A stochastic epidemic model with two age groups is established in this study,in which the susceptible(S),the exposed(E),the infected(I),the hospitalized(H)and the recovered(R)are involved within the total population,t...A stochastic epidemic model with two age groups is established in this study,in which the susceptible(S),the exposed(E),the infected(I),the hospitalized(H)and the recovered(R)are involved within the total population,the aging rates between two age groups are set to be constant.The existence-and-uniqueness of global positive solution is firstly showed.Then,by constructing several appropriate Lyapunov functions and using the high-dimensional Itô’s formula,the sufficient conditions for the stochastic extinction and stochastic persistence of the exposed individuals and the infected individuals are obtained.The stochastic extinction indicator and the stochastic persistence indicator are less-valued expressions compared with the basic reproduction number.Meanwhile,the main results of this study are modified into multi-age groups.Furthermore,by using the surveillance data for Fujian Provincial Center for Disease Control and Prevention,Fuzhou COVID-19 epidemic is chosen to carry out the numerical simulations,which show that the age group of the population plays the vital role when studying infectious diseases.展开更多
The high porosity and tunable chemical functionality of metal-organic frameworks(MOFs)make it a promising catalyst design platform.High-throughput screening of catalytic performance is feasible since the large MOF str...The high porosity and tunable chemical functionality of metal-organic frameworks(MOFs)make it a promising catalyst design platform.High-throughput screening of catalytic performance is feasible since the large MOF structure database is available.In this study,we report a machine learning model for high-throughput screening of MOF catalysts for the CO_(2) cycloaddition reaction.The descriptors for model training were judiciously chosen according to the reaction mechanism,which leads to high accuracy up to 97%for the 75%quantile of the training set as the classification criterion.The feature contribution was further evaluated with SHAP and PDP analysis to provide a certain physical understanding.12,415 hypothetical MOF structures and 100 reported MOFs were evaluated under 100℃ and 1 bar within one day using the model,and 239 potentially efficient catalysts were discovered.Among them,MOF-76(Y)achieved the top performance experimentally among reported MOFs,in good agreement with the prediction.展开更多
In this paper,we propose an RLC equivalent circuit model theory which can accurately predict the spectral response and resonance characteristics of metamaterial absorption structures,extend its design,and characterize...In this paper,we propose an RLC equivalent circuit model theory which can accurately predict the spectral response and resonance characteristics of metamaterial absorption structures,extend its design,and characterize the parameters of the model in detail.By employing this model,we conducted computations to characterize the response wavelength and bandwidth of variously sized metamaterial absorbers.A comparative analysis with Finite Difference Time Domain(FDTD)simulations demonstrated a remarkable level of consistency in the results.The designed absorbers were fabricated using micro-nano fabrication processes,and were experimentally tested to demonstrate absorption rates exceeding 90%at a wavelength of 9.28μm.The predicted results are then compared with test results.The comparison reveals good consistency in two aspects of the resonance responses,thereby confirming the rationality and accuracy of this model.展开更多
We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpr...We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpreting these parameters is crucial for effectively exploring and developing oil and gas.However,with the increasing complexity of geological conditions in this industry,there is a growing demand for improved accuracy in reservoir parameter prediction,leading to higher costs associated with manual interpretation.The conventional logging interpretation methods rely on empirical relationships between logging data and reservoir parameters,which suffer from low interpretation efficiency,intense subjectivity,and suitability for ideal conditions.The application of artificial intelligence in the interpretation of logging data provides a new solution to the problems existing in traditional methods.It is expected to improve the accuracy and efficiency of the interpretation.If large and high-quality datasets exist,data-driven models can reveal relationships of arbitrary complexity.Nevertheless,constructing sufficiently large logging datasets with reliable labels remains challenging,making it difficult to apply