This paper deals with Mckean-Vlasov backward stochastic differential equations with weak monotonicity coefficients.We first establish the existence and uniqueness of solutions to Mckean-Vlasov backward stochastic diff...This paper deals with Mckean-Vlasov backward stochastic differential equations with weak monotonicity coefficients.We first establish the existence and uniqueness of solutions to Mckean-Vlasov backward stochastic differential equations.Then we obtain a comparison theorem in one-dimensional situation.展开更多
Catalytic decomposition of methane,which produces high-purity hydrogen and high-value-added carbon nanomaterials,has shown considerable potential for development and is expected to yield significant economic benefits ...Catalytic decomposition of methane,which produces high-purity hydrogen and high-value-added carbon nanomaterials,has shown considerable potential for development and is expected to yield significant economic benefits in the future.However,designing catalysts that simultaneously exhibit high activity and long-term stability remains a significant challenge.Tuning the catalyst’s structure and electronic properties is an effective strategy for enhancing the reaction performance.In this work,a series of NixZr/ZSM-5 catalysts were prepared using the incipient wetness impregnation method,and the effect of Zr loadings on catalyst properties and performance was systematically investigated.The calcined and reduced catalysts were characterized by low-temperature N_(2)adsorption-desorption,XRD,SEM,H_(2)-TPR and XPS.The results showed that the addition of Zr significantly increased the specific surface area of the catalyst and reduced the metal particle size.Smaller NiO particles were found to enter the pores of the HZSM-5 support,and electronic interactions between NiO and ZrO_(2)markedly enhanced the metal-support interaction.The catalyst exhibited optimal catalytic performance at a Zr loading of 5%,achieving a maximum methane conversion of 68%at 625℃,maintaining activity for 900 min,and delivering a carbon yield of 1927%.Further increasing the Zr loading yielded only limited improvements in catalytic performance.Characterization of the spent catalysts and carbon products via TEM,Raman spectroscopy,and TGA revealed that the introduction of ZrO_(2)reduced metal sintering and promoted a shift in carbon nanofibers growth mode from tip-growth to base-growth.The mechanism of base-growth enabled the catalyst to maintain reaction activity for an extended period.展开更多
Energy-regenerative suspension combined with piezoelectric and electromagnetic transduction has evolved into a core technological pathway in advancing automotive design paradigms.With the aim of improving energy harve...Energy-regenerative suspension combined with piezoelectric and electromagnetic transduction has evolved into a core technological pathway in advancing automotive design paradigms.With the aim of improving energy harvesting performance,time-delayed feedback control is widely used in an energy-regenerative suspension system under different external disturbances in this paper.Meanwhile,limited research has addressed the stochastic dynamics of time-delayed nonlinear energy-regenerative suspension systems.Different from previous studies,this work studies the stochastic response and P-bifurcation of the nonlinear energy-regenerative suspension system with time-delayed feedback control.Firstly,an approximately equivalent dimension reduction system is established by the variable transformation method,and then the stationary probability density function of amplitude is obtained by the stochastic averaging method.Secondly,the precision of the method used in this work is verified by comparing the numerical solutions with the analytical results.Finally,based on the stationary probability density function,the influence of system parameters on stochastic P-bifurcation and the mean output power is discussed.展开更多
Chemical warfare agents(CWAs)remain a persistent hazard in many parts of the world,necessitating a deeper exploration of their chemical and physical characteristics and reactions under diverse conditions.Diisopropyl m...Chemical warfare agents(CWAs)remain a persistent hazard in many parts of the world,necessitating a deeper exploration of their chemical and physical characteristics and reactions under diverse conditions.Diisopropyl methylphosphonate(DIMP),a commonly used CWA surrogate,is widely studied to enhance our understanding of CWA behavior.The prevailing thermal decomposition model for DIMP,developed approximately 25 years ago,is based on data collected in nitrogen atmospheres at temperatures ranging from 700 K to 800 K.Despite its limitations,this model continues to serve as a foundation for research across various thermal and reactive environments,including combustion studies.Our recent experiments have extended the scope of decomposition analysis by examining DIMP in both nitrogen and zero air across a lower temperature range of 175℃ to 250℃.Infrared spectroscopy results under nitrogen align well with the established model;however,we observed that catalytic effects,stemming from decomposition byproducts and interactions with stainless steel surfaces,alter the reaction kinetics.In zero air environments,we observed a novel infrared absorption band.Spectral fitting suggests this band may represent a combination of propanal and acetone,while GCMS analysis points to vinyl formate and acetone as possible constituents.Although the precise identity of these new products remains unresolved,our findings clearly indicate that the existing decomposition model cannot be reliably extended to lower temperatures or non-nitrogen environments without further revisions.展开更多
Current improved Empirical Mode Decomposition(EMD)methods enhance the accurate identification of peak and valley points in mechanical signals through noise-assisted filtering techniques,thereby improving the mode deco...Current improved Empirical Mode Decomposition(EMD)methods enhance the accurate identification of peak and valley points in mechanical signals through noise-assisted filtering techniques,thereby improving the mode decomposition performance,which is of great significance in extracting fault features from mechanical signals.However,noise-assisted filtering leads to the loss of critical features in mechanical signals and introduces a large amount of residual noise into Intrinsic Mode Functions(IMFs)that obscure signal features.To address these issues,a Precise Identification-based Mode Decomposition(PIMD)method is proposed.This method directly enhances the ability of EMD to precisely identify peak and valley points by using a proposed precise identifi-cation approach,which improves mode decomposition performance and avoids the negative impacts of noise-assisted filtering,thus benefiting the extraction of more mechanical fault features.Simulation results show that the proposed PIMD method can precisely identify peak and valley points of signals with noise of different signal-tonoise ratios and perform a highly rigorous high-low frequency decomposition,significantly outperforming EMD.Finally,mechanical fault diagnostic experiments on four bearing cases and two gear cases demonstrate that,compared to four mainstream methods,the PIMD method exhibits the best mode decomposition perfor-mance and can extract more and clearer mechanical fault features.展开更多
A stochastic predator-prey system with Markov switching is explored.We have developed a new chasing technique to efficiently solve the Fokker-Planck-Kolmogorov and backward Kolmogorov equations.Dynamic balance and rel...A stochastic predator-prey system with Markov switching is explored.We have developed a new chasing technique to efficiently solve the Fokker-Planck-Kolmogorov and backward Kolmogorov equations.Dynamic balance and reliability of the switching system are evaluated via stationary probability density function and first-passage failure theory,taking into account factors such as switching frequencies,noise intensities,and initial conditions.Results reveal that Markov switching leads to stochastic P-bifurcation,enhancing dynamic balance and reducing white-noise-induced oscillations.But frequent switching can heighten initial value dependence,harming reliability.Further,the influence of the subsystem on the switching system is not proportional to its action probabilities.Monte Carlo simulations validate the findings,offering an in-depth exploration of these dynamics.展开更多
