Low-density short-duration pulsed current-assisted aging treatment was applied to the Ti-6Al-4V-0.5Mo-0.5Zr alloy subjected to different solution treatments.The results show that numerous α_(p) phases redissolve into...Low-density short-duration pulsed current-assisted aging treatment was applied to the Ti-6Al-4V-0.5Mo-0.5Zr alloy subjected to different solution treatments.The results show that numerous α_(p) phases redissolve into the new β phase during the pulsed current-assisted aging process,and then the newly formed β phase is mainly transformed into the β_(t) phase,with occasional transition to new α_(p) phase,leading to a remarkable grain refinement,especially for the lamellarαs phases.In comparison to conventional aging treatment,the pulsed current-assisted aging approach achieves a significant enhancement in strength without degrading ductility,yielding an excellent mechanical property combination:a yield strength of 932 MPa,a tensile strength of 1042 MPa,and an elongation of 12.2%.It is primarily ascribed to the increased fraction of β_(t) phases,the obvious grain refinement effect,and the slip block effect induced by the multiple-variantαs colonies distributed within β_(t) phases.展开更多
We report a method for increasing the mechanical strength of carbon nanotube(CNT)fibers while enabling the uniform adhesion of cerium oxide(CeO_(2))abrasive particles to them using polyethyleneimine(PEI).Results show ...We report a method for increasing the mechanical strength of carbon nanotube(CNT)fibers while enabling the uniform adhesion of cerium oxide(CeO_(2))abrasive particles to them using polyethyleneimine(PEI).Results show that 5%of PEI increases the tensile strength of CNT fibers by approximately 175%.CeO_(2) particles were uniformly deposited on the reinforced CNT fibers by electrophoretic deposition.A flexible polishing tool was fabricated by weaving the CeO_(2)-CNT fibers into a non-woven fabric substrate.When used to polish potassium dihydrogen phosphate crystals,the tool reduced the surface roughness from 200 to 7.6 nm within 10 min.This approach has potential use for the development of new precision processing tools.展开更多
The application and promotion of waste glass powder concrete(WGPC)cansignificantly alleviate the pressure of concrete material scarcity and environmental pollution.Compressive strength(CS)is a critical parameter for e...The application and promotion of waste glass powder concrete(WGPC)cansignificantly alleviate the pressure of concrete material scarcity and environmental pollution.Compressive strength(CS)is a critical parameter for evaluating the efficacy of WGPC.Unlike conventional testing methods,machine learning techniques offer precise and reliable predictions of concrete’s compressive strength,especially in its long-term mechanical properties.In this work,four models,namely Multiple Linear Regression(MLR),Back Propagation Neural Network(BPNN),Support Vector Regression(SVR),and Random Forest Regression(RFR)were employed.Furthermore,particle swarm optimization(PSO)algorithm and cross-validation techniques were applied to fine-tune the model parameters,striving for peak prediction performance.The results indicated that optimized models generally exhibit enhanced predictive accuracy compared to their basic counterparts.Notably,the PSO-RFR model excels among all evaluated models,showcasing superior performance on the testing dataset.It achieves a coefficient of determination(R^(2))of 0.9231,a mean absolute error(MAE)of 2.1073,and a root mean square error(RMSE)of 3.6903.When compared to experimental results,the PSO-RFR and PSO-BPNN models demonstrate exceptional predictive accuracy.Notably,the PSO-BPNN model exhibits the closest R^(2)values between its training and test sets.This close alignment of R^(2)values between the training and testing sets reflects the PSO-BPNN model’s superior generalization ability for unseen data.The findings present an efficient method for predicting concrete’s compressive strength,contributing to the sustainable development of concrete materials,and providing theoretical support for their research and application.展开更多
Kiehl and colleagues1 utilized data from the Osteoarthritis Initiative(OAI)to address a clinically significant question:Is lifetime participation in strength training(ST)associated with improved trajectories of pain,f...Kiehl and colleagues1 utilized data from the Osteoarthritis Initiative(OAI)to address a clinically significant question:Is lifetime participation in strength training(ST)associated with improved trajectories of pain,function,and mobility in individuals with knee osteoarthritis(OA)?Among 3192 participants,those classified as“Lifelong ST”(n=142)demonstrated superior 4-year patient-reported outcomes and exhibited the lowest incidence of mobility disability(0.8%vs 2.3%–4.1%).Notably,they also maintained the fastest walking speeds at Year 4.展开更多
We report first-principles predictions of a cage-like polymeric nitrogen phase(cage-N)composed of interlocked N10 clusters stabilized by mixed sp^(2)/sp^(3) hybridization.Under high pressure,cage-N exhibits exceptiona...We report first-principles predictions of a cage-like polymeric nitrogen phase(cage-N)composed of interlocked N10 clusters stabilized by mixed sp^(2)/sp^(3) hybridization.Under high pressure,cage-N exhibits exceptional mechanical performance,including an ideal compressive strength of 343 GPa at a pressure of 300 GPa,~33% higher than that of diamond.This ultrahigh strength arises from the synergistic interplay between its three-dimensional covalent framework and hybridized bonding topology,which enables isotropic stress accommodation and dynamic electronic rearrangement.These results establish cage-N as a promising non-carbon ultrahard material and provide a bonding-driven route toward designing superhard frameworks under extreme conditions.展开更多
This study investigates the impacts of mixing time,execution procedure,cement dosage(α),and total water-to-cement ratio(W_(Total)/C)on the mixing energy(E)of deep soil mixing(DSM)columns and how E influences the stre...This study investigates the impacts of mixing time,execution procedure,cement dosage(α),and total water-to-cement ratio(W_(Total)/C)on the mixing energy(E)of deep soil mixing(DSM)columns and how E influences the strength of treated sand.Columns with a diameter of 7.5 cm were constructed using three mixing times(130,190,and 250 s),two execution procedures(normal and zigzag),threeαvalues(300,400,and 500 kg/m^(3)),and three W_(Total)/C ratios(2.5,3.0,and 3.5).For comparison,equivalent laboratory samples were also examined.Results revealed that increasing the mixing time andα,adopting the zigzag execution procedure,and reducing the W_(Total)/C ratio increase E.Outcomes indicated that an increase in E from 0.49-0.70 kJ to 0.70-0.90 kJ,0.90-1.10 kJ,and 1.10-1.40 kJ improves the unconfined compressive strength(UCS)of columns on average by 66%,124%,and 179%,respectively,and the secant modulus by 61%,110%,and 152%.Average strain at maximum stress also rises from 0.68%to 0.75%,0.81%,and 0.84%,respectively.The study identified a threshold in the direct relationship between E and the strength ratio(λ),beyond whichλdid not increase significantly with further increases in E.Additionally,at low and high E levels,DSM samples mainly failed by crushing and cracking modes,respectively.In DSM columns withα=500 kg/m^(3)and W_(Total)/C=2.5,increasing average E from 0.77 kJ to 0.95 kJ,1.08 kJ,and 1.28 kJ resulted in a reduction of coefficients of variation of UCS from 30.4%to 27.8%,24.5%,and 21.1%,respectively.展开更多
Background This study compared knee osteoarthritis(OA)outcomes specific to pain,physical function,and quality of life in later life based on strength training(ST)participation over a lifetime.Methods Participants from...Background This study compared knee osteoarthritis(OA)outcomes specific to pain,physical function,and quality of life in later life based on strength training(ST)participation over a lifetime.Methods Participants from the Osteoarthritis Initiative(n=3192)were grouped by ST engagement during ages 12–18 years,19–34 years,35–49 years,and 50+years.Participants were categorized as:No ST(no ST at any point;61.7±9.0 years(mean±SD)),Some ST(engaged in ST during 1–3 life stages;58.9±8.7 years),and Lifelong ST(consistently engaged in ST across all life stages;55.6±8.1 years).Measures were collected at baseline and Year 4:Western Ontario and McMaster Universities Osteoarthritis Index Scores(WOMAC;pain,daily activities),Knee Injury and Osteoarthritis Outcome Score(KOOS;sports,recreation),Physical Activity Score for the Elderly(PASE),Short Form-12 Physical Component Score(SF-12 PCS),mobility disability,chair rise time,and walking speed(20 m and 400 m).Results At Year 4,the Lifelong ST group reported better WOMAC activity scores in the right knee along with better WOMAC pain,KOOS sports/recreation,and PASE scores compared to other groups(p<0.05).The Lifelong ST group had the lowest incidence of mobility disability of all groups(0.8%vs.2.3%–4.1%;p=0.015)and maintained the fastest walking speeds in Year 4.Conclusion For those with knee OA,ST throughout life may help preserve function and mobility,allowing for greater physical activity engagement while keeping pain levels relatively lower.展开更多
