Teacher–student relationships play a vital role in improving college students’academic performance and the quality of higher education.However,empirical studies with substantial data-driven insights remain limited.T...Teacher–student relationships play a vital role in improving college students’academic performance and the quality of higher education.However,empirical studies with substantial data-driven insights remain limited.To address this gap,this study collected 3278 questionnaires from seven universities across four provinces in China to analyze the key factors affecting college students’academic performance.A machine learning framework,CQFOA-KELM,was developed by enhancing the Fruit Fly Optimization Algorithm(FOA)with Covariance Matrix Adaptation Evolution Strategy(CMAES)and Quadratic Approximation(QA).CQFOA significantly improved population diversity and was validated on the IEEE CEC2017 benchmark functions.The CQFOA-KELM model achieved an accuracy of 98.15%and a sensitivity of 98.53%in predicting college students’academic performance.Additionally,it effectively identified the key factors influencing academic performance through the feature selection process.展开更多
In the dynamic landscape of software technologies,the demand for sophisticated applications across diverse industries is ever⁃increasing.However,predicting software defects remains a crucial challenge for ensuring the...In the dynamic landscape of software technologies,the demand for sophisticated applications across diverse industries is ever⁃increasing.However,predicting software defects remains a crucial challenge for ensuring the resilience and dependability of software systems.This study presents a novel software defect prediction technique that significantly enhances performance through a hybrid machine learning approach.The innovative methodology integrates a Genetic Algorithm(GA)for precise feature selection,a Decision Tree(DT)for robust classification,and leverages the capabilities of Particle Swarm Optimization(PSO)and Ant Colony Optimization(ACO)algorithms for precision⁃driven optimization.The utilization of datasets from varied sources enriches the predictive prowess of our model.Of particular significance in our pursuit is the unwavering focus on enhancing the prediction process through a highly refined PSO⁃ACO algorithm,thereby optimizing the efficiency and effectiveness of the GA⁃DT hybrid model.The thorough evaluation of our proposed approach unfolds across seven software projects,unveiling a paradigm shift in performance metrics.Results unequivocally demonstrate that the GA⁃DT with PSO⁃ACO algorithm surpasses its counterparts,showcasing unparalleled accuracy and reliability.Furthermore,our hybrid approach demonstrates outstanding performance in terms of F⁃measure,with an impressive increase rate of 78%.展开更多
Memristors have emerged as a transformative technology in the realm of electronic devices,offering unique advantages such as fast switching speeds,low power consumption,and the ability to sensor-memory-compute.The app...Memristors have emerged as a transformative technology in the realm of electronic devices,offering unique advantages such as fast switching speeds,low power consumption,and the ability to sensor-memory-compute.The applications span across non-volatile memory,neuromorphic computing,hardware security,and beyond,prompting memristors to become a versatile solution for next-generation computing and data storage systems.Despite enormous potential of memristors,the transition from laboratory prototypes to large-scale applications is challenging in terms of material stability,device reproducibility,and array scalability.This review systematically explores recent advancements in high-performance memristor technologies,focusing on performance enhancement strategies through material engineering,structural design,pulse protocol optimization,and algorithm control.We provide an in-depth analysis of key performance metrics tailored to specific applications,including non-volatile memory,neuromorphic computing,and hardware security.Furthermore,we propose a co-design framework that integrates device-level optimizations with operational-level improvements,aiming to bridge the gap between theoretical models and practical implementations.展开更多
The adsorptive denitrification performance of MIL-101(Cr)-0.5 toward pyridine,aniline or quinoline in simulated fuels with basic nitrogen content of 1732μg/g was evaluated separately.Furthermore,the effects of adsorp...The adsorptive denitrification performance of MIL-101(Cr)-0.5 toward pyridine,aniline or quinoline in simulated fuels with basic nitrogen content of 1732μg/g was evaluated separately.Furthermore,the effects of adsorption temperature,adsorption time and adsorbent dosage on their adsorptive denitrification performance were systematically investigated.The experimental results demonstrated that under a fixed adsorbent dosage of 0.05 g and a simulated fuel volume of 10 mL,the optimal removal efficiency for aniline was achieved at 30℃ within 30 min,whereas higher temperatures and longer times(40℃and 40 min)were required for effective removal of pyridine and quinoline.Density Functional Theory(DFT)calculations were conducted via Materials Studio(MS)software to study the adsorptive denitrification mechanism of MIL-101(Cr)toward these three basic nitrogen-containing compounds.The simulation calculation results revealed that the interaction between pyridine and MIL-101(Cr)primarily involved coordination adsorption.In contrast,the interaction between aniline or quinoline and MIL-101(Cr)proceeded mainly through coordination,with additional contributions fromπ-complexation and hydrogen bonding.The overall adsorption strength order is pyridine>aniline>quinoline.During the adsorption process,pyridine and quinoline transfer electrons to the MIL-101(Cr)surface through the H→C→N→Cr^(3+)pathway,while aniline transfers electrons to the MIL-101(Cr)surface through various pathways,including N→Cr^(3+),N→C→Cr^(3+)and N→H→O.Furthermore,adsorption kinetics studies indicated that the adsorption processes for all three basic nitrogen-containing compounds followed the quasi second order kinetic models.The experimental results on the effect of benzene on the adsorptive denitrification performance of MIL-101(Cr)-0.5 demonstrated that benzene exerted a more significant impact on the adsorption of aniline and quinoline.Finally,the adsorbent was regenerated using ethanol washing.It was found that MIL-101(Cr)-0.5 retained stable denitrification performance after two regeneration cycles.展开更多
Refractory metals,including tungsten(W),tantalum(Ta),molybdenum(Mo),and niobium(Nb),play a vital role in industries,such as nuclear energy and aerospace,owing to their exceptional melting temperatures,thermal durabili...Refractory metals,including tungsten(W),tantalum(Ta),molybdenum(Mo),and niobium(Nb),play a vital role in industries,such as nuclear energy and aerospace,owing to their exceptional melting temperatures,thermal durability,and corrosion resistance.These metals have body-centered cubic crystal structure,characterized by limited slip systems and impeded dislocation motion,resulting in significant low-temperature brittleness,which poses challenges for the conventional processing.Additive manufacturing technique provides an innovative approach,enabling the production of intricate parts without molds,which significantly improves the efficiency of material usage.This review provides a comprehensive overview of the advancements in additive manufacturing techniques for the production of refractory metals,such as W,Ta,Mo,and Nb,particularly the laser powder bed fusion.In this review,the influence mechanisms of key process parameters(laser power,scan strategy,and powder characteristics)on the evolution of material microstructure,the formation of metallurgical defects,and mechanical properties were discussed.Generally,optimizing powder characteristics,such as sphericity,implementing substrate preheating,and formulating alloying strategies can significantly improve the densification and crack resistance of manufactured parts.Meanwhile,strictly controlling the oxygen impurity content and optimizing the energy density input are also the key factors to achieve the simultaneous improvement in strength and ductility of refractory metals.Although additive manufacturing technique provides an innovative solution for processing refractory metals,critical issues,such as residual stress control,microstructure and performance anisotropy,and process stability,still need to be addressed.This review not only provides a theoretical basis for the additive manufacturing of high-performance refractory metals,but also proposes forward-looking directions for their industrial application.展开更多