The sulfation and decomposition process has proven effective in selectively extracting lithium from lepidolite.It is essential to clarify the thermochemical behavior and kinetic parameters of decomposition reactions.A...The sulfation and decomposition process has proven effective in selectively extracting lithium from lepidolite.It is essential to clarify the thermochemical behavior and kinetic parameters of decomposition reactions.Accordingly,comprehensive kinetic study by employing thermalgravimetric analysis at various heating rates was presented in this paper.Two main weight loss regions were observed during heating.The initial region corresponded to the dehydration of crystal water,whereas the subsequent region with overlapping peaks involved complex decomposition reactions.The overlapping peaks were separated into two individual reaction peaks and the activation energy of each peak was calculated using isoconversional kinetics methods.The activation energy of peak 1 exhibited a continual increase as the reaction conversion progressed,while that of peak 2 steadily decreased.The optimal kinetic models,identified as belonging to the random nucleation and subsequent growth category,provided valuable insights into the mechanism of the decomposition reactions.Furthermore,the adjustment factor was introduced to reconstruct the kinetic mechanism models,and the reconstructed models described the kinetic mechanism model more accurately for the decomposition reactions.This study enhanced the understanding of the thermochemical behavior and kinetic parameters of the lepidolite sulfation product decomposition reactions,further providing theoretical basis for promoting the selective extraction of lithium.展开更多
Predator–prey interactions are fundamental to understanding ecosystem stability and biodiversity.In this study,we propose and analyze a stochastic predator–prey model that incorporates two critical ecological factor...Predator–prey interactions are fundamental to understanding ecosystem stability and biodiversity.In this study,we propose and analyze a stochastic predator–prey model that incorporates two critical ecological factors:prey refuge and harvesting.The model also integrates disease transmission within the predator population,adding an important layer of realism.Using rigorous mathematical techniques,we demonstrate the existence and uniqueness of a global positive solution,thereby confirming the model's biological feasibility.We further derive sufficient conditions for two key ecological scenarios:stochastic permanence,which ensures the sustained co-existence of prey and predators over time,and extinction,where one or both populations decline to zero.The interplay between prey refuge and harvesting is thoroughly examined to understand their combined impact on population dynamics.All theoretical results are validated by detailed numerical simulations,highlighting the applicability of the model to real-world ecological systems.From the simulation results,we observed that with an adequate level of prey refuge and predator harvesting,the susceptible predator and prey coexist with extensive oscillations,while the infected predator population was moving towards extinction.In addition,we have investigated the effect of disease transmission on system dynamics.Our results show that,as the transmission rate of disease increases,the susceptible predator approaches extinction,whereas,on the other hand,when it declines,the susceptible predator shows robust oscillations while the infected approaches extinction.In both cases,the prey population demonstrates robust stability due to the prey refuge.Our findings show that the management of harvesting and the prey refuge can be effective ecological tactics for disease control and species protection under stochastic environmental effects.展开更多
In the image fusion field,fusing infrared images(IRIs)and visible images(VIs)excelled is a key area.The differences between IRIs and VIs make it challenging to fuse both types into a high-quality image.Accordingly,eff...In the image fusion field,fusing infrared images(IRIs)and visible images(VIs)excelled is a key area.The differences between IRIs and VIs make it challenging to fuse both types into a high-quality image.Accordingly,efficiently combining the advantages of both images while overcoming their shortcomings is necessary.To handle this challenge,we developed an end-to-end IRI andVI fusionmethod based on frequency decomposition and enhancement.By applying concepts from frequency domain analysis,we used the layering mechanism to better capture the salient thermal targets from the IRIs and the rich textural information from the VIs,respectively,significantly boosting the image fusion quality and effectiveness.In addition,the backbone network combined Restormer Blocks and Dense Blocks;Restormer blocks utilize global attention to extract shallow features.Meanwhile,Dense Blocks ensure the integration between shallow and deep features,thereby avoiding the loss of shallow attributes.Extensive experiments on TNO and MSRS datasets demonstrated that the suggested method achieved state-of-the-art(SOTA)performance in various metrics:Entropy(EN),Mutual Information(MI),Standard Deviation(SD),The Structural Similarity Index Measure(SSIM),Fusion quality(Qabf),MI of the pixel(FMI_(pixel)),and modified Visual Information Fidelity(VIF_(m)).展开更多
Upper Andean tropical forests are renowned for their extraordinary biodiversity and heterogeneous environmental conditions.Despite the critical role of litter decomposition in carbon and nutrient cycles,its dynamics i...Upper Andean tropical forests are renowned for their extraordinary biodiversity and heterogeneous environmental conditions.Despite the critical role of litter decomposition in carbon and nutrient cycles,its dynamics in this region remains unexplored at finer scales.This study investigates how micro site conditions influence litter decomposition of 15 upper Andean species over time.A reciprocal translocation field experiment was conducted over 18 months in 14 permanent plots within four sites in Colombian Andean mountain forests.Each plot contained three litterbeds(microsites),each with the 15 species,harvested at 3,6,12 and 18 months,totaling 2520 litterbags.Different forest variables,including canopy openness,leaf area index,slope and depth of litter,were measured in each litterbed.ANOVAs and linear mixed models were used to assess variation between sites and plots respectively,while multiple linear regression analyses evaluated the effects of forest variables on decay rates over time at the micro site scale.Results showed differences in absolute decay rates between sites but consistent relative decay rates,indicating varying magnitudes of decomposition,yet maintaining the same order based on their litter quality.Decay rates varied between species,with more variation in labile species compared to recalcitrant ones.Despite substantial variation in forest characteristics within sites,their influence on litter decomposition was minimal and declined over time.This suggests that,at finer spatial scales,the forest microenvironment plays a lesser role in litter decomposition,with litter quality emerging as the primary driver.This study is a step towards understanding the fine-scale dynamics of litter decomposition in upper Andean tropical forests,highlighting the intricate interplay between microenvironmental factors and decomposition processes.展开更多