Received:06 December 2025;Accepted:25 February 2026;Published:30 March 2026 ABSTRACT:In the last decade,the importance of sustainable construction and artificial intelligence(AI)in civil engineering has been underline...Received:06 December 2025;Accepted:25 February 2026;Published:30 March 2026 ABSTRACT:In the last decade,the importance of sustainable construction and artificial intelligence(AI)in civil engineering has been underlined in many studies.Numerous studies highlighted the superiority of AI techniques over simple and mathematical regression analyses,which suffer from relatively poor generalization and an inability to capture highly non-linear relationships among inputs and output(s)parameters.In this study,to evaluate the compressive strength of concrete with glass powder(GP)and recycled aggregates,600 concrete samples were tested in the laboratory,and their results were evaluated.For intelligent assessment of concrete compressive strength(CCS),the study utilized an improved artificial neural network(ANN)with particle swarm optimization(PSO)algorithm and imperialist competitive algorithm(ICA).For training the models,the experimentally obtained data were used.The concrete ingredients formed the inputs of the AI-based predictive models of CCS.The experimental findings reveal that the implementation of recycled coarse aggregates in concrete from a sustainable construction point of view is advantageous and can enhance the CCS by 11.43%.Apart from that,findings indicate that utilization of 10%GP can lead to a nearly 20%increase in CCS(from 44.6 to 54.1 MPa).Additionally,the experimental observations show almost 40%improvement of CCS when 5%micro silica was used in the concrete mixture.Based on the findings,the study suggests the utilization of waste glass powder to partially replace cement in concrete,which can reduce the amount of cement production.This reduction from economic,energy-saving,and environmental(reduction in greenhouse gas emissions)points of view is of interest.On the other hand,the AI results show that the PSO-based ANN model outperforms the ICA-based ANN for the utilized dataset.According to the findings,the PSO-based ANN predictive model(with a coefficient of determination value of 0.939 and root mean square value of 0.113 for testing data)is a capable tool in predicting the CCS.Hence,this study recommends the implementation of AI-based models in CCS assessment.展开更多
Background Intrinsic and extrinsic foot muscles contribute to foot and lower limb function.This knowledge provides opportunities to target these muscles in exercise and rehabilitation.However,a barrier is the limited ...Background Intrinsic and extrinsic foot muscles contribute to foot and lower limb function.This knowledge provides opportunities to target these muscles in exercise and rehabilitation.However,a barrier is the limited understanding of how prescribed exercises affect muscle activation and force output.This study examined muscle activation and torque production about the metatarsophalangeal joints of intrinsic and extrinsic foot muscles during common foot strengthening exercises.The secondary aim was to determine whether the addition of a postural change(leaning forward)or addition of mass can further increase muscle activation and torque.Methods Fifteen healthy participants(9 males,6 females;age=36.5±8.0 years,mean±SD)participated.Muscle activation was measured using fine-wire electromyography(EMG)electrodes for 2 intrinsic and 2 extrinsic muscles,and surface EMG electrodes for 4 superficial muscles.Metatarsophalangeal joint torque was measured using a custom toe dynamometer.Participants attended one 90-min session and performed 16 exercises.Five included the addition of leaning forward and 3 the addition of mass(20%of participant’s mass)to compare their effects on muscle activity and torque.Results Muscle activation and metatarsophalangeal joint torque varied considerably across exercises.Both the addition of leaning forward and the addition of mass increased muscle activation and metatarsophalangeal joint torque,with the addition of leaning forward producing a 35%median increase in torque about the metatarsophalangeal joints.Conclusion Many common exercises,despite high muscle activation,produce relatively low metatarsophalangeal joint torque,raising questions about their clinical value.Adding a forward lean significantly increases toe flexor muscle activity and torque,reaching levels greater than added mass and comparable to walking.展开更多
Strain measurements during uniaxial compressive strength(UCS)testing and their subsequent interpretation to obtain elastic parameters are relatively straightforward for most rocks.However,for slates,which are foliated...Strain measurements during uniaxial compressive strength(UCS)testing and their subsequent interpretation to obtain elastic parameters are relatively straightforward for most rocks.However,for slates,which are foliated metamorphic rocks characterized by significant anisotropy,the dependence of elastic properties on the orientation of foliation complicates the measurement and interpretation of strain data.In this study,a series of wave propagation velocity tests and UCS tests are conducted on cylindrical and prismatic slate specimens to gain a better understanding of how to obtain and process deformability and strength results.Wave propagation velocity results demonstrate an increase with the dip of foliation planes crossed,which is consistent with previous studies.Based on UCS test results,two methodologies are considered for obtaining transversely isotropic deformability parameters:the least-squares method and the recently proposed generalized reduction gradient(GRG)algorithm.Their performance is assessed in the context of potentially variable and limited amounts of data.GRG algorithms provide an enhanced analysis technique for estimating anisotropic elastic properties when dealing with limited or heterogeneous laboratory test data.Different strength models have also been considered,including the classic Jaeger's weakness plane(JPW)and its subsequent modification,i.e.2HBJPW.The 2HBJPW approach has proven to be more consistent with the obtained results and enhances the representation of the strength properties of slates.Additionally,a finite element method(FEM)numerical approach is employed to compare results with analytical and experimental ones,demonstrating a good match,thereby offering calibrated inputs for rock engineering applications.展开更多
China has set its economic growth target for 2026 in the range of 4.5-5 percent.After three consecutive years of aiming for growth of around 5 percent,the slightly adjusted range reflects a prudent assessment of a com...China has set its economic growth target for 2026 in the range of 4.5-5 percent.After three consecutive years of aiming for growth of around 5 percent,the slightly adjusted range reflects a prudent assessment of a complex global environment while still signalling a commitment to stable development.展开更多
In complex geological environments,the analysis of drill cores to determine rock strength can be challenging due to the wide variability in the degree of fracturing,leading to subjectivity in the collection of represe...In complex geological environments,the analysis of drill cores to determine rock strength can be challenging due to the wide variability in the degree of fracturing,leading to subjectivity in the collection of representative samples for uniaxial compressive strength testing.This study evaluates non-destructive techniques on calcareous rocks with different tectonic deformations,including Equotip hardness,ultrasound P-wave velocity,thin section analysis,and calcimetry,integrated with photogrammetric fracture analysis.The investigated carbonate rock samples are sourced from drill cores derived from the Umbria-Marche fold and thrust belt(northern Apennines,Italy),including a gently dipping limb of an anticline,a hinge zone of an anticline,and a fault zone associated with a thrust.Fracture intensity,quantified by the P21 parameter using photogrammetric techniques on pre-loading rock samples,is assessed alongside macroscopic identification of discontinuities(such as stylolites,veins,and joints)using marker colours to monitor failures during uniaxial compression testing.Empirical correlations depicted by single and multi-linear relationships indicate a strong dependence between the mechanical and physical properties of limestones.Both Equotip and P-wave velocity are influenced by fracture intensity,but P-wave velocity varies significantly with discontinuity orientation,especially at 45°-90°.To refine uniaxial compressive strength predictions and mitigate multicollinearity,statistical approaches,including linear and multilinear regression,Principal Component Analysis and Gaussian Process Regression,were tested.Findings improve the reliability of non-destructive techniques for assessing rock strength in structurally complex settings,with implications for geotechnical applications.展开更多