To elucidate the effect of calcite-regulated activated carbon(AC)structure on low-temperature denitrification performance of SCR catalysts,this work prepared a series of Mn-Ce/De-AC-xCaCO_(3)(x is the calcite content ...To elucidate the effect of calcite-regulated activated carbon(AC)structure on low-temperature denitrification performance of SCR catalysts,this work prepared a series of Mn-Ce/De-AC-xCaCO_(3)(x is the calcite content in coal)catalysts were prepared by the incipient wetness impregnation method,followed by acid washing to remove calcium-containing minerals.Comprehensive characterization and low-temperature denitrification tests revealed that calcite-induced structural modulation of coal-derived AC significantly enhances catalytic activity.Specifically,NO conversion increased from 88.3%of Mn-Ce/De-AC to 91.7%of Mn-Ce/De-AC-1CaCO_(3)(210℃).The improved SCR denitrification activity results from the enhancement of physicochemical properties including higher Mn^(4+)content and Ce^(4+)/Ce^(3+)ratio,an abundance of chemisorbed oxygen and acidic sites,which could strengthen the SCR reaction pathways(richer NH_(3)activated species and bidentate nitrate active species).Therefore,NO removal is enhanced.展开更多
Severe failures of nonstructural components have occurred during previous earthquakes.Claddings are one of the most widely used nonstructural component and are installed in many modern buildings;therefore,an evaluatio...Severe failures of nonstructural components have occurred during previous earthquakes.Claddings are one of the most widely used nonstructural component and are installed in many modern buildings;therefore,an evaluation of their seismic performance is important and cannot be ignored.To investigate the seismic performance of large-sized high performance concrete cladding(HPCC),a series of full-scale experimental tests were conducted using a unidirectional shaking table.A steel supporting frame was used to install the HPCCs and reproduce the effects of the building under earthquake.The tests were divided into two parts:in-plane(IP)testing and out-plane(OP)testing.Three recorded accelerograms,one artificial accelerogram,and one sinusoidal accelerogram were used to conduct the shaking table tests.The results show that the maximum recorded IP responses of acceleration and interstory drift ratio were 1.04 g and 1/97,while the OP responses were 1.02 g and 1/51.The HPCCs functioned well throughout the entire experimental protocol.The fundamental frequency of the HPCCs systems rarely changed after the tests.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
Optimization algorithms are crucial for solving NP-hard problems in engineering and computational sciences.Metaheuristic algorithms,in particular,have proven highly effective in complex optimization scenarios characte...Optimization algorithms are crucial for solving NP-hard problems in engineering and computational sciences.Metaheuristic algorithms,in particular,have proven highly effective in complex optimization scenarios characterized by high dimensionality and intricate variable relationships.The Mountain Gazelle Optimizer(MGO)is notably effective but struggles to balance local search refinement and global space exploration,often leading to premature convergence and entrapment in local optima.This paper presents the Improved MGO(IMGO),which integrates three synergistic enhancements:dynamic chaos mapping using piecewise chaotic sequences to boost explo-ration diversity;Opposition-Based Learning(OBL)with adaptive,diversity-driven activation to speed up convergence;and structural refinements to the position update mechanisms to enhance exploitation.The IMGO underwent a comprehensive evaluation using 52 standardised benchmark functions and seven engineering optimization problems.Benchmark evaluations showed that IMGO achieved the highest rank in best solution quality for 31 functions,the highest rank in mean performance for 18 functions,and the highest rank in worst-case performance for 14 functions among 11 competing algorithms.Statistical validation using Wilcoxon signed-rank tests confirmed that IMGO outperformed individual competitors across 16 to 50 functions,depending on the algorithm.At the same time,Friedman ranking analysis placed IMGO with an average rank of 4.15,compared to the baseline MGO’s 4.38,establishing the best overall performance.The evaluation of engineering problems revealed consistent improvements,including an optimal cost of 1.6896 for the welded beam design vs.MGO’s 1.7249,a minimum cost of 5885.33 for the pressure vessel design vs.MGO’s 6300,and a minimum weight of 2964.52 kg for the speed reducer design vs.MGO’s 2990.00 kg.Ablation studies identified OBL as the strongest individual contributor,whereas complete integration achieved superior performance through synergistic interactions among components.Computational complexity analysis established an O(T×N×5×f(P))time complexity,representing a 1.25×increase in fitness evaluation relative to the baseline MGO,validating the favorable accuracy-efficiency trade-offs for practical optimization applications.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
The cloud-fog computing paradigm has emerged as a novel hybrid computing model that integrates computational resources at both fog nodes and cloud servers to address the challenges posed by dynamic and heterogeneous c...The cloud-fog computing paradigm has emerged as a novel hybrid computing model that integrates computational resources at both fog nodes and cloud servers to address the challenges posed by dynamic and heterogeneous computing networks.Finding an optimal computational resource for task offloading and then executing efficiently is a critical issue to achieve a trade-off between energy consumption and transmission delay.In this network,the task processed at fog nodes reduces transmission delay.Still,it increases energy consumption,while routing tasks to the cloud server saves energy at the cost of higher communication delay.Moreover,the order in which offloaded tasks are executed affects the system’s efficiency.For instance,executing lower-priority tasks before higher-priority jobs can disturb the reliability and stability of the system.Therefore,an efficient strategy of optimal computation offloading and task scheduling is required for operational efficacy.In this paper,we introduced a multi-objective and enhanced version of Cheeta Optimizer(CO),namely(MoECO),to jointly optimize the computation offloading and task scheduling in cloud-fog networks to minimize two competing objectives,i.e.,energy consumption and communication delay.MoECO first assigns tasks to the optimal computational nodes and then the allocated tasks are scheduled for processing based on the task priority.The mathematical modelling of CO needs improvement in computation time and convergence speed.Therefore,MoECO is proposed to increase the search capability of agents by controlling the search strategy based on a leader’s location.The adaptive step length operator is adjusted to diversify the solution and thus improves the exploration phase,i.e.,global search strategy.Consequently,this prevents the algorithm from getting trapped in the local optimal solution.Moreover,the interaction factor during the exploitation phase is also adjusted based on the location of the prey instead of the adjacent Cheetah.This increases the exploitation capability of agents,i.e.,local search capability.Furthermore,MoECO employs a multi-objective Pareto-optimal front to simultaneously minimize designated objectives.Comprehensive simulations in MATLAB demonstrate that the proposed algorithm obtains multiple solutions via a Pareto-optimal front and achieves an efficient trade-off between optimization objectives compared to baseline methods.展开更多