Litter decomposition is an essential ecosystem process influenced by multiple factors,including substrate quality,climate,edaphic environment,and decomposer communities.However,the role of canopy species identity and ...Litter decomposition is an essential ecosystem process influenced by multiple factors,including substrate quality,climate,edaphic environment,and decomposer communities.However,the role of canopy species identity and diversity on leaf litter decomposition in forests remains understudied.By controlling for macroclimate,soil properties,and litter substrate in a mature common garden,we investigated whether a three-month tea bag incubation of standardized green and rooibos tea substrate is driven by canopy tree species characteristics and diversity.Our study hypothesized two primary pathways:a chemical engineering effect,where trees alter soil properties and decomposer communities through litter input,and a physical engineering effect,where tree canopy structure modulates the local microclimate.The results showed that even under uniform macroclimatic and initial soil conditions,mass loss rates varied widely for green tea(27.4%–73.2%)and rooibos tea(6.1%–34.7%),comparable as found in other research between distinct biomes.While substrate quality was the dominant factor,both engineering pathways and,to a minor extent,tree diversity modulated mass losses.For green tea,tree chemical and physical characteristics seemed equally important,while the physical environment showed an increased importance for rooibos.Incubation depth played a key role,where forest floor decomposition rates are more susceptible to temporal climate variations,and soil-layer decomposition rates are less susceptible to climate variations and more determined by tree species identity.Our findings suggest that tea bag experiments focusing solely on topsoil burial may underestimate processes in the forest floor and the mineralorganic boundary layer.This study underscores the critical role of litter substrate quality in decomposition while demonstrating that tree community composition and the associated herbaceous layer,through both chemical and physical engineering pathways,strongly modulate decomposition rates.展开更多
Theauthor proposes a dual layer source grid load storage collaborative planning model based on Benders decomposition to optimize the low-carbon and economic performance of the distribution network.The model plans the ...Theauthor proposes a dual layer source grid load storage collaborative planning model based on Benders decomposition to optimize the low-carbon and economic performance of the distribution network.The model plans the configuration of photovoltaic(3.8 MW),wind power(2.5 MW),energy storage(2.2 MWh),and SVC(1.2 Mvar)through interaction between upper and lower layers,and modifies lines 2–3,8–9,etc.to improve transmission capacity and voltage stability.The author uses normal distribution and Monte Carlo method to model load uncertainty,and combines Weibull distribution to describe wind speed characteristics.Compared to the traditional three-layer model(TLM),Benders decomposition-based two-layer model(BLBD)has a 58.1%reduction in convergence time(5.36 vs.12.78 h),a 51.1%reduction in iteration times(23 vs.47 times),a 8.07%reduction in total cost(12.436 vs.13.528 million yuan),and a 9.62%reduction in carbon emissions(12,456 vs.13,782 t).After optimization,the peak valley difference decreased from4.1 to 2.9MW,the renewable energy consumption rate reached 93.4%,and the energy storage efficiency was 87.6%.Themodel has been validated in the IEEE 33 node system,demonstrating its superiority in terms of economy,low-carbon,and reliability.展开更多
The integration of a high proportion of renewable energy introduces significant challenges for the adaptability of traditional fault nature identification methods.To address these challenges,this paper presents a nove...The integration of a high proportion of renewable energy introduces significant challenges for the adaptability of traditional fault nature identification methods.To address these challenges,this paper presents a novel fault nature identification method for renewable energy grid-connected interconnection lines,leveraging wavelet packet decomposition and voltage waveform time-frequency morphology comparison algorithms.First,the paper investigates the harmonic injection mechanism during non-full-phase operation following fault isolation in photovoltaic renewable energy systems,and examines the voltage characteristics of faulted phases in renewable energy scenarios.The analysis reveals that substantial differences exist in both the time and frequency domains of phase voltages before and after the extinction of transient faults,whereas permanent faults do not exhibit such variations.Building on this observation,the paper proposes a voltage time-frequency feature extraction method based on wavelet packet decomposition,wherein low-frequency waveform components are selected to characterize fault features.Subsequently,a fault nature identification method is introduced,based on a voltage waveform time-frequency morphology comparison.By employing a windowing technique to quantify waveform differences before and after arc extinction,this method effectively distinguishes between permanent and transient faults and accurately determines the arc extinction time.Finally,a 220 kV renewable energy grid connection line model is developed using PSCAD for verification.The results demonstrate that the proposed method is highly adaptable across various fault locations,transition resistances,and renewable energy control strategies,and can reliably identify fault nature in renewable energy grid connection scenarios.展开更多
The microstructural evolution of Cu−19Ni−6Cr−7Mn alloy during aging treatment was investigated.After aging for 120 min at 500℃,the alloy exhibited excellent mechanical properties,including a tensile strength of 978 M...The microstructural evolution of Cu−19Ni−6Cr−7Mn alloy during aging treatment was investigated.After aging for 120 min at 500℃,the alloy exhibited excellent mechanical properties,including a tensile strength of 978 MPa and an elastic modulus of 145.8 GPa.After aging for 240 min at 500℃,the elastic modulus of the alloy reached 149.5 GPa,which was among the highest values reported for Cu alloys.It was worth mentioning that the tensile strength increased rapidly from 740 to 934 MPa after aging for 5 min at 500℃,which was close to the maximum tensile strength(978 MPa).Analysis of the underlying strengthening mechanisms and phase transformation behavior revealed that the Cu−19Ni−6Cr−7Mn alloy underwent spinodal decomposition and DO_(22) ordering during the first 5 min of aging at 500℃,and L1_(2) ordered phases and bcc-Cr precipitates appeared.Therefore,the enhanced mechanical properties of the Cu−19Ni−6Cr−7Mn alloy can be attributed to the stress field generated by spinodal decomposition and the presence of nanoscale ordered phase and Cr precipitates.展开更多
The stochastic extended finite-fault simulation method(EXSIM)is a widely used tool in seismological research,with applications in ground motion prediction and simulation,seismic hazard analysis,and engineering studies...The stochastic extended finite-fault simulation method(EXSIM)is a widely used tool in seismological research,with applications in ground motion prediction and simulation,seismic hazard analysis,and engineering studies.However,recent studies have revealed a significant limitation:EXSIM tends to overpredict ground motions in the low-to-intermediate frequency range,particularly for large thrust earthquakes that are often characterized by a double-corner-frequency source model.To address this issue and enhance simulation accuracy,this study introduces two key improvements:(1)a novel asperity-distributed stress-drop composite fault model and(2)a hybrid application of EXSIM with the composite fault model.The proposed method is validated through its application to the 2013 M_(w)6.7 Lushan earthquake that occurred in China and six thrust earthquakes with an M_(w)≥6.5 in Japan.By comparing the simulated ground motions with recorded data,the results demonstrate that the improved method achieves consistent accuracy across the high-and low-frequency spectrum(combined goodness-of-fit:CGOF<0.35).This study significantly broadens the applicability of stochastic finite-fault simulations,enabling more reliable predictions for a wider range of seismic scenarios,including complex thrust faulting events.展开更多