Texture and grain structure evolution during annealing and their effects on tensile strength and anisotropy were studied using XRD,DSC,SEM,EBSD and TEM.The results indicate that elevated rolling temperatures reduce th...Texture and grain structure evolution during annealing and their effects on tensile strength and anisotropy were studied using XRD,DSC,SEM,EBSD and TEM.The results indicate that elevated rolling temperatures reduce the f(g)_(max(Copper))/f(g)_(max(Brass))ratio,increase S-Brass fine bands,and promote S-dispersoid precipitation,leading to finer recrystallized grains.Dominant recrystallization textures transform from Goss+P to Goss and then to Goss+Cube with increasing rolling temperature.Annealing at 350℃shows four tensile strength response stages:fast softening I,rapid strengthening II,slow strengthening III,and slow softening IV.The transition from Stages I to II is driven by the formation of strong Goss and P textures,and Stage IV is linked to enhanced Cube texture.Plates with Goss+Cube textures and fine equiaxed grains exhibit the lowest YS/UTS ratio and minimal anisotropy.展开更多
Accurate prediction of concrete compressive strength is fundamental for optimizing mix designs,improving material utilization,and ensuring structural safety in modern construction.Traditional empirical methods often f...Accurate prediction of concrete compressive strength is fundamental for optimizing mix designs,improving material utilization,and ensuring structural safety in modern construction.Traditional empirical methods often fail to capture the non-linear relationships among concrete constituents,especially with the growing use of supple-mentary cementitious materials and recycled aggregates.This study presents an integrated machine learning framework for concrete strength prediction,combining advanced regression models—namely CatBoost—with metaheuristic optimization algorithms,with a particular focus on the Somersaulting Spider Optimizer(SSO).A comprehensive dataset encompassing diverse mix proportions and material types was used to evaluate baseline machine learning models,including CatBoost,XGBoost,ExtraTrees,and RandomForest.Among these,CatBoost demonstrated superior accuracy across multiple performance metrics.To further enhance predictive capability,several bio-inspired optimizers were employed for hyperparameter tuning.The SSO-CatBoost hybrid achieved the lowest mean squared error and highest correlation coefficients,outperforming other metaheuristic approaches such as Genetic Algorithm,Particle Swarm Optimization,and Grey Wolf Optimizer.Statistical significance was established through Analysis of Variance and Wilcoxon signed-rank testing,confirming the robustness of the optimized models.The proposed methodology not only delivers improved predictive performance but also offers a transparent framework for mix design optimization,supporting data-driven decision making in sustainable and resilient infrastructure development.展开更多
Excavation causes stress redistribution and affects the stress path during the shearing process of rock.The shear strength of rock varies under different stress paths,and the presence of defects reduces the shear stre...Excavation causes stress redistribution and affects the stress path during the shearing process of rock.The shear strength of rock varies under different stress paths,and the presence of defects reduces the shear strength.To further investigate this phenomenon,this study investigates the shear behaviour of rocks with different shear surface integrities under the influenceof different stress paths through laboratory tests and numerical simulations.The results indicate that the shear strength depends on the stress path and a decrease in the shear surface integrity reduces the degree of dependence.The cohesion and friction angle of the Mohr‒Coulomb criterion decrease with weakening of the shear surface integrity.For different stress paths,the direct shear strength is always greater than that of other shear stress paths.The pattern of changes in the acoustic emission count and cumulative count indirectly reflectsthe above findings.Numerical simulations further indicate that the different principal stress states and normal suppression effects during the shearing process lead to changes in the factors of crack propagation,resulting in different mechanical behaviours under various stress paths.For rocks with different integrity levels,the main reason for the different path dependences of shear strength is that the size of the area affected by shear is different.Shear failure will concentrate on the shear plane when the normal inhibition effect is greater.This study explores the mechanism of rock shear behaviour,providing a theoretical basis for establishing more accurate constitutive models and strength criteria.展开更多
We employed machine learning approaches and visualization interpretation methods to explore the influencing factors of the compressive strength of sea sand concrete to attain a better understanding of the inherent law...We employed machine learning approaches and visualization interpretation methods to explore the influencing factors of the compressive strength of sea sand concrete to attain a better understanding of the inherent laws of concrete mix design.Four models,including random forest,Cat Boost,XGBoost,and deep neural network,were trained.The experimental results demonstrate that the XGBoost model performs the best in predicting the strength of sea sand concrete.Its R^(2)value reached 0.9999,and evaluation indexes such as MAPE,RMSE,MAE,and MSE are superior to those of other models.The principal component analysis(PCA)was conducted to visually analyze the structure and distribution of the original feature data,and Pearson correlation coefficient analysis and Shapley additive explanation(SHAP)were utilized to explore the impact of input characteristics on the strength of sea sand concrete.SHAP analysis is more conducive to revealing the nonlinear effects of various characteristics on the model prediction results,especially that particle size of stone has significant impacts on the strength of sea sand concrete.In addition,experimental verification was carried out to confirm the accuracy of the optimized training model.These findings offer some insights for the future design and application of sea sand in high-performance marine and coastal infrastructure.展开更多
The penetration of shaped charge jets into targets at high velocities is significantly influenced by the compressibility effect,while at low velocities,the strength effect becomes predominant.In the latter regime,mate...The penetration of shaped charge jets into targets at high velocities is significantly influenced by the compressibility effect,while at low velocities,the strength effect becomes predominant.In the latter regime,material strength dictates the resistance to plastic deformation and flow,a contrast to the shockwave-dominated interactions where compressibility is key.This paper presents a self-consistent compressible penetration theory that considers both the axial penetration and radial crater growth of shaped charge jets into targets.An integrated approach where the axial and radial dynamics are coupled has been proposed,influencing each other through shared physical principles rather than being treated as separate,empirically linked phenomena.The presented theory is rooted in the compressible Bernoulli equation and the linear Rankine-Hugoniot relation.These foundational equations are employed to accurately model the high-pressure shock state and subsequent material flow at the jet-target interface,providing a robust physical basis for the penetration model.Notably,it considers the target material's compressibility,which elevates the pressure at the jet-target interface beyond