Wireless Sensor Networks(WSNs)have become foundational in numerous real-world applications,ranging from environmental monitoring and industrial automation to healthcare systems and smart city development.As these netw...Wireless Sensor Networks(WSNs)have become foundational in numerous real-world applications,ranging from environmental monitoring and industrial automation to healthcare systems and smart city development.As these networks continue to grow in scale and complexity,the need for energy-efficient,scalable,and robust communication protocols becomes more critical than ever.Metaheuristic algorithms have shown significant promise in addressing these challenges,offering flexible and effective solutions for optimizing WSN performance.Among them,the Grey Wolf Optimizer(GWO)algorithm has attracted growing attention due to its simplicity,fast convergence,and strong global search capabilities.Accordingly,this survey provides an in-depth review of the applications of GWO and its variants for clustering,multi-hop routing,and hybrid cluster-based routing in WSNs.We categorize and analyze the existing GWO-based approaches across these key network optimization tasks,discussing the different problem formulations,decision variables,objective functions,and performance metrics used.In doing so,we examine standard GWO,multi-objective GWO,and hybrid GWO models that incorporate other computational intelligence techniques.Each method is evaluated based on how effectively it addresses the core constraints of WSNs,including energy consumption,communication overhead,and network lifetime.Finally,this survey outlines existing gaps in the literature and proposes potential future research directions aimed at enhancing the effectiveness and real-world applicability of GWO-based techniques for WSN clustering and routing.Our goal is to provide researchers and practitioners with a clear,structured understanding of the current state of GWO in WSNs and inspire further innovation in this evolving field.展开更多
The rapid advancements in computer vision(CV)technology have transformed the traditional approaches to material microstructure analysis.This review outlines the history of CV and explores the applications of deep-lear...The rapid advancements in computer vision(CV)technology have transformed the traditional approaches to material microstructure analysis.This review outlines the history of CV and explores the applications of deep-learning(DL)-driven CV in four key areas of materials science:microstructure-based performance prediction,microstructure information generation,microstructure defect detection,and crystal structure-based property prediction.The CV has significantly reduced the cost of traditional experimental methods used in material performance prediction.Moreover,recent progress made in generating microstructure images and detecting microstructural defects using CV has led to increased efficiency and reliability in material performance assessments.The DL-driven CV models can accelerate the design of new materials with optimized performance by integrating predictions based on both crystal and microstructural data,thereby allowing for the discovery and innovation of next-generation materials.Finally,the review provides insights into the rapid interdisciplinary developments in the field of materials science and future prospects.展开更多
In composite solid propellants with high aluminum(Al)content and low burning rate,incomplete combustion of the Al powder may occur.In this study,varying lithium(Li)content in Al-Li alloy powder was utilized instead of...In composite solid propellants with high aluminum(Al)content and low burning rate,incomplete combustion of the Al powder may occur.In this study,varying lithium(Li)content in Al-Li alloy powder was utilized instead of pure aluminum particles to mitigate agglomeration and enhance the combustion efficiency of solid propellants(Combustion efficiency herein refers to the completeness of metallic fuel oxidation,quantified as the ratio of actual-to-theoretical energy released during combustion)with high Al content and low burning rates.The impact of Al-Li alloy with different Li contents on combustion and agglomeration of solid propellant was investigated using explosion heat,combustion heat,differential thermal analysis(DTA),thermos-gravimetric analysis(TG),dynamic high-pressure combustion test,ignition experiment of small solid rocket motor(SRM)tests,condensation combustion product collection,and X-ray diffraction techniques(XRD).Compared with pure Al,Al-Li alloys exhibit higher combustion heat,which contributes to improved combustion efficiency in Al-Li alloy-containing propellants.DTA and TG analyses demonstrated higher reactivity and lower ignition temperatures for Al-Li alloys.High-pressure combustion experiments at 5 MPa showed that Al-Li alloy fuel significantly decreases combustion agglomeration.The results from theφ75 mm andφ165 mm SRM and XRD tests further support this finding.This study provides novel insights into the combustion and agglomeration behaviors of high-Al,low-burning-rate composite solid propellants and supports the potential application of Al-Li alloys in advanced propellant formulations.展开更多
In the context of the revolution in new technologies,a key question is whether the rapid growth of the digital economy,driven by digital technologies,has improved regional innovation performance.Using inter-provincial...In the context of the revolution in new technologies,a key question is whether the rapid growth of the digital economy,driven by digital technologies,has improved regional innovation performance.Using inter-provincial panel data from China(2012–2022)and adopting a business environment perspective,this study applies a Panel Extended Regression Model(PERM),a Panel Simultaneous Equation Model(PSEM),and a Tobit-IV model to analyze how the development of the digital economy influences regional innovation.The results reveal a pronounced U-shaped relationship between the digital economy and the regional innovation performance at the provincial level in China,with the business environment serving as a significant mediator in this relationship.Moreover,regional innovation performance in China exhibits a“ratchet effect,”with the impact of the digital economy varying markedly across regions.While the eastern and western regions have entered an upward phase,whereby the digital economy boosts innovation,the central region displays a weaker effect.Further analysis indicates that the synergy between the business environment and the digital economy in driving innovation remains suboptimal.These findings were supported by robust checks.This study offers theoretical insights and empirical evidence that support the coordinated development of digital government and the digital factor market,as well as business environment reforms that are in alignment with the innovation demands of the digital era.展开更多
This work examines the microstructure and corrosion properties of fine-grained Al 7075 across different regions under varying cooling conditions during friction stir welding.The findings demonstrate that forced coolin...This work examines the microstructure and corrosion properties of fine-grained Al 7075 across different regions under varying cooling conditions during friction stir welding.The findings demonstrate that forced cooling significantly improves the corrosion resistance of the welded joints.Specifically,the corrosion resistance was the highest in the stir zone,followed by the thermo-mechanical affected zone,and then the heat affected zone.Forced cooling mitigates grain growth by controlling the welding thermal effects,thereby increasing the proportion ofΣ3 grain boundaries.The modification of these microstructural characteristics promotes the formation of a dense oxide layer,thereby enhancing the corrosion resistance.Furthermore,forced cooling mitigates the precipitation and coarsening of the anodic phase in the stir zone,which in turn reduces the susceptibility of the joint to pitting corrosion.Additionally,the lower recrystallization texture content in the joint,resulting from forced cooling,contributes to a reduction in the number of corrosion-active sites,thereby further improving the corrosion performance of the welded joint.展开更多