Addressing the limitations of inadequate stochastic disturbance characterization during wind turbine degradation processes that result in constrained modeling accuracy,replacement-based maintenance practices that devi...Addressing the limitations of inadequate stochastic disturbance characterization during wind turbine degradation processes that result in constrained modeling accuracy,replacement-based maintenance practices that deviate from actual operational conditions,and static maintenance strategies that fail to adapt to accelerated deterioration trends leading to suboptimal remaining useful life utilization,this study proposes a Time-Based Incomplete Maintenance(TBIM)strategy incorporating reliability constraints through stochastic differential equations(SDE).By quantifying stochastic interference via Brownian motion terms and characterizing nonlinear degradation features through state influence rate functions,a high-precision SDE degradation model is constructed,achieving 16%residual reduction compared to conventional ordinary differential equation(ODE)methods.The introduction of age reduction factors and failure rate growth factors establishes an incomplete maintenance mechanism that transcends traditional“as-good-as-new”assumptions,with the TBIM model demonstrating an additional 8.5%residual reduction relative to baseline SDE approaches.A dynamic maintenance interval optimization model driven by dual parameters—preventive maintenance threshold R_(p) and replacement threshold R_(r)—is designed to achieve synergistic optimization of equipment reliability and maintenance economics.Experimental validation demonstrates that the optimized TBIM extends equipment lifespan by 4.4%and reducesmaintenance costs by 4.16%at R_(p)=0.80,while achieving 17.2%lifespan enhancement and 14.6%cost reduction at R_(p)=0.90.This methodology provides a solution for wind turbine preventive maintenance that integrates condition sensitivity with strategic foresight.展开更多
In dry-coupled ultrasonic thickness measurement,thick rubber layers introduce high-amplitude parasitic echoes that obscure defect signals and degrade thickness accuracy.Existing methods struggle to resolve overlap-pin...In dry-coupled ultrasonic thickness measurement,thick rubber layers introduce high-amplitude parasitic echoes that obscure defect signals and degrade thickness accuracy.Existing methods struggle to resolve overlap-ping echoes under variable coupling conditions and non-stationary noise.This study proposes a novel dual-criterion framework integrating energy contribution and statistical impulsivity metrics to isolate specimen re-flections from coupling-layer interference.By decomposing A-scan signals into Intrinsic Mode Functions(IMFs),the framework employs energy contribution thresholds(>85%)and kurtosis indices(>3)to autonomously select IMFs containing valid specimen echoes.Hybrid time-frequency thresholding further suppresses interference through amplitude filtering and spectral focusing.Experimental results demonstrate the framework’s robustness,achieving 92.3%thickness accuracy for 5 mm steel specimens with 5 mm rubber coupling,outperforming conventional methods by up to 18.7%.The dual-criterion approach reduces operator dependency by 37%and maintainsΔT<0.03 mm under surface roughness up to 6.3μm,offering a practical solution for industrial nondestructive testing with thick dry-coupled interfaces.展开更多
Dear Editor,This letter studies the problem of stealthy attacks targeting stochastic event-based estimation,alongside proposing measures for their mitigation.A general attack framework is introduced,and the correspond...Dear Editor,This letter studies the problem of stealthy attacks targeting stochastic event-based estimation,alongside proposing measures for their mitigation.A general attack framework is introduced,and the corresponding stealthiness condition is analyzed.To enhance system security,we advocate for a single-dimensional encryption method,showing that securing a singular data element is sufficient to shield the system from the perils of stealthy attacks.展开更多
We develop and implement a Stochastic Discrete Event Simulation(SDES)algorithm to model the housing re-covery trajectory after an extreme event.The algorithm models discrete events and their underlying uncertainties i...We develop and implement a Stochastic Discrete Event Simulation(SDES)algorithm to model the housing re-covery trajectory after an extreme event.The algorithm models discrete events and their underlying uncertainties in each construction phase.Specifically,the algorithm is developed for the Government Assisted Owner Driven(GAOD)reconstruction system to simulate long-term recovery trajectory.SDES,as a flexible modeling approach,can simulate any housing recovery scenario that follows phased reconstruction.The 2015 M 7.8 Gorkha earthquake sequence in Nepal is considered the extreme event,with 796,245 buildings requiring reconstruction.We present some recovery trajectories from severely hit,crisis hit,and earthquake hit parishes,comparing them with the actual reconstruction progress.We also assess quality and improvement of reconstructed buildings using seismic fragility functions,compared to pre-earthquake constructions.Housing recovery uncertainties are dissected in relation to reconstruction pace.We conclude that the vast majority of the reconstructed buildings followed the Build Back Better(BBB)approach and missed the opportunity to pursue the Build Back Resilient(BBR)approach due to multifaceted challenges ranging from unclear policies to economic constraints.We critically assess the GAOD vs Owner Driven(OD)recovery framework and conclude that insurance-supported and technically assisted OD approach could be the most suitable model for post extreme event housing recovery.展开更多
Deep neural networks often outperform classical machine learning algorithms in solving real-world problems.However,designing better networks usually requires domain expertise and consumes significant time and com-puti...Deep neural networks often outperform classical machine learning algorithms in solving real-world problems.However,designing better networks usually requires domain expertise and consumes significant time and com-puting resources.Moreover,when the task changes,the original network architecture becomes outdated and requires redesigning.Thus,Neural Architecture Search(NAS)has gained attention as an effective approach to automatically generate optimal network architectures.Most NAS methods mainly focus on achieving high performance while ignoring architectural complexity.A myriad of research has revealed that network performance and structural complexity are often positively correlated.Nevertheless,complex network structures will bring enormous computing resources.To cope with this,we formulate the neural architecture search task as a multi-objective optimization problem,where an optimal architecture is learned by minimizing the classification error rate and the number of network parameters simultaneously.And then a decomposition-based multi-objective stochastic fractal search method is proposed to solve it.In view of the discrete property of the NAS problem,we discretize the stochastic fractal search step size so that the network architecture can be optimized more effectively.Additionally,two distinct update methods are employed in step size update stage to enhance the global and local search abilities adaptively.Furthermore,an information exchange mechanism between architectures is raised to accelerate the convergence process and improve the efficiency of the algorithm.Experimental studies show that the proposed algorithm has competitive performance comparable to many existing manual and automatic deep neural network generation approaches,which achieved a parameter-less and high-precision architecture with low-cost on each of the six benchmark datasets.展开更多
基金Supported by the National Natural Science Foundation of China(12001074)the Research Innovation Program of Graduate Students in Hunan Province(CX20220258)+1 种基金the Research Innovation Program of Graduate Students of Central South University(1053320214147)the Key Scientific Research Project of Higher Education Institutions in Henan Province(25B110025)。
文摘This paper deals with Mckean-Vlasov backward stochastic differential equations with weak monotonicity coefficients.We first establish the existence and uniqueness of solutions to Mckean-Vlasov backward stochastic differential equations.Then we obtain a comparison theorem in one-dimensional situation.