that observed with incompressible materials.This pressure increase is directly proportional to the target's degree of compressibility.As such,this model of compressible penetration reorients the analytical approach:rather than merely estimating penetration resistance,it determines this value from the target material's specific compressibility and yield strength.This shift from empirical correlations to a physics-based derivation of penetration resistance enhances the model's predictive power,particularly for novel target materials or engagement conditions outside established experimental datasets.This investigation establishes a quantitative link between the material's yield strength and its penetration resistance.The accuracy of this penetration resistance value is paramount,as it significantly influences the predicted crater diameter;indeed,the crater diameter's sensitivity to this resistance underscores the necessity for its precise determination.Ultimately,by integrating the yield strength of the target material,this framework enables the prediction of both the penetration depth and the resultant crater diameter from a shaped charge jet.The theory's validation involved two experimental sets:the first focused on shaped charge jet penetration into 45#steel at varied stand-offs,while the second utilized targets of high-to ultrahigh-strength steel-fiber reactive powder concrete(RPC)with differing strength characteristics.These experimental campaigns were specifically chosen to test the theory against both ductile metallic alloys,where plastic flow is significant,and advanced quasi-brittle cementitious composites,presenting a broad spectrum of material responses and penetration challenges.Resulting hole profiles derived from theoretical calculations demonstrated a strong correspondence with empirical measurements for both material types.展开更多
Purpose:ATLAS is a cross-sectional study aiming to investigate environmental and genetic determinants of athletic performance in healthy Greek competitive athletes(CA).This article presents the study design,investigat...Purpose:ATLAS is a cross-sectional study aiming to investigate environmental and genetic determinants of athletic performance in healthy Greek competitive athletes(CA).This article presents the study design,investigates the muscle strength performance(MSP)of 289 adult and teenage CA,exercisers,and physically inactive individuals(PI),and proposes predictive models of MSP for adults.Methods:Muscle maximal,speed,and explosive strength(MMS/MSS/MES)at unilateral maximal concentric flexion and extension contraction(FC/EC)were evaluated using Biodex System 3 PRO^(TM)at 60°/s,180°/s,and 300°/s,while additional performance markers were assessed through field ergometric testing.Participants were interviewed about their lifestyle,dietary habits,physical activity,injury,and medical history.Body composition was assessed via bioelectrical impedance.gDNA was extracted from biochemical samples and then genotyped.Statistical analysis was conducted using IBM SPSS Statistics v21.0 and R.Results:Age,fitness,and sex impacted correlations of MSP with body composition and anthropometric measurements(p<0.05).Among CA,females outperformed males in accuracy(p<0.001)while,males outperformed females in anaerobic power,MSP,speed,and endurance(p<0.001).Adult CA outperformed exercisers and PI in MMS,MSS,and MES(p<0.05).Multiple linear regression models,with predictors age,FFM,body extremity,training load explained the majority of variation in MMS(R^(2)_(adj):71.4%–88.9%),MSS(R^(2)_(adj):64.8%–78.4%),and MES(R^(2)_(adj):52.7%–68.4%)at EC,FC,and their mean(p<0.001).Conclusions:Muscle-strengthening strategies should be customized according to individual fitness levels,body composition,and anthropometric measurements.The innovative sex-specific regression models assessing MMS,MSS,and MES at EC and FC provide a framework for personalizing rehabilitation and skill-specific training strategies.展开更多
The low ionic conductivity and poor mechanical strength of polyethylene oxide(PEO)-based electrolytes severely restrict their practical application.To address this problem,this work designs a scalable,high-strength(24...The low ionic conductivity and poor mechanical strength of polyethylene oxide(PEO)-based electrolytes severely restrict their practical application.To address this problem,this work designs a scalable,high-strength(24.3 MPa)bicontinuous porous poly(m-phthaloyl-m-phenylenediamine)(PMIA)membrane integrated into PEO/LiTFSI(PL),thus forming a PMIA/PEO/LiTFSI(PPL)composite electrolyte.Compared to the PL electrolyte,the PPL electrolyte reinforced by a bicontinuous porous PMIA membrane exhibits significantly enhanced mechanical strength,reaching 13.4 MPa.In addition,the amide groups on PMIA strongly coordinate with LiTFSI and form hydrogen bonds with PEO,promoting Li salt dissociation and reducing the Li^(+)migration barrier.This creates efficient,fast Li^(+)transport channels at the PMIA/PL interfaces,effectively promoting the uniform Li^(+)deposition and minimizing lithium dendrite formation.The PPL electrolyte achieves high ionic conductivity(1×10^(−4)S cm^(−1)at 30°C)and Li^(+)transference number(tLi^(+)=0.43).The assembled LiFePO_(4)/Li battery demonstrates excellent cycling stability,retaining 80%capacity after 2000 cycles at 2 C,while the Li/Li symmetric cell operates stably for over 900 h at 0.3 mA cm^(−2).Therefore,the scalable porous PMIA membrane effectively enhances both the mechanical strength and Li^(+)transport in PEO-based electrolytes,offering a viable strategy for their commercial-scale implementation.展开更多
Water storage in the Three Gorges Reservoir in China has increased the regional microseismicity.Bedding-rock landslides,one of the most common slope structures in the Three Gorges Reservoir,are highly prone to sliding...Water storage in the Three Gorges Reservoir in China has increased the regional microseismicity.Bedding-rock landslides,one of the most common slope structures in the Three Gorges Reservoir,are highly prone to sliding under seismic loading.Existing research primarily focuses on the stability of bedding rock landslides under strong earthquakes,while studies on the cumulative damage and long-term stability of bedding rock landslides under high-frequency microseismicity remain immature.In this study,we considered bedding rock landslides under high-frequency microseismicity in the Three Gorges Reservoir area as the research subject and equivalent microseismicity as pre-peak cyclic loading.First,we analyzed the shear strength deterioration of rock mass structural planes under pre-peak cyclic loading conditions and found that the deformation and failure of structural planes involve contact and damage effects.The shear strength of the rock mass structural planes under pre-peak cyclic loading conditions is affected by the confining pressure,loading rate,loading amplitude,and number of loading cycles.Among these factors,the shear strength of the structural planes was the most sensitive to the number of loading cycles.As the number of cycles increased,the rock mass structural planes underwent three stages:stress adjustment(increase in shear strength),fatigue damage(gradual decrease in shear strength),and structural failure(rapid decrease in shear strength).The stability of bedding rock landslides under high-frequency microseismicity was analyzed,revealing that the stability of bedding rock landslides under high-frequency microseismicity can be divided into three stages:short-term enhancement,gradual degradation,and rapid deterioration,exhibiting characteristics of gradual and sudden changes.展开更多
基金National Key Research and Development Program of China(2021YFB3700801)。
文摘Low-density short-duration pulsed current-assisted aging treatment was applied to the Ti-6Al-4V-0.5Mo-0.5Zr alloy subjected to different solution treatments.The results show that numerous α_(p) phases redissolve into the new β phase during the pulsed current-assisted aging process,and then the newly formed β phase is mainly transformed into the β_(t) phase,with occasional transition to new α_(p) phase,leading to a remarkable grain refinement,especially for the lamellarαs phases.In comparison to conventional aging treatment,the pulsed current-assisted aging approach achieves a significant enhancement in strength without degrading ductility,yielding an excellent mechanical property combination:a yield strength of 932 MPa,a tensile strength of 1042 MPa,and an elongation of 12.2%.It is primarily ascribed to the increased fraction of β_(t) phases,the obvious grain refinement effect,and the slip block effect induced by the multiple-variantαs colonies distributed within β_(t) phases.
