface constraint effect decreasing,resulting in the different responses of PSC strength and direction in different parts to fracture behaviors.The PSC flow model is initially validated by the relationship between the accumulated charges of different parts.The results are expected to provide a new reference and method for mining design and roadway quality assessment.

Driving pressure in mechanical ventilation:A review

Driving pressure(ΔP)is a core therapeutic component of mechanical ventilation(MV).Varying levels ofΔP have been employed during MV depending on the type of underlying pathology and severity of injury.However,ΔP levels have also been shown to closely impact hard endpoints such as mortality.Considering this,conducting an in-depth review ofΔP as a unique,outcome-impacting therapeutic modality is extremely important.There is a need to understand the subtleties involved in making sureΔP levels are optimized to enhance outcomes and minimize harm.We performed this narrative review to further explore the various uses ofΔP,the different parameters that can affect its use,and how outcomes vary in different patient populations at different pressure levels.To better utilizeΔP in MV-requiring patients,additional large-scale clinical studies are needed.

Reservoir quality evaluation of the Narimba Formation in Bass Basin,Australia:Implications from petrophysical analysis,sedimentological features,capillary pressure and wetting fluid saturation

The evaluation of reservoir quality was accomplished on the Late Paleocene to Early Eocene Narimba Formation in Bass Basin,Australia.This study involved combination methods such as petrophysical analysis,petrography and sedimentological studies,reservoir quality and fluid flow units from derivative parameters,and capillary pressure and wetting fluid saturation relationship.Textural and diagenetic features are affecting the reservoir quality.Cementation,compaction,and presence of clay minerals such as kaolinite are found to reduce the quality while dissolution and secondary porosity are noticed to improve it.It is believed that the Narimba Formation is a potential reservoir with a wide range of porosity and permeability.Porosity ranges from 3.1%to 25.4%with a mean of 15.84%,while permeability ranges between 0.01 mD and 510 mD,with a mean of 31.05 mD.Based on the heterogenous lithology,the formation has been categorized into five groups based on permeability variations.Group I showed an excellent to good quality reservoir with coarse grains.The impacts of both textural and diagenetic features improve the reservoir and producing higher reservoir quality index(RQI)and flow zone indicators(FZI)as well as mostly mega pores.The non-wetting fluid migration has the higher possibility to flow in the formation while displacement pressure recorded as zero.Group II showed a fair quality reservoir with lower petrophysical properties in macro pores.The irreducible water saturation is increasing while the textural and digenetic properties are still enhancing the reservoir quality.Group III reflects lower quality reservoir with mostly macro pores and higher displacement pressure.It may indicate smaller grain size and increasing amount of cement and clay minerals.Group IV,and V are interpreted as a poor-quality reservoir that has lower RQI and FZI.The textural and digenetic features are negatively affecting the reservoir and are leading to smaller pore size and pore throat radii(r35)values to be within the range of macro,meso-,micro-,and nano pores.The capillary displacement pressure curves of the three groups show increases reaching the maximum value of 400 psia in group V.Agreement with the classification of permeability,r35 values,and pore type can be used in identifying the quality of reservoir.

An adaptive physics-informed deep learning method for pore pressure prediction using seismic data

Accurate prediction of formation pore pressure is essential to predict fluid flow and manage hydrocarbon production in petroleum engineering.Recent deep learning technique has been receiving more interest due to the great potential to deal with pore pressure prediction.However,most of the traditional deep learning models are less efficient to address generalization problems.To fill this technical gap,in this work,we developed a new adaptive physics-informed deep learning model with high generalization capability to predict pore pressure values directly from seismic data.Specifically,the new model,named CGP-NN,consists of a novel parametric features extraction approach(1DCPP),a stacked multilayer gated recurrent model(multilayer GRU),and an adaptive physics-informed loss function.Through machine training,the developed model can automatically select the optimal physical model to constrain the results for each pore pressure prediction.The CGP-NN model has the best generalization when the physicsrelated metricλ=0.5.A hybrid approach combining Eaton and Bowers methods is also proposed to build machine-learnable labels for solving the problem of few labels.To validate the developed model and methodology,a case study on a complex reservoir in Tarim Basin was further performed to demonstrate the high accuracy on the pore pressure prediction of new wells along with the strong generalization ability.The adaptive physics-informed deep learning approach presented here has potential application in the prediction of pore pressures coupled with multiple genesis mechanisms using seismic data.

Synergy of ZnO Nanowire Arrays and Electrospun Membrane Gradient Wrinkles in Piezoresistive Materials for Wide‑Sensing Range and High‑Sensitivity Flexible Pressure Sensor

Flexible pressure sensors have come under the spotlight because of their widespread adoption in human motion detection and human‒machine interactions.However,manufacturing pressure sensors with broad sensing ranges and large sensitivities continues to be a daunting task.Herein,a pressure sensor based on a gradient wrinkled electrospun polyurethane membrane with MXene-embedded ZnO nanowire arrays(ZAGW)was proposed.Under tiny pressure,dramatic increases in the contact area caused by interlocks of MXene-embedded ZnO nanowire arrays contributed to realizing a high sensitivity(236.5 kPa^(−1)).Additionally,the wide-sensing range(0–260 kPa)came from the fact that a wrinkled membrane with a gradient contact height ensured a continuous contact area change by gradually activating contact wrinkles.Meanwhile,the contact states of the gradient wrinkled membrane at varying pressures were investigated to expound the sensing mechanism of the ZAGW sensor.These exceptional performances enabled the ZAGW sensor to have vast application potential in human motion monitoring and tactile sensing.Furthermore,the ZAGW sensor can be integrated into the sensor array to monitor the pressure distribution.Considering the outstanding performance,the combination of ZnO nanowire arrays and electrospun membrane gradient wrinkles provides an innovative avenue for future sensing research.