基金Supported by Innovative Research Groups of the National Natural Science Foundation of China(22021004)。
文摘Catalytic decomposition of methane,which produces high-purity hydrogen and high-value-added carbon nanomaterials,has shown considerable potential for development and is expected to yield significant economic benefits in the future.However,designing catalysts that simultaneously exhibit high activity and long-term stability remains a significant challenge.Tuning the catalyst’s structure and electronic properties is an effective strategy for enhancing the reaction performance.In this work,a series of NixZr/ZSM-5 catalysts were prepared using the incipient wetness impregnation method,and the effect of Zr loadings on catalyst properties and performance was systematically investigated.The calcined and reduced catalysts were characterized by low-temperature N_(2)adsorption-desorption,XRD,SEM,H_(2)-TPR and XPS.The results showed that the addition of Zr significantly increased the specific surface area of the catalyst and reduced the metal particle size.Smaller NiO particles were found to enter the pores of the HZSM-5 support,and electronic interactions between NiO and ZrO_(2)markedly enhanced the metal-support interaction.The catalyst exhibited optimal catalytic performance at a Zr loading of 5%,achieving a maximum methane conversion of 68%at 625℃,maintaining activity for 900 min,and delivering a carbon yield of 1927%.Further increasing the Zr loading yielded only limited improvements in catalytic performance.Characterization of the spent catalysts and carbon products via TEM,Raman spectroscopy,and TGA revealed that the introduction of ZrO_(2)reduced metal sintering and promoted a shift in carbon nanofibers growth mode from tip-growth to base-growth.The mechanism of base-growth enabled the catalyst to maintain reaction activity for an extended period.
基金Project supported by the National Natural Science Foundation of China(Grant No.12002089)the Science and Technology Projects in Guangzhou(Grant No.2023A04J1323)UKRI Horizon Europe Guarantee(Marie SklodowskaCurie Fellowship)(Grant No.EP/Y016130/1)。
文摘Energy-regenerative suspension combined with piezoelectric and electromagnetic transduction has evolved into a core technological pathway in advancing automotive design paradigms.With the aim of improving energy harvesting performance,time-delayed feedback control is widely used in an energy-regenerative suspension system under different external disturbances in this paper.Meanwhile,limited research has addressed the stochastic dynamics of time-delayed nonlinear energy-regenerative suspension systems.Different from previous studies,this work studies the stochastic response and P-bifurcation of the nonlinear energy-regenerative suspension system with time-delayed feedback control.Firstly,an approximately equivalent dimension reduction system is established by the variable transformation method,and then the stationary probability density function of amplitude is obtained by the stochastic averaging method.Secondly,the precision of the method used in this work is verified by comparing the numerical solutions with the analytical results.Finally,based on the stationary probability density function,the influence of system parameters on stochastic P-bifurcation and the mean output power is discussed.
基金sponsored by the Department of Defense,Defense Threat Reduction Agency under the Materials Science in Extreme Environments University Research Alliance,HDTRA1-20-2-0001。
文摘Chemical warfare agents(CWAs)remain a persistent hazard in many parts of the world,necessitating a deeper exploration of their chemical and physical characteristics and reactions under diverse conditions.Diisopropyl methylphosphonate(DIMP),a commonly used CWA surrogate,is widely studied to enhance our understanding of CWA behavior.The prevailing thermal decomposition model for DIMP,developed approximately 25 years ago,is based on data collected in nitrogen atmospheres at temperatures ranging from 700 K to 800 K.Despite its limitations,this model continues to serve as a foundation for research across various thermal and reactive environments,including combustion studies.Our recent experiments have extended the scope of decomposition analysis by examining DIMP in both nitrogen and zero air across a lower temperature range of 175℃ to 250℃.Infrared spectroscopy results under nitrogen align well with the established model;however,we observed that catalytic effects,stemming from decomposition byproducts and interactions with stainless steel surfaces,alter the reaction kinetics.In zero air environments,we observed a novel infrared absorption band.Spectral fitting suggests this band may represent a combination of propanal and acetone,while GCMS analysis points to vinyl formate and acetone as possible constituents.Although the precise identity of these new products remains unresolved,our findings clearly indicate that the existing decomposition model cannot be reliably extended to lower temperatures or non-nitrogen environments without further revisions.
基金Supported by National Natural Science Foundation of China(Grant No.52075236)Opening Foundation of Intelligent Manufacturing Technology(Shantou University),Ministry of Education(Grant No.STME2024002)+1 种基金Hundred Doctor and Hundred Enterprise,Science and Technology Project,Ji'an City(Grant No.42064001)Guangdong Provincial University Innovation Team Project(Grant No.2020KCXTD012).
文摘Current improved Empirical Mode Decomposition(EMD)methods enhance the accurate identification of peak and valley points in mechanical signals through noise-assisted filtering techniques,thereby improving the mode decomposition performance,which is of great significance in extracting fault features from mechanical signals.However,noise-assisted filtering leads to the loss of critical features in mechanical signals and introduces a large amount of residual noise into Intrinsic Mode Functions(IMFs)that obscure signal features.To address these issues,a Precise Identification-based Mode Decomposition(PIMD)method is proposed.This method directly enhances the ability of EMD to precisely identify peak and valley points by using a proposed precise identifi-cation approach,which improves mode decomposition performance and avoids the negative impacts of noise-assisted filtering,thus benefiting the extraction of more mechanical fault features.Simulation results show that the proposed PIMD method can precisely identify peak and valley points of signals with noise of different signal-tonoise ratios and perform a highly rigorous high-low frequency decomposition,significantly outperforming EMD.Finally,mechanical fault diagnostic experiments on four bearing cases and two gear cases demonstrate that,compared to four mainstream methods,the PIMD method exhibits the best mode decomposition perfor-mance and can extract more and clearer mechanical fault features.
基金Project supported by the National Natural Science Foundation of China(Grant No.12472033)。
文摘A stochastic predator-prey system with Markov switching is explored.We have developed a new chasing technique to efficiently solve the Fokker-Planck-Kolmogorov and backward Kolmogorov equations.Dynamic balance and reliability of the switching system are evaluated via stationary probability density function and first-passage failure theory,taking into account factors such as switching frequencies,noise intensities,and initial conditions.Results reveal that Markov switching leads to stochastic P-bifurcation,enhancing dynamic balance and reducing white-noise-induced oscillations.But frequent switching can heighten initial value dependence,harming reliability.Further,the influence of the subsystem on the switching system is not proportional to its action probabilities.Monte Carlo simulations validate the findings,offering an in-depth exploration of these dynamics.