文摘We report a method for increasing the mechanical strength of carbon nanotube(CNT)fibers while enabling the uniform adhesion of cerium oxide(CeO_(2))abrasive particles to them using polyethyleneimine(PEI).Results show that 5%of PEI increases the tensile strength of CNT fibers by approximately 175%.CeO_(2) particles were uniformly deposited on the reinforced CNT fibers by electrophoretic deposition.A flexible polishing tool was fabricated by weaving the CeO_(2)-CNT fibers into a non-woven fabric substrate.When used to polish potassium dihydrogen phosphate crystals,the tool reduced the surface roughness from 200 to 7.6 nm within 10 min.This approach has potential use for the development of new precision processing tools.
文摘The application and promotion of waste glass powder concrete(WGPC)cansignificantly alleviate the pressure of concrete material scarcity and environmental pollution.Compressive strength(CS)is a critical parameter for evaluating the efficacy of WGPC.Unlike conventional testing methods,machine learning techniques offer precise and reliable predictions of concrete’s compressive strength,especially in its long-term mechanical properties.In this work,four models,namely Multiple Linear Regression(MLR),Back Propagation Neural Network(BPNN),Support Vector Regression(SVR),and Random Forest Regression(RFR)were employed.Furthermore,particle swarm optimization(PSO)algorithm and cross-validation techniques were applied to fine-tune the model parameters,striving for peak prediction performance.The results indicated that optimized models generally exhibit enhanced predictive accuracy compared to their basic counterparts.Notably,the PSO-RFR model excels among all evaluated models,showcasing superior performance on the testing dataset.It achieves a coefficient of determination(R^(2))of 0.9231,a mean absolute error(MAE)of 2.1073,and a root mean square error(RMSE)of 3.6903.When compared to experimental results,the PSO-RFR and PSO-BPNN models demonstrate exceptional predictive accuracy.Notably,the PSO-BPNN model exhibits the closest R^(2)values between its training and test sets.This close alignment of R^(2)values between the training and testing sets reflects the PSO-BPNN model’s superior generalization ability for unseen data.The findings present an efficient method for predicting concrete’s compressive strength,contributing to the sustainable development of concrete materials,and providing theoretical support for their research and application.
文摘Kiehl and colleagues1 utilized data from the Osteoarthritis Initiative(OAI)to address a clinically significant question:Is lifetime participation in strength training(ST)associated with improved trajectories of pain,function,and mobility in individuals with knee osteoarthritis(OA)?Among 3192 participants,those classified as“Lifelong ST”(n=142)demonstrated superior 4-year patient-reported outcomes and exhibited the lowest incidence of mobility disability(0.8%vs 2.3%–4.1%).Notably,they also maintained the fastest walking speeds at Year 4.
基金supported by the Natural Science Foundation of China(Grant Nos.T2325013,52288102,52090024,12034009,12474004,and 12304036)the National Key R&D Program of China Grant No.2023YFA1610000+1 种基金the Fundamental Research Funds for the Central Universitiesthe Program for Jilin University and Sun Yat-sen University.
文摘We report first-principles predictions of a cage-like polymeric nitrogen phase(cage-N)composed of interlocked N10 clusters stabilized by mixed sp^(2)/sp^(3) hybridization.Under high pressure,cage-N exhibits exceptional mechanical performance,including an ideal compressive strength of 343 GPa at a pressure of 300 GPa,~33% higher than that of diamond.This ultrahigh strength arises from the synergistic interplay between its three-dimensional covalent framework and hybridized bonding topology,which enables isotropic stress accommodation and dynamic electronic rearrangement.These results establish cage-N as a promising non-carbon ultrahard material and provide a bonding-driven route toward designing superhard frameworks under extreme conditions.
文摘This study investigates the impacts of mixing time,execution procedure,cement dosage(α),and total water-to-cement ratio(W_(Total)/C)on the mixing energy(E)of deep soil mixing(DSM)columns and how E influences the strength of treated sand.Columns with a diameter of 7.5 cm were constructed using three mixing times(130,190,and 250 s),two execution procedures(normal and zigzag),threeαvalues(300,400,and 500 kg/m^(3)),and three W_(Total)/C ratios(2.5,3.0,and 3.5).For comparison,equivalent laboratory samples were also examined.Results revealed that increasing the mixing time andα,adopting the zigzag execution procedure,and reducing the W_(Total)/C ratio increase E.Outcomes indicated that an increase in E from 0.49-0.70 kJ to 0.70-0.90 kJ,0.90-1.10 kJ,and 1.10-1.40 kJ improves the unconfined compressive strength(UCS)of columns on average by 66%,124%,and 179%,respectively,and the secant modulus by 61%,110%,and 152%.Average strain at maximum stress also rises from 0.68%to 0.75%,0.81%,and 0.84%,respectively.The study identified a threshold in the direct relationship between E and the strength ratio(λ),beyond whichλdid not increase significantly with further increases in E.Additionally,at low and high E levels,DSM samples mainly failed by crushing and cracking modes,respectively.In DSM columns withα=500 kg/m^(3)and W_(Total)/C=2.5,increasing average E from 0.77 kJ to 0.95 kJ,1.08 kJ,and 1.28 kJ resulted in a reduction of coefficients of variation of UCS from 30.4%to 27.8%,24.5%,and 21.1%,respectively.