data-driven models effectively in logging data interpretation.Furthermore,data-driven models often act as“black boxes”without explaining their predictions or ensuring compliance with primary physical constraints.This paper proposes a machine learning method with strong physical constraints by integrating mechanism and data-driven models.Prior knowledge of logging data interpretation is embedded into machine learning regarding network structure,loss function,and optimization algorithm.We employ the Physically Informed Auto-Encoder(PIAE)to predict porosity and water saturation,which can be trained without labeled reservoir parameters using self-supervised learning techniques.This approach effectively achieves automated interpretation and facilitates generalization across diverse datasets.展开更多
In this paper,a physical model of RIS of bistatic polarized radar cross section is derived starting from the Stratton-Chu equations under the assumptions of physical optics,PEC,far field and rectangular RIS element.In...In this paper,a physical model of RIS of bistatic polarized radar cross section is derived starting from the Stratton-Chu equations under the assumptions of physical optics,PEC,far field and rectangular RIS element.In the context of important physical characteristics of the backscattering polarization of RIS,the modeling of the RIS wireless channel requires a tradeoff between complexity and accuracy,as well as usability and simplicity.For channel modeling of RIS systems,RIS is modelled as multi-equivalent virtual base stations(BSs)induced by multi polarized electromagnetic waves from different incident directions.The comparison between test and simulation results demonstrates that the proposed algorithm effectively captures the key characteristics of the general RIS element polarization physical model and provides accurate results.展开更多
The kinetic characteristics of plasma-assisted oxidative pyrolysis of ammonia are studied by using the global/fluid models hybrid solution method.Firstly,the stable products of plasma-assisted oxidative pyrolysis of a...The kinetic characteristics of plasma-assisted oxidative pyrolysis of ammonia are studied by using the global/fluid models hybrid solution method.Firstly,the stable products of plasma-assisted oxidative pyrolysis of ammonia are measured.The results show that the consumption of NH_(3)/O_(2)and the production of N_(2)/H_(2)change linearly with the increase of voltage,which indicates the decoupling of nonequilibrium molecular excitation and oxidative pyrolysis of ammonia at low temperatures.Secondly,the detailed reaction kinetics mechanism of ammonia oxidative pyrolysis stimulated by a nanosecond pulse voltage at low pressure and room temperature is established.Based on the reaction path analysis,the simplified mechanism is obtained.The detailed and simplified mechanism simulation results are compared with experimental data to verify the accuracy of the simplified mechanism.Finally,based on the simplified mechanism,the fluid model of ammonia oxidative pyrolysis stimulated by the nanosecond pulse plasma is established to study the pre-sheath/sheath behavior and the resultant consumption and formation of key species.The results show that the generation,development,and propagation of the pre-sheath have a great influence on the formation and consumption of species.The consumption of NH_(3)by the cathode pre-sheath is greater than that by the anode pre-sheath,but the opposite is true for OH and O(1S).However,within the sheath,almost all reactions do not occur.Further,by changing the parameters of nanosecond pulse power supply voltage,it is found that the electron number density,electron current density,and applied peak voltages are not the direct reasons for the structural changes of the sheath and pre-sheath.Furthermore,the discharge interval has little effect on the sheath structure and gas mixture breakdown.The research results of this paper not only help to understand the kinetic promotion of non-equilibrium excitation in the process of oxidative pyrolysis but also help to explore the influence of transport and chemical reaction kinetics on the oxidative pyrolysis of ammonia.展开更多