The regulation of ferrocene-based catalysts on heat transfer in highpressure combustion of ammonium perchlorate/hydroxyl-terminated polybutadiene/aluminum composite propellants

The regulation of the burning rate pressure exponent for the ammonium perchlorate/hydroxylterminated polybutadiene/aluminum(AP/HTPB/Al)composite propellants under high pressures is a crucial step for its application in high-pressure solid rocket motors.In this work,the combustion characteristics of AP/HTPB/Al composite propellants containing ferrocene-based catalysts were investigated,including the burning rate,thermal behavior,the local heat transfer,and temperature profile in the range of 7-28 MPa.The results showed that the exponent breaks were still observed in the propellants after the addition of positive catalysts(Ce-Fc-MOF),the burning rate inhibitor((Ferrocenylmethyl)trimethylammonium bromide,Fc Br)and the mixture of Fc Br/catocene(GFP).However,the characteristic pressure has increased,and the exponent decreased from 1.14 to 0.66,0.55,and 0.48 when the addition of Ce-FcMOF,Fc Br and Fc Br/GFP in the propellants.In addition,the temperature in the first decomposition stage was increased by 7.50℃ and 11.40℃ for the AP/Fc Br mixture and the AP/Fc Br/GFP mixture,respectively,compared to the pure AP.On the other hand,the temperature in the second decomposition stage decreased by 48.30℃ and 81.70℃ for AP/Fc Br and AP/Fc Br/GFP mixtures,respectively.It was also found that Fc Br might generate ammonia to cover the AP surface.In this case,a reaction between the methyl in Fc Br and perchloric acid caused more ammonia to appear at the AP surface,resulting in the suppression of ammonia desorption.In addition,the coarse AP particles on the quenched surface were of a concave shape relative to the binder matrix under low and high pressures when the catalysts were added.In the process,the decline at the AP/HTPB interface was only exhibited in the propellant with the addition of Ce-Fc-MOF.The ratio of the gas-phase temperature gradient of the propellants containing catalysts was reduced significantly below and above the characteristic pressure,rather than 3.6 times of the difference in the blank propellant.Overall,the obtained results demonstrated that the pressure exponent could be effectively regulated and controlled by adjusting the propellant local heat and mass transfer under high and low pressures.

Parental Educational Expectations,Academic Pressure,and Adolescent Mental Health:An Empirical Study Based on CEPS Survey Data

Background:This study aimed to investigate the relationship between parental educational expectations and adolescent mental health problems,with academic pressure as a moderating variable.Methods:This study was based on the baseline data of the China Education Panel Survey,which was collected within one school year during 2013–2014.It included 19,958 samples from seventh and ninth graders,who ranged from 11 to 18 years old.After removing missing values and conducting relevant data processing,the effective sample size for analysis was 16344.The OLS(Ordinary Least Squares)multiple linear regression analysis was used to examine the relationship between parental educational expectations,academic pressure,and adolescents’mental health problems.In addition,we established an interaction term between parents’educational expectations and academic pressure to investigate the moderating effect of academic stress.Results:The study found that adolescents whose parents had high educational expectations reported less mental health problems.(β=−0.195;p<0.001).Additionally,adolescents who had high academic pressure reported more mental health problems.(β=0.649;p<0.001).Furthermore,the study found that academic pressure had a significant moderating effect on the relationship between parental educational expectations and adolescents’mental health problems(β=0.082;p<0.001).Conclusion:Parental educational expectations had a close relationship with adolescents’mental health problems,and academic pressure moderated this relationship.For those adolescents with high levels of academic pressure,the association between high parental educational expectations and mental health problems became stronger.On the contrary,for those adolescents with low levels of academic pressure,the association between high parental educational expectations and mental health problems became weaker.These findings shed new light on how parental educational expectations affected adolescent mental health problems and had significant implications for their healthy development.

Lateral earth pressure of granular backfills on retaining walls with expanded polystyrene geofoam inclusions under limited surcharge loading

Existing studies have focused on the behavior of the retaining wall equipped with expanded polystyrene(EPS)geofoam inclusions under semi-infinite surcharge loading rather than limited surcharge loading.In this paper,the failure mode and the earth pressure acting on the rigid retaining wall with EPS geofoam inclusions and granular backfills(henceforth referred to as EPS-wall),under limited surcharge loading are investigated through two-and three-dimensional model tests.The testing results show that different from the sliding of almost all the backfill in the EPS-wall under semi-infinite surcharge loading,only an approximately triangular backfill slides in the wall under limited surcharge loading.The distribution of the lateral earth pressure on the EPS-wall under limited surcharge loading is non-linear,and the distribution changes from the increase of the wall depth to the decrease with the increase of the limited surcharge loading.An approach based on the force equilibrium of a differential element is developed to predict the lateral earth pressure behind the EPS-wall subjected to limited surcharge loading,and its performance was fully validated by the three-dimensional model tests.