文摘Background This study compared knee osteoarthritis(OA)outcomes specific to pain,physical function,and quality of life in later life based on strength training(ST)participation over a lifetime.Methods Participants from the Osteoarthritis Initiative(n=3192)were grouped by ST engagement during ages 12–18 years,19–34 years,35–49 years,and 50+years.Participants were categorized as:No ST(no ST at any point;61.7±9.0 years(mean±SD)),Some ST(engaged in ST during 1–3 life stages;58.9±8.7 years),and Lifelong ST(consistently engaged in ST across all life stages;55.6±8.1 years).Measures were collected at baseline and Year 4:Western Ontario and McMaster Universities Osteoarthritis Index Scores(WOMAC;pain,daily activities),Knee Injury and Osteoarthritis Outcome Score(KOOS;sports,recreation),Physical Activity Score for the Elderly(PASE),Short Form-12 Physical Component Score(SF-12 PCS),mobility disability,chair rise time,and walking speed(20 m and 400 m).Results At Year 4,the Lifelong ST group reported better WOMAC activity scores in the right knee along with better WOMAC pain,KOOS sports/recreation,and PASE scores compared to other groups(p<0.05).The Lifelong ST group had the lowest incidence of mobility disability of all groups(0.8%vs.2.3%–4.1%;p=0.015)and maintained the fastest walking speeds in Year 4.Conclusion For those with knee OA,ST throughout life may help preserve function and mobility,allowing for greater physical activity engagement while keeping pain levels relatively lower.
文摘Received:06 December 2025;Accepted:25 February 2026;Published:30 March 2026 ABSTRACT:In the last decade,the importance of sustainable construction and artificial intelligence(AI)in civil engineering has been underlined in many studies.Numerous studies highlighted the superiority of AI techniques over simple and mathematical regression analyses,which suffer from relatively poor generalization and an inability to capture highly non-linear relationships among inputs and output(s)parameters.In this study,to evaluate the compressive strength of concrete with glass powder(GP)and recycled aggregates,600 concrete samples were tested in the laboratory,and their results were evaluated.For intelligent assessment of concrete compressive strength(CCS),the study utilized an improved artificial neural network(ANN)with particle swarm optimization(PSO)algorithm and imperialist competitive algorithm(ICA).For training the models,the experimentally obtained data were used.The concrete ingredients formed the inputs of the AI-based predictive models of CCS.The experimental findings reveal that the implementation of recycled coarse aggregates in concrete from a sustainable construction point of view is advantageous and can enhance the CCS by 11.43%.Apart from that,findings indicate that utilization of 10%GP can lead to a nearly 20%increase in CCS(from 44.6 to 54.1 MPa).Additionally,the experimental observations show almost 40%improvement of CCS when 5%micro silica was used in the concrete mixture.Based on the findings,the study suggests the utilization of waste glass powder to partially replace cement in concrete,which can reduce the amount of cement production.This reduction from economic,energy-saving,and environmental(reduction in greenhouse gas emissions)points of view is of interest.On the other hand,the AI results show that the PSO-based ANN model outperforms the ICA-based ANN for the utilized dataset.According to the findings,the PSO-based ANN predictive model(with a coefficient of determination value of 0.939 and root mean square value of 0.113 for testing data)is a capable tool in predicting the CCS.Hence,this study recommends the implementation of AI-based models in CCS assessment.
文摘Background Intrinsic and extrinsic foot muscles contribute to foot and lower limb function.This knowledge provides opportunities to target these muscles in exercise and rehabilitation.However,a barrier is the limited understanding of how prescribed exercises affect muscle activation and force output.This study examined muscle activation and torque production about the metatarsophalangeal joints of intrinsic and extrinsic foot muscles during common foot strengthening exercises.The secondary aim was to determine whether the addition of a postural change(leaning forward)or addition of mass can further increase muscle activation and torque.Methods Fifteen healthy participants(9 males,6 females;age=36.5±8.0 years,mean±SD)participated.Muscle activation was measured using fine-wire electromyography(EMG)electrodes for 2 intrinsic and 2 extrinsic muscles,and surface EMG electrodes for 4 superficial muscles.Metatarsophalangeal joint torque was measured using a custom toe dynamometer.Participants attended one 90-min session and performed 16 exercises.Five included the addition of leaning forward and 3 the addition of mass(20%of participant’s mass)to compare their effects on muscle activity and torque.Results Muscle activation and metatarsophalangeal joint torque varied considerably across exercises.Both the addition of leaning forward and the addition of mass increased muscle activation and metatarsophalangeal joint torque,with the addition of leaning forward producing a 35%median increase in torque about the metatarsophalangeal joints.Conclusion Many common exercises,despite high muscle activation,produce relatively low metatarsophalangeal joint torque,raising questions about their clinical value.Adding a forward lean significantly increases toe flexor muscle activity and torque,reaching levels greater than added mass and comparable to walking.
文摘Strain measurements during uniaxial compressive strength(UCS)testing and their subsequent interpretation to obtain elastic parameters are relatively straightforward for most rocks.However,for slates,which are foliated metamorphic rocks characterized by significant anisotropy,the dependence of elastic properties on the orientation of foliation complicates the measurement and interpretation of strain data.In this study,a series of wave propagation velocity tests and UCS tests are conducted on cylindrical and prismatic slate specimens to gain a better understanding of how to obtain and process deformability and strength results.Wave propagation velocity results demonstrate an increase with the dip of foliation planes crossed,which is consistent with previous studies.Based on UCS test results,two methodologies are considered for obtaining transversely isotropic deformability parameters:the least-squares method and the recently proposed generalized reduction gradient(GRG)algorithm.Their performance is assessed in the context of potentially variable and limited amounts of data.GRG algorithms provide an enhanced analysis technique for estimating anisotropic elastic properties when dealing with limited or heterogeneous laboratory test data.Different strength models have also been considered,including the classic Jaeger's weakness plane(JPW)and its subsequent modification,i.e.2HBJPW.The 2HBJPW approach has proven to be more consistent with the obtained results and enhances the representation of the strength properties of slates.Additionally,a finite element method(FEM)numerical approach is employed to compare results with analytical and experimental ones,demonstrating a good match,thereby offering calibrated inputs for rock engineering applications.
文摘China has set its economic growth target for 2026 in the range of 4.5-5 percent.After three consecutive years of aiming for growth of around 5 percent,the slightly adjusted range reflects a prudent assessment of a complex global environment while still signalling a commitment to stable development.