基金financially supported by the National Natural Science Foundation of China(Nos.52034002 and U2202254)the Fundamental Research Funds for the Central Universities,China(No.FRF-TT-19-001)。
文摘The sulfation and decomposition process has proven effective in selectively extracting lithium from lepidolite.It is essential to clarify the thermochemical behavior and kinetic parameters of decomposition reactions.Accordingly,comprehensive kinetic study by employing thermalgravimetric analysis at various heating rates was presented in this paper.Two main weight loss regions were observed during heating.The initial region corresponded to the dehydration of crystal water,whereas the subsequent region with overlapping peaks involved complex decomposition reactions.The overlapping peaks were separated into two individual reaction peaks and the activation energy of each peak was calculated using isoconversional kinetics methods.The activation energy of peak 1 exhibited a continual increase as the reaction conversion progressed,while that of peak 2 steadily decreased.The optimal kinetic models,identified as belonging to the random nucleation and subsequent growth category,provided valuable insights into the mechanism of the decomposition reactions.Furthermore,the adjustment factor was introduced to reconstruct the kinetic mechanism models,and the reconstructed models described the kinetic mechanism model more accurately for the decomposition reactions.This study enhanced the understanding of the thermochemical behavior and kinetic parameters of the lepidolite sulfation product decomposition reactions,further providing theoretical basis for promoting the selective extraction of lithium.
基金supported by the National Natural Science Foundation of China(Grant No.32271554)the Guangdong Basic and Applied Basic Research Foundation(Grant No.2023A1515011501)。
文摘Predator–prey interactions are fundamental to understanding ecosystem stability and biodiversity.In this study,we propose and analyze a stochastic predator–prey model that incorporates two critical ecological factors:prey refuge and harvesting.The model also integrates disease transmission within the predator population,adding an important layer of realism.Using rigorous mathematical techniques,we demonstrate the existence and uniqueness of a global positive solution,thereby confirming the model's biological feasibility.We further derive sufficient conditions for two key ecological scenarios:stochastic permanence,which ensures the sustained co-existence of prey and predators over time,and extinction,where one or both populations decline to zero.The interplay between prey refuge and harvesting is thoroughly examined to understand their combined impact on population dynamics.All theoretical results are validated by detailed numerical simulations,highlighting the applicability of the model to real-world ecological systems.From the simulation results,we observed that with an adequate level of prey refuge and predator harvesting,the susceptible predator and prey coexist with extensive oscillations,while the infected predator population was moving towards extinction.In addition,we have investigated the effect of disease transmission on system dynamics.Our results show that,as the transmission rate of disease increases,the susceptible predator approaches extinction,whereas,on the other hand,when it declines,the susceptible predator shows robust oscillations while the infected approaches extinction.In both cases,the prey population demonstrates robust stability due to the prey refuge.Our findings show that the management of harvesting and the prey refuge can be effective ecological tactics for disease control and species protection under stochastic environmental effects.
基金funded by Anhui Province University Key Science and Technology Project(2024AH053415)Anhui Province University Major Science and Technology Project(2024AH040229)+3 种基金Talent Research Initiation Fund Project of Tongling University(2024tlxyrc019)Tongling University School-Level Scientific Research Project(2024tlxyptZD07)TheUniversity Synergy Innovation Programof Anhui Province(GXXT-2023-050)Tongling City Science and Technology Major Special Project(Unveiling and Commanding Model)(200401JB004).
文摘In the image fusion field,fusing infrared images(IRIs)and visible images(VIs)excelled is a key area.The differences between IRIs and VIs make it challenging to fuse both types into a high-quality image.Accordingly,efficiently combining the advantages of both images while overcoming their shortcomings is necessary.To handle this challenge,we developed an end-to-end IRI andVI fusionmethod based on frequency decomposition and enhancement.By applying concepts from frequency domain analysis,we used the layering mechanism to better capture the salient thermal targets from the IRIs and the rich textural information from the VIs,respectively,significantly boosting the image fusion quality and effectiveness.In addition,the backbone network combined Restormer Blocks and Dense Blocks;Restormer blocks utilize global attention to extract shallow features.Meanwhile,Dense Blocks ensure the integration between shallow and deep features,thereby avoiding the loss of shallow attributes.Extensive experiments on TNO and MSRS datasets demonstrated that the suggested method achieved state-of-the-art(SOTA)performance in various metrics:Entropy(EN),Mutual Information(MI),Standard Deviation(SD),The Structural Similarity Index Measure(SSIM),Fusion quality(Qabf),MI of the pixel(FMI_(pixel)),and modified Visual Information Fidelity(VIF_(m)).
基金supported by the Universidad del Rosario(Small grant ID:IV-FPD003)。
文摘Upper Andean tropical forests are renowned for their extraordinary biodiversity and heterogeneous environmental conditions.Despite the critical role of litter decomposition in carbon and nutrient cycles,its dynamics in this region remains unexplored at finer scales.This study investigates how micro site conditions influence litter decomposition of 15 upper Andean species over time.A reciprocal translocation field experiment was conducted over 18 months in 14 permanent plots within four sites in Colombian Andean mountain forests.Each plot contained three litterbeds(microsites),each with the 15 species,harvested at 3,6,12 and 18 months,totaling 2520 litterbags.Different forest variables,including canopy openness,leaf area index,slope and depth of litter,were measured in each litterbed.ANOVAs and linear mixed models were used to assess variation between sites and plots respectively,while multiple linear regression analyses evaluated the effects of forest variables on decay rates over time at the micro site scale.Results showed differences in absolute decay rates between sites but consistent relative decay rates,indicating varying magnitudes of decomposition,yet maintaining the same order based on their litter quality.Decay rates varied between species,with more variation in labile species compared to recalcitrant ones.Despite substantial variation in forest characteristics within sites,their influence on litter decomposition was minimal and declined over time.This suggests that,at finer spatial scales,the forest microenvironment plays a lesser role in litter decomposition,with litter quality emerging as the primary driver.This study is a step towards understanding the fine-scale dynamics of litter decomposition in upper Andean tropical forests,highlighting the intricate interplay between microenvironmental factors and decomposition processes.