Short rotation plantation forestry(SRF)is being widely adopted to increase wood production,in order to meet global demand for wood products.However,to ensure maximum gains from SRF,optimised management regimes need to...Short rotation plantation forestry(SRF)is being widely adopted to increase wood production,in order to meet global demand for wood products.However,to ensure maximum gains from SRF,optimised management regimes need to be established by integrating robust predictions and an understanding of mechanisms underlying tree growth.Hybrid ecophysiological models,such as potentially useable light sum equation(PULSE)models,are useful tools requiring minimal input data that meet the requirements of SRF.PULSE models have been tested and calibrated for different evergreen conifers and broadleaves at both juvenile and mature stages of tree growth with coarse soil and climate data.Therefore,it is prudent to question:can adding detailed soil and climatic data reduce errors in this type of model?In addition,PULSE techniques have not been used to model deciduous species,which are a challenge for ecophysiological models due to their phenology.This study developed a PULSE model for a clonal Populus tomentosa plantation in northern China using detailed edaphic and climatic data.The results showed high precision and low bias in height(m)and basal area(m^(2)·ha^(-1))predictions.While detailed edaphoclimatic data produce highly precise predictions and a good mechanistic understanding,the study suggested that local climatic data could also be employed.The study showed that PULSE modelling in combination with coarse level of edaphic and local climate data resulted in reasonably precise tree growth prediction and minimal bias.展开更多
The significant threat of wildfires to forest ecology and biodiversity,particularly in tropical and subtropical regions,underscores the necessity for advanced predictive models amidst shifting climate patterns.There i...The significant threat of wildfires to forest ecology and biodiversity,particularly in tropical and subtropical regions,underscores the necessity for advanced predictive models amidst shifting climate patterns.There is a need to evaluate and enhance wildfire prediction methods,focusing on their application during extended periods of intense heat and drought.This study reviews various wildfire modelling approaches,including traditional physical,semi-empirical,numerical,and emerging machine learning(ML)-based models.We critically assess these models’capabilities in predicting fire susceptibility and post-ignition spread,highlighting their strengths and limitations.Our findings indicate that while traditional models provide foundational insights,they often fall short in dynamically estimating parameters and predicting ignition events.Cellular automata models,despite their potential,face challenges in data integration and computational demands.Conversely,ML models demonstrate superior efficiency and accuracy by leveraging diverse datasets,though they encounter interpretability issues.This review recommends hybrid modelling approaches that integrate multiple methods to harness their combined strengths.By incorporating data assimilation techniques with dynamic forecasting models,the predictive capabilities of ML-based predictions can be significantly enhanced.This review underscores the necessity for continued refinement of these models to ensure their reliability in real-world applications,ultimately contributing to more effective wildfire mitigation and management strategies.Future research should focus on improving hybrid models and exploring new data integration methods to advance predictive capabilities.展开更多
The carbon emissions and cost during the construction phase are significant contributors to the oilfield lifecycle.As oilfields enter the late stage,the adaptability of facilities decreases.To achieve sustainable deve...The carbon emissions and cost during the construction phase are significant contributors to the oilfield lifecycle.As oilfields enter the late stage,the adaptability of facilities decreases.To achieve sustainable development,oilfield reconstruction was usually conducted in discrete rather than continuous space.Motivated by economic and sustainability goals,a 3-phase heuristic model for oilfield reconstruction was developed to mine potential locations in continuous space.In phase 1,considering the process characteristics of the oil and gas gathering system,potential locations were mined in continuous space.In phase 2,incorporating comprehensive reconstruction measures,a reconstruction model was established in discrete space.In phase 3,the topology was further adjusted in continuous space.Subsequently,the model was transformed into a single-objective mixed integer linear programming model using the augmented ε-constraint method.Numerical experiments revealed that the small number of potential locations could effectively reduce the reconstruction cost,and the quality of potential locations mined in phase 1 surpassed those generated in random or grid form.Case studies showed that cost and carbon emissions for a new block were reduced by up to 10.45% and 7.21 %,respectively.These reductions were because the potential locations mined in 1P reduced the number of metering stations,and 3P adjusted the locations of metering stations in continuous space to shorten the pipeline length.For an old oilfield,the load and connection ratios of the old metering station increased to 89.7% and 94.9%,respectively,enhancing operation efficiency.Meanwhile,recycling facilitated the diversification of reconstruction measures and yielded a profit of 582,573 ￥,constituting 5.56% of the total cost.This study adopted comprehensive reconstruction measures and tapped into potential reductions in cost and carbon emissions for oilfield reconstruction,offering valuable insights for future oilfield design and construction.展开更多