Al/NH_(4)CoF_(3)-Φ(Φ=0.5,1.0,1.5,2.0,and 3.0)binary composites and Al-NH_(4)CoF_(3)@P(VDF-HFP)ternary composites are fabricated via ultrasonication-assisted blending and electrostatic spraying.The effect of equivale...Al/NH_(4)CoF_(3)-Φ(Φ=0.5,1.0,1.5,2.0,and 3.0)binary composites and Al-NH_(4)CoF_(3)@P(VDF-HFP)ternary composites are fabricated via ultrasonication-assisted blending and electrostatic spraying.The effect of equivalence ratio(Φ)on the reaction properties is systematically investigated in the binary Al/NH_(4)CoF_(3)system.For ternary systems,electrostatic spraying allows both components to be efficiently encapsulated by P(VDF-HFP)and to achieve structural stabilization and enhanced reactivity through synergistic interfacial interactions.Morphological analysis using SEM/TEM revealed that P(VDF-HFP)formed a protective layer on Al and NH_(4)CoF_(3)particles,improving dispersion,hydrophobicity(water contact angle increased by 80.5%compared to physically mixed composites),and corrosion resistance.Thermal decomposition of NH_(4)CoF_(3)occurred at 265℃,releasing NH_(3)and HF,which triggered exothermic reactions with Al.The ternary composites exhibited a narrowed main reaction temperature range and concentrated heat release,attributed to improved interfacial contact and polymer decomposition.Combustion tests demonstrated that Al-NH_(4)CoF_(3)@P(VDF-HFP)achieved self-sustaining combustion.In addition,a simple validation was done by replacing the Al component in the aluminium-containing propellant,demonstrating its potential application in the propellant field.This work establishes a novel strategy for designing stable,high-energy composites with potential applications in advanced propulsion systems.展开更多
Isolation technology can reduce the type of structural damage that earthquakes cause.A new type of composite sliding-rolling friction composite seismic isolation bearing(SRF)with composite sliding friction and rolling...Isolation technology can reduce the type of structural damage that earthquakes cause.A new type of composite sliding-rolling friction composite seismic isolation bearing(SRF)with composite sliding friction and rolling friction is proposed.SRF is capable of realizing a parallel arrangement of sliding friction and rolling friction,and the coefficient of dynamic friction shows variability.The proposed static tests on composite bearings were conducted to investigate the effects of the number of shims,loading speed and vertical pressure on the dynamic friction factor.Test results show that the coefficient of dynamic friction first generally decreases and then increases with an increase in sliding speed,prior to again decreasing with an increase in vertical pressure.The dynamic friction factor increases and then decreases with an increase in the number of shims for a four-roll ball.It decreases and then increases with an increase in the number of shims for a five-roll ball.Based on finite element analysis,modeling and analyzing the effects of the coefficient of friction,the number of balls and the number of shims on the hysteresis performance of the support and derive its skeleton curve.The SRF hysteretic performance,dynamic friction factor and the number of rolling balls and shims show significant correlation.展开更多
Background The decline in reproductive performance of aged hens is mainly attributed to oxidative damage in reproductive organs,hepatic lipid metabolism disorders,and intestinal microbiota dysbiosis.Glycyrrhizin(GL)ha...Background The decline in reproductive performance of aged hens is mainly attributed to oxidative damage in reproductive organs,hepatic lipid metabolism disorders,and intestinal microbiota dysbiosis.Glycyrrhizin(GL)has been proven to enhance antioxidant capacity,regulate lipid metabolism and gut microbiota in mammals,but its efficacy in hens remains unclear.Hence,this study aimed to investigate whether dietary GL supplementation improves reproductive performance in hens during the late laying stage by modulating intestinal microbiota composition,hepatic lipid metabolism and ovarian antioxidant status.Results Dietary supplementation with 100 mg/kg GL significantly improved the egg production rate,egg quality,and hatching rate in aged breeder hens(P<0.05).GL supplementation also increased the serum levels of HDLC,TP and ALB,and enhanced the antioxidant capacity in both serum and ovary(P<0.05).In addition,dietary GL elevated the serum progesterone(P4)levels by enhancing the transcription level of steroid synthesis key enzymes(CYP11A1 and 3β-HSD)in the ovary(P<0.05).Dietary GL also promoted the synthesis and transport of vitellogenin(VTG)by upregulating the VTG-Ⅱ(P<0.05)and APOV1(P=0.077)expression levels in the liver,thereby increasing the number of grade follicles and small yellow follicles.Moreover,dietary GL enhanced hepatic fatty acidβ-oxidation by upregulating PPARαand CPT-I(P<0.05),and downregulating ACC expression levels(P<0.05).In agreement,liver metabolomics analysis revealed that dietary GL supplementation significantly altered hepatic metabolism,with 389 differentially identified metabolites(P<0.05).The key metabolites(e.g.,taurocholic acid,tauroursodeoxycholic acid,nicotinuric acid,glycodeoxycholic acid(hydrate))were identified,and they were mainly functionally enriched in betaalanine metabolism nicotinate,taurine and hypotaurine metabolism(P<0.05).Finally,16S rRNA gene sequencing revealed that dietary GL reversed age-induced changes in gut microbiota composition,characterized by a significant increase in Lactobacillus abundance and a decrease in Bacteroides(P<0.05).Conclusions These results collectively demonstrate that dietary supplementation with 100 mg/kg GL improved reproductive performance by reversing age-induced changes in gut microbiota,enhancing hepatic vitellogenin synthesis,and ameliorating ovarian function in aged breeder hens.This study suggests that dietary GL is a potential strategy to improve reproductive performance in broiler breeder hens during the late laying period.展开更多
The core components of an aircraft and the source of its lift are its wings,but lift generation is disrupted by the high temperature and pressure generated on the wing surface when an aircraft gun is fired.Here,to inv...The core components of an aircraft and the source of its lift are its wings,but lift generation is disrupted by the high temperature and pressure generated on the wing surface when an aircraft gun is fired.Here,to investigate how this process influences the aerodynamic parameters of aircraft wings,the k-ωshearstress-transport turbulence model and the nested dynamic grid technique are used to analyze numerically the transient process of the muzzle jet of a 30-mm small-caliber aircraft gun in highaltitude(10 km)flight with an incoming Mach number of Ma=0.8.For comparison,two other models are established,one with no projectile and the other with no wing.The results indicate that when the aircraft gun is fired,the muzzle jet acts on the wing,creating a pressure field thereon.The uneven distribution of high pressure greatly reduces the lift of the aircraft,causing oscillations in its drag and disrupting its dynamic balance,thereby affecting its flight speed and attitude.Meanwhile,the muzzle jet is obstructed by the wing,and its flow field is distorted and deformed,developing upward toward the wing.Because of the influence of the incoming flow,the shockwave front of the projectile changes from a smooth spherical shape to an irregular one,and the motion parameters of the projectile are also greatly affected by oscillations.The present results provide an important theoretical basis for how the guns of fighter aircraft influence the aerodynamic performance of the wings.展开更多
文摘Teacher–student relationships play a vital role in improving college students’academic performance and the quality of higher education.However,empirical studies with substantial data-driven insights remain limited.To address this gap,this study collected 3278 questionnaires from seven universities across four provinces in China to analyze the key factors affecting college students’academic performance.A machine learning framework,CQFOA-KELM,was developed by enhancing the Fruit Fly Optimization Algorithm(FOA)with Covariance Matrix Adaptation Evolution Strategy(CMAES)and Quadratic Approximation(QA).CQFOA significantly improved population diversity and was validated on the IEEE CEC2017 benchmark functions.The CQFOA-KELM model achieved an accuracy of 98.15%and a sensitivity of 98.53%in predicting college students’academic performance.Additionally,it effectively identified the key factors influencing academic performance through the feature selection process.