基金funded by the Global PhD Scholarship between KU Leuven and UCLouvain。
文摘Litter decomposition is an essential ecosystem process influenced by multiple factors,including substrate quality,climate,edaphic environment,and decomposer communities.However,the role of canopy species identity and diversity on leaf litter decomposition in forests remains understudied.By controlling for macroclimate,soil properties,and litter substrate in a mature common garden,we investigated whether a three-month tea bag incubation of standardized green and rooibos tea substrate is driven by canopy tree species characteristics and diversity.Our study hypothesized two primary pathways:a chemical engineering effect,where trees alter soil properties and decomposer communities through litter input,and a physical engineering effect,where tree canopy structure modulates the local microclimate.The results showed that even under uniform macroclimatic and initial soil conditions,mass loss rates varied widely for green tea(27.4%–73.2%)and rooibos tea(6.1%–34.7%),comparable as found in other research between distinct biomes.While substrate quality was the dominant factor,both engineering pathways and,to a minor extent,tree diversity modulated mass losses.For green tea,tree chemical and physical characteristics seemed equally important,while the physical environment showed an increased importance for rooibos.Incubation depth played a key role,where forest floor decomposition rates are more susceptible to temporal climate variations,and soil-layer decomposition rates are less susceptible to climate variations and more determined by tree species identity.Our findings suggest that tea bag experiments focusing solely on topsoil burial may underestimate processes in the forest floor and the mineralorganic boundary layer.This study underscores the critical role of litter substrate quality in decomposition while demonstrating that tree community composition and the associated herbaceous layer,through both chemical and physical engineering pathways,strongly modulate decomposition rates.
文摘Theauthor proposes a dual layer source grid load storage collaborative planning model based on Benders decomposition to optimize the low-carbon and economic performance of the distribution network.The model plans the configuration of photovoltaic(3.8 MW),wind power(2.5 MW),energy storage(2.2 MWh),and SVC(1.2 Mvar)through interaction between upper and lower layers,and modifies lines 2–3,8–9,etc.to improve transmission capacity and voltage stability.The author uses normal distribution and Monte Carlo method to model load uncertainty,and combines Weibull distribution to describe wind speed characteristics.Compared to the traditional three-layer model(TLM),Benders decomposition-based two-layer model(BLBD)has a 58.1%reduction in convergence time(5.36 vs.12.78 h),a 51.1%reduction in iteration times(23 vs.47 times),a 8.07%reduction in total cost(12.436 vs.13.528 million yuan),and a 9.62%reduction in carbon emissions(12,456 vs.13,782 t).After optimization,the peak valley difference decreased from4.1 to 2.9MW,the renewable energy consumption rate reached 93.4%,and the energy storage efficiency was 87.6%.Themodel has been validated in the IEEE 33 node system,demonstrating its superiority in terms of economy,low-carbon,and reliability.
基金supported by State Grid Sichuan Electric Power Company science and technology project“Research on Key Technologies for Reclosing of High-Ratio New Energy Grid Connection Lines.”(Program No:52199723002Q).
文摘The integration of a high proportion of renewable energy introduces significant challenges for the adaptability of traditional fault nature identification methods.To address these challenges,this paper presents a novel fault nature identification method for renewable energy grid-connected interconnection lines,leveraging wavelet packet decomposition and voltage waveform time-frequency morphology comparison algorithms.First,the paper investigates the harmonic injection mechanism during non-full-phase operation following fault isolation in photovoltaic renewable energy systems,and examines the voltage characteristics of faulted phases in renewable energy scenarios.The analysis reveals that substantial differences exist in both the time and frequency domains of phase voltages before and after the extinction of transient faults,whereas permanent faults do not exhibit such variations.Building on this observation,the paper proposes a voltage time-frequency feature extraction method based on wavelet packet decomposition,wherein low-frequency waveform components are selected to characterize fault features.Subsequently,a fault nature identification method is introduced,based on a voltage waveform time-frequency morphology comparison.By employing a windowing technique to quantify waveform differences before and after arc extinction,this method effectively distinguishes between permanent and transient faults and accurately determines the arc extinction time.Finally,a 220 kV renewable energy grid connection line model is developed using PSCAD for verification.The results demonstrate that the proposed method is highly adaptable across various fault locations,transition resistances,and renewable energy control strategies,and can reliably identify fault nature in renewable energy grid connection scenarios.
基金supported by the National Key R&D Program of China (No. 2021YFB3700700)the Henan Province Top Talent Training Program Project, China (No. 244500510020)the High-level Talent Research Start-up Project Funding of Henan Academy of Sciences, China (No. 242017001)。
文摘The microstructural evolution of Cu−19Ni−6Cr−7Mn alloy during aging treatment was investigated.After aging for 120 min at 500℃,the alloy exhibited excellent mechanical properties,including a tensile strength of 978 MPa and an elastic modulus of 145.8 GPa.After aging for 240 min at 500℃,the elastic modulus of the alloy reached 149.5 GPa,which was among the highest values reported for Cu alloys.It was worth mentioning that the tensile strength increased rapidly from 740 to 934 MPa after aging for 5 min at 500℃,which was close to the maximum tensile strength(978 MPa).Analysis of the underlying strengthening mechanisms and phase transformation behavior revealed that the Cu−19Ni−6Cr−7Mn alloy underwent spinodal decomposition and DO_(22) ordering during the first 5 min of aging at 500℃,and L1_(2) ordered phases and bcc-Cr precipitates appeared.Therefore,the enhanced mechanical properties of the Cu−19Ni−6Cr−7Mn alloy can be attributed to the stress field generated by spinodal decomposition and the presence of nanoscale ordered phase and Cr precipitates.
基金National Key Research and Development Program of China under Grant No.2022YFC3003601National Natural Science Foundation of China under Grant No.52478570+1 种基金Heilongjiang Provincial Natural Science Foundation Outstanding Youth Program under Grant No.J020245002the Key Research and Development Program of Xinjiang Production and Construction Corps under Grant No.2024AB077。
文摘The stochastic extended finite-fault simulation method(EXSIM)is a widely used tool in seismological research,with applications in ground motion prediction and simulation,seismic hazard analysis,and engineering studies.However,recent studies have revealed a significant limitation:EXSIM tends to overpredict ground motions in the low-to-intermediate frequency range,particularly for large thrust earthquakes that are often characterized by a double-corner-frequency source model.To address this issue and enhance simulation accuracy,this study introduces two key improvements:(1)a novel asperity-distributed stress-drop composite fault model and(2)a hybrid application of EXSIM with the composite fault model.The proposed method is validated through its application to the 2013 M_(w)6.7 Lushan earthquake that occurred in China and six thrust earthquakes with an M_(w)≥6.5 in Japan.By comparing the simulated ground motions with recorded data,the results demonstrate that the improved method achieves consistent accuracy across the high-and low-frequency spectrum(combined goodness-of-fit:CGOF<0.35).This study significantly broadens the applicability of stochastic finite-fault simulations,enabling more reliable predictions for a wider range of seismic scenarios,including complex thrust faulting events.
基金supported in part by the National Natural Science Foundation of China(No.52467008)Gansu Provincial Depatment of Education Youth Doctoral Suppo Project(2024QB-051).