文摘In complex geological environments,the analysis of drill cores to determine rock strength can be challenging due to the wide variability in the degree of fracturing,leading to subjectivity in the collection of representative samples for uniaxial compressive strength testing.This study evaluates non-destructive techniques on calcareous rocks with different tectonic deformations,including Equotip hardness,ultrasound P-wave velocity,thin section analysis,and calcimetry,integrated with photogrammetric fracture analysis.The investigated carbonate rock samples are sourced from drill cores derived from the Umbria-Marche fold and thrust belt(northern Apennines,Italy),including a gently dipping limb of an anticline,a hinge zone of an anticline,and a fault zone associated with a thrust.Fracture intensity,quantified by the P21 parameter using photogrammetric techniques on pre-loading rock samples,is assessed alongside macroscopic identification of discontinuities(such as stylolites,veins,and joints)using marker colours to monitor failures during uniaxial compression testing.Empirical correlations depicted by single and multi-linear relationships indicate a strong dependence between the mechanical and physical properties of limestones.Both Equotip and P-wave velocity are influenced by fracture intensity,but P-wave velocity varies significantly with discontinuity orientation,especially at 45°-90°.To refine uniaxial compressive strength predictions and mitigate multicollinearity,statistical approaches,including linear and multilinear regression,Principal Component Analysis and Gaussian Process Regression,were tested.Findings improve the reliability of non-destructive techniques for assessing rock strength in structurally complex settings,with implications for geotechnical applications.
基金financial support from the National Key Research and Development Program of China(No.2016YFB0300900)the National Key Fundamental Research Project of China(No.2012CB619506-3)the National Natural Science Foundation of China(No.51171209)。
文摘Texture and grain structure evolution during annealing and their effects on tensile strength and anisotropy were studied using XRD,DSC,SEM,EBSD and TEM.The results indicate that elevated rolling temperatures reduce the f(g)_(max(Copper))/f(g)_(max(Brass))ratio,increase S-Brass fine bands,and promote S-dispersoid precipitation,leading to finer recrystallized grains.Dominant recrystallization textures transform from Goss+P to Goss and then to Goss+Cube with increasing rolling temperature.Annealing at 350℃shows four tensile strength response stages:fast softening I,rapid strengthening II,slow strengthening III,and slow softening IV.The transition from Stages I to II is driven by the formation of strong Goss and P textures,and Stage IV is linked to enhanced Cube texture.Plates with Goss+Cube textures and fine equiaxed grains exhibit the lowest YS/UTS ratio and minimal anisotropy.
文摘Accurate prediction of concrete compressive strength is fundamental for optimizing mix designs,improving material utilization,and ensuring structural safety in modern construction.Traditional empirical methods often fail to capture the non-linear relationships among concrete constituents,especially with the growing use of supple-mentary cementitious materials and recycled aggregates.This study presents an integrated machine learning framework for concrete strength prediction,combining advanced regression models—namely CatBoost—with metaheuristic optimization algorithms,with a particular focus on the Somersaulting Spider Optimizer(SSO).A comprehensive dataset encompassing diverse mix proportions and material types was used to evaluate baseline machine learning models,including CatBoost,XGBoost,ExtraTrees,and RandomForest.Among these,CatBoost demonstrated superior accuracy across multiple performance metrics.To further enhance predictive capability,several bio-inspired optimizers were employed for hyperparameter tuning.The SSO-CatBoost hybrid achieved the lowest mean squared error and highest correlation coefficients,outperforming other metaheuristic approaches such as Genetic Algorithm,Particle Swarm Optimization,and Grey Wolf Optimizer.Statistical significance was established through Analysis of Variance and Wilcoxon signed-rank testing,confirming the robustness of the optimized models.The proposed methodology not only delivers improved predictive performance but also offers a transparent framework for mix design optimization,supporting data-driven decision making in sustainable and resilient infrastructure development.
基金support from the Postgraduate Research&Practice Innovation Program of Jiangsu Province,China(Grant No.KYCX24_2822)the Graduate Innovation Program of China University of Mining and Technology(Grant No.2024WLKXJ205)the National Natural Science Foundation of China(Grant No.52474157).
文摘Excavation causes stress redistribution and affects the stress path during the shearing process of rock.The shear strength of rock varies under different stress paths,and the presence of defects reduces the shear strength.To further investigate this phenomenon,this study investigates the shear behaviour of rocks with different shear surface integrities under the influenceof different stress paths through laboratory tests and numerical simulations.The results indicate that the shear strength depends on the stress path and a decrease in the shear surface integrity reduces the degree of dependence.The cohesion and friction angle of the Mohr‒Coulomb criterion decrease with weakening of the shear surface integrity.For different stress paths,the direct shear strength is always greater than that of other shear stress paths.The pattern of changes in the acoustic emission count and cumulative count indirectly reflectsthe above findings.Numerical simulations further indicate that the different principal stress states and normal suppression effects during the shearing process lead to changes in the factors of crack propagation,resulting in different mechanical behaviours under various stress paths.For rocks with different integrity levels,the main reason for the different path dependences of shear strength is that the size of the area affected by shear is different.Shear failure will concentrate on the shear plane when the normal inhibition effect is greater.This study explores the mechanism of rock shear behaviour,providing a theoretical basis for establishing more accurate constitutive models and strength criteria.
基金Funded by the Chongqing Natural Science Foundation Project(No.cstc202ljcyj-msxmX0725)。
文摘We employed machine learning approaches and visualization interpretation methods to explore the influencing factors of the compressive strength of sea sand concrete to attain a better understanding of the inherent laws of concrete mix design.Four models,including random forest,Cat Boost,XGBoost,and deep neural network,were trained.The experimental results demonstrate that the XGBoost model performs the best in predicting the strength of sea sand concrete.Its R^(2)value reached 0.9999,and evaluation indexes such as MAPE,RMSE,MAE,and MSE are superior to those of other models.The principal component analysis(PCA)was conducted to visually analyze the structure and distribution of the original feature data,and Pearson correlation coefficient analysis and Shapley additive explanation(SHAP)were utilized to explore the impact of input characteristics on the strength of sea sand concrete.SHAP analysis is more conducive to revealing the nonlinear effects of various characteristics on the model prediction results,especially that particle size of stone has significant impacts on the strength of sea sand concrete.In addition,experimental verification was carried out to confirm the accuracy of the optimized training model.These findings offer some insights for the future design and application of sea sand in high-performance marine and coastal infrastructure.