文摘In the dynamic landscape of software technologies,the demand for sophisticated applications across diverse industries is ever⁃increasing.However,predicting software defects remains a crucial challenge for ensuring the resilience and dependability of software systems.This study presents a novel software defect prediction technique that significantly enhances performance through a hybrid machine learning approach.The innovative methodology integrates a Genetic Algorithm(GA)for precise feature selection,a Decision Tree(DT)for robust classification,and leverages the capabilities of Particle Swarm Optimization(PSO)and Ant Colony Optimization(ACO)algorithms for precision⁃driven optimization.The utilization of datasets from varied sources enriches the predictive prowess of our model.Of particular significance in our pursuit is the unwavering focus on enhancing the prediction process through a highly refined PSO⁃ACO algorithm,thereby optimizing the efficiency and effectiveness of the GA⁃DT hybrid model.The thorough evaluation of our proposed approach unfolds across seven software projects,unveiling a paradigm shift in performance metrics.Results unequivocally demonstrate that the GA⁃DT with PSO⁃ACO algorithm surpasses its counterparts,showcasing unparalleled accuracy and reliability.Furthermore,our hybrid approach demonstrates outstanding performance in terms of F⁃measure,with an impressive increase rate of 78%.
基金supported by the National Key R&D Project from the Minister of Science and Technology(2024YFA1211500)the National Natural Science Foundation of China(Grant Nos.62304130,62405158 and 62574123)+1 种基金the Shanghai youth science and technology star project(24QA2702800)Shanghai Key Laboratory of Chips and Systems for Intelligent Connected Vehicle。
文摘Memristors have emerged as a transformative technology in the realm of electronic devices,offering unique advantages such as fast switching speeds,low power consumption,and the ability to sensor-memory-compute.The applications span across non-volatile memory,neuromorphic computing,hardware security,and beyond,prompting memristors to become a versatile solution for next-generation computing and data storage systems.Despite enormous potential of memristors,the transition from laboratory prototypes to large-scale applications is challenging in terms of material stability,device reproducibility,and array scalability.This review systematically explores recent advancements in high-performance memristor technologies,focusing on performance enhancement strategies through material engineering,structural design,pulse protocol optimization,and algorithm control.We provide an in-depth analysis of key performance metrics tailored to specific applications,including non-volatile memory,neuromorphic computing,and hardware security.Furthermore,we propose a co-design framework that integrates device-level optimizations with operational-level improvements,aiming to bridge the gap between theoretical models and practical implementations.
基金Supported by Basic Scientific Research Project of the Liaoning Provincial Department of Education Has Been Unveiled to Facilitate Local Project Funding (JYTMS20230835)Enhanced Scientific Research Project Funded by the Departmentof Higher Education in Liaoning Province (General program)(JYTMS20230852)。
文摘The adsorptive denitrification performance of MIL-101(Cr)-0.5 toward pyridine,aniline or quinoline in simulated fuels with basic nitrogen content of 1732μg/g was evaluated separately.Furthermore,the effects of adsorption temperature,adsorption time and adsorbent dosage on their adsorptive denitrification performance were systematically investigated.The experimental results demonstrated that under a fixed adsorbent dosage of 0.05 g and a simulated fuel volume of 10 mL,the optimal removal efficiency for aniline was achieved at 30℃ within 30 min,whereas higher temperatures and longer times(40℃and 40 min)were required for effective removal of pyridine and quinoline.Density Functional Theory(DFT)calculations were conducted via Materials Studio(MS)software to study the adsorptive denitrification mechanism of MIL-101(Cr)toward these three basic nitrogen-containing compounds.The simulation calculation results revealed that the interaction between pyridine and MIL-101(Cr)primarily involved coordination adsorption.In contrast,the interaction between aniline or quinoline and MIL-101(Cr)proceeded mainly through coordination,with additional contributions fromπ-complexation and hydrogen bonding.The overall adsorption strength order is pyridine>aniline>quinoline.During the adsorption process,pyridine and quinoline transfer electrons to the MIL-101(Cr)surface through the H→C→N→Cr^(3+)pathway,while aniline transfers electrons to the MIL-101(Cr)surface through various pathways,including N→Cr^(3+),N→C→Cr^(3+)and N→H→O.Furthermore,adsorption kinetics studies indicated that the adsorption processes for all three basic nitrogen-containing compounds followed the quasi second order kinetic models.The experimental results on the effect of benzene on the adsorptive denitrification performance of MIL-101(Cr)-0.5 demonstrated that benzene exerted a more significant impact on the adsorption of aniline and quinoline.Finally,the adsorbent was regenerated using ethanol washing.It was found that MIL-101(Cr)-0.5 retained stable denitrification performance after two regeneration cycles.
基金National MCF Energy R&D Program(2024YFE03260300)。
文摘Refractory metals,including tungsten(W),tantalum(Ta),molybdenum(Mo),and niobium(Nb),play a vital role in industries,such as nuclear energy and aerospace,owing to their exceptional melting temperatures,thermal durability,and corrosion resistance.These metals have body-centered cubic crystal structure,characterized by limited slip systems and impeded dislocation motion,resulting in significant low-temperature brittleness,which poses challenges for the conventional processing.Additive manufacturing technique provides an innovative approach,enabling the production of intricate parts without molds,which significantly improves the efficiency of material usage.This review provides a comprehensive overview of the advancements in additive manufacturing techniques for the production of refractory metals,such as W,Ta,Mo,and Nb,particularly the laser powder bed fusion.In this review,the influence mechanisms of key process parameters(laser power,scan strategy,and powder characteristics)on the evolution of material microstructure,the formation of metallurgical defects,and mechanical properties were discussed.Generally,optimizing powder characteristics,such as sphericity,implementing substrate preheating,and formulating alloying strategies can significantly improve the densification and crack resistance of manufactured parts.Meanwhile,strictly controlling the oxygen impurity content and optimizing the energy density input are also the key factors to achieve the simultaneous improvement in strength and ductility of refractory metals.Although additive manufacturing technique provides an innovative solution for processing refractory metals,critical issues,such as residual stress control,microstructure and performance anisotropy,and process stability,still need to be addressed.This review not only provides a theoretical basis for the additive manufacturing of high-performance refractory metals,but also proposes forward-looking directions for their industrial application.