文摘Addressing the limitations of inadequate stochastic disturbance characterization during wind turbine degradation processes that result in constrained modeling accuracy,replacement-based maintenance practices that deviate from actual operational conditions,and static maintenance strategies that fail to adapt to accelerated deterioration trends leading to suboptimal remaining useful life utilization,this study proposes a Time-Based Incomplete Maintenance(TBIM)strategy incorporating reliability constraints through stochastic differential equations(SDE).By quantifying stochastic interference via Brownian motion terms and characterizing nonlinear degradation features through state influence rate functions,a high-precision SDE degradation model is constructed,achieving 16%residual reduction compared to conventional ordinary differential equation(ODE)methods.The introduction of age reduction factors and failure rate growth factors establishes an incomplete maintenance mechanism that transcends traditional“as-good-as-new”assumptions,with the TBIM model demonstrating an additional 8.5%residual reduction relative to baseline SDE approaches.A dynamic maintenance interval optimization model driven by dual parameters—preventive maintenance threshold R_(p) and replacement threshold R_(r)—is designed to achieve synergistic optimization of equipment reliability and maintenance economics.Experimental validation demonstrates that the optimized TBIM extends equipment lifespan by 4.4%and reducesmaintenance costs by 4.16%at R_(p)=0.80,while achieving 17.2%lifespan enhancement and 14.6%cost reduction at R_(p)=0.90.This methodology provides a solution for wind turbine preventive maintenance that integrates condition sensitivity with strategic foresight.
基金funded by the National Natural Science Foundation of China,grant number U24A20135Inner Mongolia Natural Science Foundation major project,grant number 2023ZD12+7 种基金Inner Mongolia Autonomous Region key research and development and achievement transformation plan project,grant number 2023YFHH0090Natural Science Foundation of Inner Mongolia,grant number 2022MS05006Inner Mongolia Autonomous Region Talent Development FundUniversity basic research business expenses,grant number 2023RCTD012University basic research business expenses,grant number 2023QNJS075Postgraduate Research Innovation Program and of Inner Mongolia Autonomous Region,grant number KC2024053BUniversity basic research business expenses,grant number 2024YXXS012National Key Laboratory of Special Vehicle Design and Manufacturing Integration Technology,grant number GZ2023KF012.
文摘In dry-coupled ultrasonic thickness measurement,thick rubber layers introduce high-amplitude parasitic echoes that obscure defect signals and degrade thickness accuracy.Existing methods struggle to resolve overlap-ping echoes under variable coupling conditions and non-stationary noise.This study proposes a novel dual-criterion framework integrating energy contribution and statistical impulsivity metrics to isolate specimen re-flections from coupling-layer interference.By decomposing A-scan signals into Intrinsic Mode Functions(IMFs),the framework employs energy contribution thresholds(>85%)and kurtosis indices(>3)to autonomously select IMFs containing valid specimen echoes.Hybrid time-frequency thresholding further suppresses interference through amplitude filtering and spectral focusing.Experimental results demonstrate the framework’s robustness,achieving 92.3%thickness accuracy for 5 mm steel specimens with 5 mm rubber coupling,outperforming conventional methods by up to 18.7%.The dual-criterion approach reduces operator dependency by 37%and maintainsΔT<0.03 mm under surface roughness up to 6.3μm,offering a practical solution for industrial nondestructive testing with thick dry-coupled interfaces.
基金supported by the National Natural Science Foundation of China(62303353,62273030,62573320)。
文摘Dear Editor,This letter studies the problem of stealthy attacks targeting stochastic event-based estimation,alongside proposing measures for their mitigation.A general attack framework is introduced,and the corresponding stealthiness condition is analyzed.To enhance system security,we advocate for a single-dimensional encryption method,showing that securing a singular data element is sufficient to shield the system from the perils of stealthy attacks.
文摘We develop and implement a Stochastic Discrete Event Simulation(SDES)algorithm to model the housing re-covery trajectory after an extreme event.The algorithm models discrete events and their underlying uncertainties in each construction phase.Specifically,the algorithm is developed for the Government Assisted Owner Driven(GAOD)reconstruction system to simulate long-term recovery trajectory.SDES,as a flexible modeling approach,can simulate any housing recovery scenario that follows phased reconstruction.The 2015 M 7.8 Gorkha earthquake sequence in Nepal is considered the extreme event,with 796,245 buildings requiring reconstruction.We present some recovery trajectories from severely hit,crisis hit,and earthquake hit parishes,comparing them with the actual reconstruction progress.We also assess quality and improvement of reconstructed buildings using seismic fragility functions,compared to pre-earthquake constructions.Housing recovery uncertainties are dissected in relation to reconstruction pace.We conclude that the vast majority of the reconstructed buildings followed the Build Back Better(BBB)approach and missed the opportunity to pursue the Build Back Resilient(BBR)approach due to multifaceted challenges ranging from unclear policies to economic constraints.We critically assess the GAOD vs Owner Driven(OD)recovery framework and conclude that insurance-supported and technically assisted OD approach could be the most suitable model for post extreme event housing recovery.
基金supported by the China Postdoctoral Science Foundation Funded Project(Grant Nos.2017M613054 and 2017M613053)the Shaanxi Postdoctoral Science Foundation Funded Project(Grant No.2017BSHYDZZ33)the National Science Foundation of China(Grant No.62102239).
文摘Deep neural networks often outperform classical machine learning algorithms in solving real-world problems.However,designing better networks usually requires domain expertise and consumes significant time and com-puting resources.Moreover,when the task changes,the original network architecture becomes outdated and requires redesigning.Thus,Neural Architecture Search(NAS)has gained attention as an effective approach to automatically generate optimal network architectures.Most NAS methods mainly focus on achieving high performance while ignoring architectural complexity.A myriad of research has revealed that network performance and structural complexity are often positively correlated.Nevertheless,complex network structures will bring enormous computing resources.To cope with this,we formulate the neural architecture search task as a multi-objective optimization problem,where an optimal architecture is learned by minimizing the classification error rate and the number of network parameters simultaneously.And then a decomposition-based multi-objective stochastic fractal search method is proposed to solve it.In view of the discrete property of the NAS problem,we discretize the stochastic fractal search step size so that the network architecture can be optimized more effectively.Additionally,two distinct update methods are employed in step size update stage to enhance the global and local search abilities adaptively.Furthermore,an information exchange mechanism between architectures is raised to accelerate the convergence process and improve the efficiency of the algorithm.Experimental studies show that the proposed algorithm has competitive performance comparable to many existing manual and automatic deep neural network generation approaches,which achieved a parameter-less and high-precision architecture with low-cost on each of the six benchmark datasets.