基金the Fundamental Research Funds for the Central Universities of Nanjing University of Science and Technology(CN)under Grant No.30924010803。
文摘The penetration of shaped charge jets into targets at high velocities is significantly influenced by the compressibility effect,while at low velocities,the strength effect becomes predominant.In the latter regime,material strength dictates the resistance to plastic deformation and flow,a contrast to the shockwave-dominated interactions where compressibility is key.This paper presents a self-consistent compressible penetration theory that considers both the axial penetration and radial crater growth of shaped charge jets into targets.An integrated approach where the axial and radial dynamics are coupled has been proposed,influencing each other through shared physical principles rather than being treated as separate,empirically linked phenomena.The presented theory is rooted in the compressible Bernoulli equation and the linear Rankine-Hugoniot relation.These foundational equations are employed to accurately model the high-pressure shock state and subsequent material flow at the jet-target interface,providing a robust physical basis for the penetration model.Notably,it considers the target material's compressibility,which elevates the pressure at the jet-target interface beyond that observed with incompressible materials.This pressure increase is directly proportional to the target's degree of compressibility.As such,this model of compressible penetration reorients the analytical approach:rather than merely estimating penetration resistance,it determines this value from the target material's specific compressibility and yield strength.This shift from empirical correlations to a physics-based derivation of penetration resistance enhances the model's predictive power,particularly for novel target materials or engagement conditions outside established experimental datasets.This investigation establishes a quantitative link between the material's yield strength and its penetration resistance.The accuracy of this penetration resistance value is paramount,as it significantly influences the predicted crater diameter;indeed,the crater diameter's sensitivity to this resistance underscores the necessity for its precise determination.Ultimately,by integrating the yield strength of the target material,this framework enables the prediction of both the penetration depth and the resultant crater diameter from a shaped charge jet.The theory's validation involved two experimental sets:the first focused on shaped charge jet penetration into 45#steel at varied stand-offs,while the second utilized targets of high-to ultrahigh-strength steel-fiber reactive powder concrete(RPC)with differing strength characteristics.These experimental campaigns were specifically chosen to test the theory against both ductile metallic alloys,where plastic flow is significant,and advanced quasi-brittle cementitious composites,presenting a broad spectrum of material responses and penetration challenges.Resulting hole profiles derived from theoretical calculations demonstrated a strong correspondence with empirical measurements for both material types.
文摘Purpose:ATLAS is a cross-sectional study aiming to investigate environmental and genetic determinants of athletic performance in healthy Greek competitive athletes(CA).This article presents the study design,investigates the muscle strength performance(MSP)of 289 adult and teenage CA,exercisers,and physically inactive individuals(PI),and proposes predictive models of MSP for adults.Methods:Muscle maximal,speed,and explosive strength(MMS/MSS/MES)at unilateral maximal concentric flexion and extension contraction(FC/EC)were evaluated using Biodex System 3 PRO^(TM)at 60°/s,180°/s,and 300°/s,while additional performance markers were assessed through field ergometric testing.Participants were interviewed about their lifestyle,dietary habits,physical activity,injury,and medical history.Body composition was assessed via bioelectrical impedance.gDNA was extracted from biochemical samples and then genotyped.Statistical analysis was conducted using IBM SPSS Statistics v21.0 and R.Results:Age,fitness,and sex impacted correlations of MSP with body composition and anthropometric measurements(p<0.05).Among CA,females outperformed males in accuracy(p<0.001)while,males outperformed females in anaerobic power,MSP,speed,and endurance(p<0.001).Adult CA outperformed exercisers and PI in MMS,MSS,and MES(p<0.05).Multiple linear regression models,with predictors age,FFM,body extremity,training load explained the majority of variation in MMS(R^(2)_(adj):71.4%–88.9%),MSS(R^(2)_(adj):64.8%–78.4%),and MES(R^(2)_(adj):52.7%–68.4%)at EC,FC,and their mean(p<0.001).Conclusions:Muscle-strengthening strategies should be customized according to individual fitness levels,body composition,and anthropometric measurements.The innovative sex-specific regression models assessing MMS,MSS,and MES at EC and FC provide a framework for personalizing rehabilitation and skill-specific training strategies.
基金supported by the National Natural Science Foundation of China(52273059,52203066,52403046 and 52473219)the Science and Technology Plans of Tianjin(22JCYBJC01030)+3 种基金the Tianjin Natural Science Foundation(23JCYBJC00660)the Tianjin Enterprise Science and Technology Commissioner Project(23YDTPJC00490)the China Postdoctoral Science Foundation Grant(2023M742135,2024T170525)the State Key Laboratory of Membrane and Membrane Separation,Tiangong University.
文摘The low ionic conductivity and poor mechanical strength of polyethylene oxide(PEO)-based electrolytes severely restrict their practical application.To address this problem,this work designs a scalable,high-strength(24.3 MPa)bicontinuous porous poly(m-phthaloyl-m-phenylenediamine)(PMIA)membrane integrated into PEO/LiTFSI(PL),thus forming a PMIA/PEO/LiTFSI(PPL)composite electrolyte.Compared to the PL electrolyte,the PPL electrolyte reinforced by a bicontinuous porous PMIA membrane exhibits significantly enhanced mechanical strength,reaching 13.4 MPa.In addition,the amide groups on PMIA strongly coordinate with LiTFSI and form hydrogen bonds with PEO,promoting Li salt dissociation and reducing the Li^(+)migration barrier.This creates efficient,fast Li^(+)transport channels at the PMIA/PL interfaces,effectively promoting the uniform Li^(+)deposition and minimizing lithium dendrite formation.The PPL electrolyte achieves high ionic conductivity(1×10^(−4)S cm^(−1)at 30°C)and Li^(+)transference number(tLi^(+)=0.43).The assembled LiFePO_(4)/Li battery demonstrates excellent cycling stability,retaining 80%capacity after 2000 cycles at 2 C,while the Li/Li symmetric cell operates stably for over 900 h at 0.3 mA cm^(−2).Therefore,the scalable porous PMIA membrane effectively enhances both the mechanical strength and Li^(+)transport in PEO-based electrolytes,offering a viable strategy for their commercial-scale implementation.
基金sponsored by the General Program of the National Natural Science Foundation of China(Grant No.42407221)the Open Fund of State Key Laboratory of Geohazard Prevention and Geoenvironment Protection(Grant No.SKLGP2024K009)the Hubei Provincial Natural Science Foun-dation,China(Grant No.2023AFB567).
文摘Water storage in the Three Gorges Reservoir in China has increased the regional microseismicity.Bedding-rock landslides,one of the most common slope structures in the Three Gorges Reservoir,are highly prone to sliding under seismic loading.Existing research primarily focuses on the stability of bedding rock landslides under strong earthquakes,while studies on the cumulative damage and long-term stability of bedding rock landslides under high-frequency microseismicity remain immature.In this study,we considered bedding rock landslides under high-frequency microseismicity in the Three Gorges Reservoir area as the research subject and equivalent microseismicity as pre-peak cyclic loading.First,we analyzed the shear strength deterioration of rock mass structural planes under pre-peak cyclic loading conditions and found that the deformation and failure of structural planes involve contact and damage effects.The shear strength of the rock mass structural planes under pre-peak cyclic loading conditions is affected by the confining pressure,loading rate,loading amplitude,and number of loading cycles.Among these factors,the shear strength of the structural planes was the most sensitive to the number of loading cycles.As the number of cycles increased,the rock mass structural planes underwent three stages:stress adjustment(increase in shear strength),fatigue damage(gradual decrease in shear strength),and structural failure(rapid decrease in shear strength).The stability of bedding rock landslides under high-frequency microseismicity was analyzed,revealing that the stability of bedding rock landslides under high-frequency microseismicity can be divided into three stages:short-term enhancement,gradual degradation,and rapid deterioration,exhibiting characteristics of gradual and sudden changes.