基金Supported by the Science and Technology Cooperation and Exchange special project of Cooperation of Shanxi Province(202404041101014)the Fundamental Research Program of Shanxi Province(202403021212333)+3 种基金the Joint Funds of the National Natural Science Foundation of China(U24A20555)the Lvliang Key R&D of University-Local Cooperation(2023XDHZ10)the Initiation Fund for Doctoral Research of Taiyuan University of Science and Technology(20242026)the Outstanding Doctor Funding Award of Shanxi Province(20242080).
文摘To elucidate the effect of calcite-regulated activated carbon(AC)structure on low-temperature denitrification performance of SCR catalysts,this work prepared a series of Mn-Ce/De-AC-xCaCO_(3)(x is the calcite content in coal)catalysts were prepared by the incipient wetness impregnation method,followed by acid washing to remove calcium-containing minerals.Comprehensive characterization and low-temperature denitrification tests revealed that calcite-induced structural modulation of coal-derived AC significantly enhances catalytic activity.Specifically,NO conversion increased from 88.3%of Mn-Ce/De-AC to 91.7%of Mn-Ce/De-AC-1CaCO_(3)(210℃).The improved SCR denitrification activity results from the enhancement of physicochemical properties including higher Mn^(4+)content and Ce^(4+)/Ce^(3+)ratio,an abundance of chemisorbed oxygen and acidic sites,which could strengthen the SCR reaction pathways(richer NH_(3)activated species and bidentate nitrate active species).Therefore,NO removal is enhanced.
基金National Key R&D Program of China under Grant No.2024YFD1600404。
文摘Severe failures of nonstructural components have occurred during previous earthquakes.Claddings are one of the most widely used nonstructural component and are installed in many modern buildings;therefore,an evaluation of their seismic performance is important and cannot be ignored.To investigate the seismic performance of large-sized high performance concrete cladding(HPCC),a series of full-scale experimental tests were conducted using a unidirectional shaking table.A steel supporting frame was used to install the HPCCs and reproduce the effects of the building under earthquake.The tests were divided into two parts:in-plane(IP)testing and out-plane(OP)testing.Three recorded accelerograms,one artificial accelerogram,and one sinusoidal accelerogram were used to conduct the shaking table tests.The results show that the maximum recorded IP responses of acceleration and interstory drift ratio were 1.04 g and 1/97,while the OP responses were 1.02 g and 1/51.The HPCCs functioned well throughout the entire experimental protocol.The fundamental frequency of the HPCCs systems rarely changed after the tests.
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.
文摘Optimization algorithms are crucial for solving NP-hard problems in engineering and computational sciences.Metaheuristic algorithms,in particular,have proven highly effective in complex optimization scenarios characterized by high dimensionality and intricate variable relationships.The Mountain Gazelle Optimizer(MGO)is notably effective but struggles to balance local search refinement and global space exploration,often leading to premature convergence and entrapment in local optima.This paper presents the Improved MGO(IMGO),which integrates three synergistic enhancements:dynamic chaos mapping using piecewise chaotic sequences to boost explo-ration diversity;Opposition-Based Learning(OBL)with adaptive,diversity-driven activation to speed up convergence;and structural refinements to the position update mechanisms to enhance exploitation.The IMGO underwent a comprehensive evaluation using 52 standardised benchmark functions and seven engineering optimization problems.Benchmark evaluations showed that IMGO achieved the highest rank in best solution quality for 31 functions,the highest rank in mean performance for 18 functions,and the highest rank in worst-case performance for 14 functions among 11 competing algorithms.Statistical validation using Wilcoxon signed-rank tests confirmed that IMGO outperformed individual competitors across 16 to 50 functions,depending on the algorithm.At the same time,Friedman ranking analysis placed IMGO with an average rank of 4.15,compared to the baseline MGO’s 4.38,establishing the best overall performance.The evaluation of engineering problems revealed consistent improvements,including an optimal cost of 1.6896 for the welded beam design vs.MGO’s 1.7249,a minimum cost of 5885.33 for the pressure vessel design vs.MGO’s 6300,and a minimum weight of 2964.52 kg for the speed reducer design vs.MGO’s 2990.00 kg.Ablation studies identified OBL as the strongest individual contributor,whereas complete integration achieved superior performance through synergistic interactions among components.Computational complexity analysis established an O(T×N×5×f(P))time complexity,representing a 1.25×increase in fitness evaluation relative to the baseline MGO,validating the favorable accuracy-efficiency trade-offs for practical optimization applications.
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appreciation to the Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R384)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘The cloud-fog computing paradigm has emerged as a novel hybrid computing model that integrates computational resources at both fog nodes and cloud servers to address the challenges posed by dynamic and heterogeneous computing networks.Finding an optimal computational resource for task offloading and then executing efficiently is a critical issue to achieve a trade-off between energy consumption and transmission delay.In this network,the task processed at fog nodes reduces transmission delay.Still,it increases energy consumption,while routing tasks to the cloud server saves energy at the cost of higher communication delay.Moreover,the order in which offloaded tasks are executed affects the system’s efficiency.For instance,executing lower-priority tasks before higher-priority jobs can disturb the reliability and stability of the system.Therefore,an efficient strategy of optimal computation offloading and task scheduling is required for operational efficacy.In this paper,we introduced a multi-objective and enhanced version of Cheeta Optimizer(CO),namely(MoECO),to jointly optimize the computation offloading and task scheduling in cloud-fog networks to minimize two competing objectives,i.e.,energy consumption and communication delay.MoECO first assigns tasks to the optimal computational nodes and then the allocated tasks are scheduled for processing based on the task priority.The mathematical modelling of CO needs improvement in computation time and convergence speed.Therefore,MoECO is proposed to increase the search capability of agents by controlling the search strategy based on a leader’s location.The adaptive step length operator is adjusted to diversify the solution and thus improves the exploration phase,i.e.,global search strategy.Consequently,this prevents the algorithm from getting trapped in the local optimal solution.Moreover,the interaction factor during the exploitation phase is also adjusted based on the location of the prey instead of the adjacent Cheetah.This increases the exploitation capability of agents,i.e.,local search capability.Furthermore,MoECO employs a multi-objective Pareto-optimal front to simultaneously minimize designated objectives.Comprehensive simulations in MATLAB demonstrate that the proposed algorithm obtains multiple solutions via a Pareto-optimal front and achieves an efficient trade-off between optimization objectives compared to baseline methods.
文摘Wireless Sensor Networks(WSNs)have become foundational in numerous real-world applications,ranging from environmental monitoring and industrial automation to healthcare systems and smart city development.As these networks continue to grow in scale and complexity,the need for energy-efficient,scalable,and robust communication protocols becomes more critical than ever.Metaheuristic algorithms have shown significant promise in addressing these challenges,offering flexible and effective solutions for optimizing WSN performance.Among them,the Grey Wolf Optimizer(GWO)algorithm has attracted growing attention due to its simplicity,fast convergence,and strong global search capabilities.Accordingly,this survey provides an in-depth review of the applications of GWO and its variants for clustering,multi-hop routing,and hybrid cluster-based routing in WSNs.We categorize and analyze the existing GWO-based approaches across these key network optimization tasks,discussing the different problem formulations,decision variables,objective functions,and performance metrics used.In doing so,we examine standard GWO,multi-objective GWO,and hybrid GWO models that incorporate other computational intelligence techniques.Each method is evaluated based on how effectively it addresses the core constraints of WSNs,including energy consumption,communication overhead,and network lifetime.Finally,this survey outlines existing gaps in the literature and proposes potential future research directions aimed at enhancing the effectiveness and real-world applicability of GWO-based techniques for WSN clustering and routing.Our goal is to provide researchers and practitioners with a clear,structured understanding of the current state of GWO in WSNs and inspire further innovation in this evolving field.
基金financially supported by the National Science Fund for Distinguished Young Scholars,China(No.52025041)the National Natural Science Foundation of China(Nos.52450003,U2341267,and 52174294)+1 种基金the National Postdoctoral Program for Innovative Talents,China(No.BX20240437)the Fundamental Research Funds for the Central Universities,China(Nos.FRF-IDRY-23-037 and FRF-TP-20-02C2)。
文摘The rapid advancements in computer vision(CV)technology have transformed the traditional approaches to material microstructure analysis.This review outlines the history of CV and explores the applications of deep-learning(DL)-driven CV in four key areas of materials science:microstructure-based performance prediction,microstructure information generation,microstructure defect detection,and crystal structure-based property prediction.The CV has significantly reduced the cost of traditional experimental methods used in material performance prediction.Moreover,recent progress made in generating microstructure images and detecting microstructural defects using CV has led to increased efficiency and reliability in material performance assessments.The DL-driven CV models can accelerate the design of new materials with optimized performance by integrating predictions based on both crystal and microstructural data,thereby allowing for the discovery and innovation of next-generation materials.Finally,the review provides insights into the rapid interdisciplinary developments in the field of materials science and future prospects.
基金the National Natural Science Foundation of China(Grant No.U2441263)for financial support of this work。
文摘In composite solid propellants with high aluminum(Al)content and low burning rate,incomplete combustion of the Al powder may occur.In this study,varying lithium(Li)content in Al-Li alloy powder was utilized instead of pure aluminum particles to mitigate agglomeration and enhance the combustion efficiency of solid propellants(Combustion efficiency herein refers to the completeness of metallic fuel oxidation,quantified as the ratio of actual-to-theoretical energy released during combustion)with high Al content and low burning rates.The impact of Al-Li alloy with different Li contents on combustion and agglomeration of solid propellant was investigated using explosion heat,combustion heat,differential thermal analysis(DTA),thermos-gravimetric analysis(TG),dynamic high-pressure combustion test,ignition experiment of small solid rocket motor(SRM)tests,condensation combustion product collection,and X-ray diffraction techniques(XRD).Compared with pure Al,Al-Li alloys exhibit higher combustion heat,which contributes to improved combustion efficiency in Al-Li alloy-containing propellants.DTA and TG analyses demonstrated higher reactivity and lower ignition temperatures for Al-Li alloys.High-pressure combustion experiments at 5 MPa showed that Al-Li alloy fuel significantly decreases combustion agglomeration.The results from theφ75 mm andφ165 mm SRM and XRD tests further support this finding.This study provides novel insights into the combustion and agglomeration behaviors of high-Al,low-burning-rate composite solid propellants and supports the potential application of Al-Li alloys in advanced propellant formulations.
基金National Social Science Fund of China(18KXS009)the Sichuan Provincial Soft Science Program(22JDR0261)the Sichuan University“From 0 to 1”Innovation Research Program(2021CXC10)。
文摘In the context of the revolution in new technologies,a key question is whether the rapid growth of the digital economy,driven by digital technologies,has improved regional innovation performance.Using inter-provincial panel data from China(2012–2022)and adopting a business environment perspective,this study applies a Panel Extended Regression Model(PERM),a Panel Simultaneous Equation Model(PSEM),and a Tobit-IV model to analyze how the development of the digital economy influences regional innovation.The results reveal a pronounced U-shaped relationship between the digital economy and the regional innovation performance at the provincial level in China,with the business environment serving as a significant mediator in this relationship.Moreover,regional innovation performance in China exhibits a“ratchet effect,”with the impact of the digital economy varying markedly across regions.While the eastern and western regions have entered an upward phase,whereby the digital economy boosts innovation,the central region displays a weaker effect.Further analysis indicates that the synergy between the business environment and the digital economy in driving innovation remains suboptimal.These findings were supported by robust checks.This study offers theoretical insights and empirical evidence that support the coordinated development of digital government and the digital factor market,as well as business environment reforms that are in alignment with the innovation demands of the digital era.
基金Project(ASM-20240)supported by the Key Laboratory of Advanced Structural Materials(Changchun University of Technology),Ministry of Education,ChinaProject(2022TD-30)supported by the Scientific and Technological Innovation Team Project of Shaanxi Innovation Capability Support Plan,China。
文摘This work examines the microstructure and corrosion properties of fine-grained Al 7075 across different regions under varying cooling conditions during friction stir welding.The findings demonstrate that forced cooling significantly improves the corrosion resistance of the welded joints.Specifically,the corrosion resistance was the highest in the stir zone,followed by the thermo-mechanical affected zone,and then the heat affected zone.Forced cooling mitigates grain growth by controlling the welding thermal effects,thereby increasing the proportion ofΣ3 grain boundaries.The modification of these microstructural characteristics promotes the formation of a dense oxide layer,thereby enhancing the corrosion resistance.Furthermore,forced cooling mitigates the precipitation and coarsening of the anodic phase in the stir zone,which in turn reduces the susceptibility of the joint to pitting corrosion.Additionally,the lower recrystallization texture content in the joint,resulting from forced cooling,contributes to a reduction in the number of corrosion-active sites,thereby further improving the corrosion performance of the welded joint.
基金supported by the National Natural Science Foundation of China(No.51706105)。
文摘Al/NH_(4)CoF_(3)-Φ(Φ=0.5,1.0,1.5,2.0,and 3.0)binary composites and Al-NH_(4)CoF_(3)@P(VDF-HFP)ternary composites are fabricated via ultrasonication-assisted blending and electrostatic spraying.The effect of equivalence ratio(Φ)on the reaction properties is systematically investigated in the binary Al/NH_(4)CoF_(3)system.For ternary systems,electrostatic spraying allows both components to be efficiently encapsulated by P(VDF-HFP)and to achieve structural stabilization and enhanced reactivity through synergistic interfacial interactions.Morphological analysis using SEM/TEM revealed that P(VDF-HFP)formed a protective layer on Al and NH_(4)CoF_(3)particles,improving dispersion,hydrophobicity(water contact angle increased by 80.5%compared to physically mixed composites),and corrosion resistance.Thermal decomposition of NH_(4)CoF_(3)occurred at 265℃,releasing NH_(3)and HF,which triggered exothermic reactions with Al.The ternary composites exhibited a narrowed main reaction temperature range and concentrated heat release,attributed to improved interfacial contact and polymer decomposition.Combustion tests demonstrated that Al-NH_(4)CoF_(3)@P(VDF-HFP)achieved self-sustaining combustion.In addition,a simple validation was done by replacing the Al component in the aluminium-containing propellant,demonstrating its potential application in the propellant field.This work establishes a novel strategy for designing stable,high-energy composites with potential applications in advanced propulsion systems.
文摘Isolation technology can reduce the type of structural damage that earthquakes cause.A new type of composite sliding-rolling friction composite seismic isolation bearing(SRF)with composite sliding friction and rolling friction is proposed.SRF is capable of realizing a parallel arrangement of sliding friction and rolling friction,and the coefficient of dynamic friction shows variability.The proposed static tests on composite bearings were conducted to investigate the effects of the number of shims,loading speed and vertical pressure on the dynamic friction factor.Test results show that the coefficient of dynamic friction first generally decreases and then increases with an increase in sliding speed,prior to again decreasing with an increase in vertical pressure.The dynamic friction factor increases and then decreases with an increase in the number of shims for a four-roll ball.It decreases and then increases with an increase in the number of shims for a five-roll ball.Based on finite element analysis,modeling and analyzing the effects of the coefficient of friction,the number of balls and the number of shims on the hysteresis performance of the support and derive its skeleton curve.The SRF hysteretic performance,dynamic friction factor and the number of rolling balls and shims show significant correlation.
基金supported and funded by the National Key Research and Development Program of China(2023YFD1300801)the Agricultural Science and Technology Innovation Program in Chinese Academy of Agricultural Sciences(ASTIP-IAS-08)。
文摘Background The decline in reproductive performance of aged hens is mainly attributed to oxidative damage in reproductive organs,hepatic lipid metabolism disorders,and intestinal microbiota dysbiosis.Glycyrrhizin(GL)has been proven to enhance antioxidant capacity,regulate lipid metabolism and gut microbiota in mammals,but its efficacy in hens remains unclear.Hence,this study aimed to investigate whether dietary GL supplementation improves reproductive performance in hens during the late laying stage by modulating intestinal microbiota composition,hepatic lipid metabolism and ovarian antioxidant status.Results Dietary supplementation with 100 mg/kg GL significantly improved the egg production rate,egg quality,and hatching rate in aged breeder hens(P<0.05).GL supplementation also increased the serum levels of HDLC,TP and ALB,and enhanced the antioxidant capacity in both serum and ovary(P<0.05).In addition,dietary GL elevated the serum progesterone(P4)levels by enhancing the transcription level of steroid synthesis key enzymes(CYP11A1 and 3β-HSD)in the ovary(P<0.05).Dietary GL also promoted the synthesis and transport of vitellogenin(VTG)by upregulating the VTG-Ⅱ(P<0.05)and APOV1(P=0.077)expression levels in the liver,thereby increasing the number of grade follicles and small yellow follicles.Moreover,dietary GL enhanced hepatic fatty acidβ-oxidation by upregulating PPARαand CPT-I(P<0.05),and downregulating ACC expression levels(P<0.05).In agreement,liver metabolomics analysis revealed that dietary GL supplementation significantly altered hepatic metabolism,with 389 differentially identified metabolites(P<0.05).The key metabolites(e.g.,taurocholic acid,tauroursodeoxycholic acid,nicotinuric acid,glycodeoxycholic acid(hydrate))were identified,and they were mainly functionally enriched in betaalanine metabolism nicotinate,taurine and hypotaurine metabolism(P<0.05).Finally,16S rRNA gene sequencing revealed that dietary GL reversed age-induced changes in gut microbiota composition,characterized by a significant increase in Lactobacillus abundance and a decrease in Bacteroides(P<0.05).Conclusions These results collectively demonstrate that dietary supplementation with 100 mg/kg GL improved reproductive performance by reversing age-induced changes in gut microbiota,enhancing hepatic vitellogenin synthesis,and ameliorating ovarian function in aged breeder hens.This study suggests that dietary GL is a potential strategy to improve reproductive performance in broiler breeder hens during the late laying period.
基金supported by the National Natural Science Foundation of China(Grant No.12402268)the Fundamental Research Funds for the Central Universities(Grant No.30925010410)。
文摘The core components of an aircraft and the source of its lift are its wings,but lift generation is disrupted by the high temperature and pressure generated on the wing surface when an aircraft gun is fired.Here,to investigate how this process influences the aerodynamic parameters of aircraft wings,the k-ωshearstress-transport turbulence model and the nested dynamic grid technique are used to analyze numerically the transient process of the muzzle jet of a 30-mm small-caliber aircraft gun in highaltitude(10 km)flight with an incoming Mach number of Ma=0.8.For comparison,two other models are established,one with no projectile and the other with no wing.The results indicate that when the aircraft gun is fired,the muzzle jet acts on the wing,creating a pressure field thereon.The uneven distribution of high pressure greatly reduces the lift of the aircraft,causing oscillations in its drag and disrupting its dynamic balance,thereby affecting its flight speed and attitude.Meanwhile,the muzzle jet is obstructed by the wing,and its flow field is distorted and deformed,developing upward toward the wing.Because of the influence of the incoming flow,the shockwave front of the projectile changes from a smooth spherical shape to an irregular one,and the motion parameters of the projectile are also greatly affected by oscillations.The present results provide an important theoretical basis for how the guns of fighter aircraft influence the aerodynamic performance of the wings.