To examine the similarities and differences in the evolution of cavity,wetting and dynamics of a highspeed,oblique water-entry projectile with different positive angles of attack,a comparative analysis has been conduc...To examine the similarities and differences in the evolution of cavity,wetting and dynamics of a highspeed,oblique water-entry projectile with different positive angles of attack,a comparative analysis has been conducted based on the numerical results of two mathematical models,the rigid-body model and fluid-structure interaction model.In addition,the applicable scope of the above two methods,and the structural response characteristics of the projectile have also been investigated.Our results demonstrate that:(1) The impact loads and angular motion of the projectile of the rigid-body method are more likely to exhibit periodic variations due to the periodic tail slap,its range of positive angles of attack is about α<2°.(2) When the projectile undergone significant wetting,a strong coupling effect is observed among wetting,structural deformation,and projectile motion.With the applied projectile shape,it is observed that,when the projectile bends,the final wetting position is that of Part B(cylinder of body).With the occu rrence of this phenomenon,the projectile ballistics beco me completely unstable.(3) The force exerted on the lower surface of the projectile induced by wetting is the primary reason of the destabilization of the projectile traj ectory and structu ral deformation failure.Bending deformation is most likely to appear at the junction of Part C(cone of body) and Part D(tail).The safe angles of attack of the projectile stability are found to be about α≤2°.展开更多
Using a dynamic laser monitoring technique,the solubility of 3-nitro-1,2,4-triazole-5-one(NTO)was investigated in two different binary systems,namely hydroxylamine nitrate(HAN)-water and boric acid(HB)-water ranging f...Using a dynamic laser monitoring technique,the solubility of 3-nitro-1,2,4-triazole-5-one(NTO)was investigated in two different binary systems,namely hydroxylamine nitrate(HAN)-water and boric acid(HB)-water ranging from 278.15 K to 318.15 K.The solubility in each system was found to be positively correlated with temperature.Furthermore,solubility data were analyzed using four equations:the modified Apelblat equation,Van’t Hoff equation,λh equation and CNIBS/R-K equations,and they provided satisfactory results for both two systems.The average root-mean-square deviation(105RMSD)values for these models were less than 13.93.Calculations utilizing the Van’t Hoff equation and Gibbs equations facilitated the derivation of apparent thermodynamic properties of NTO dissolution in the two systems,including values for Gibbs free energy,enthalpy and entropy.The%ζ_(H)is larger than%ζ_(TS),and all the%ζ_(H)data are≥58.63%,indicating that the enthalpy make a greater contribution than entropy to theΔG_(soln)^(Θ).展开更多
The effect of alcoholic polyethylene-vinyl acetate(EVA)product ethylene-vinyl alcohol copolymer(EVAL)on the low-temperature flow properties of model oil containing asphaltene(ASP)was investigated.The change of wax cry...The effect of alcoholic polyethylene-vinyl acetate(EVA)product ethylene-vinyl alcohol copolymer(EVAL)on the low-temperature flow properties of model oil containing asphaltene(ASP)was investigated.The change of wax crystal microscopic morphology of model oil before and after modification were examined,and the influence of asphaltene mass fraction on the rheological improvement effect of EVAL was analyzed.The composite system of EVAL and asphaltene significantly reduced the pour point,gel point,apparent viscosity,storage modulus and loss modulus of waxy oil at low temperatures.When the EVAL concentration is 400 ppm and the asphaltene mass fraction is 0.5 wt%,the synergistic effect of the two is optimal,which can reduce the pour point by 17℃and the modulus value by more than 98%.The introduction of EVAL strengthens the interaction between asphaltenes and wax crystals,forming EVALASP aggregates,which promote the adsorption of wax crystals on asphaltenes to form composite particles,and the polar groups prevent the aggregation of wax crystals and reduce the size of wax crystals,thus greatly improving the fluidity of waxy oils.展开更多
A novel method is introduced to optimize the traditional Skanavi model by decomposing the electric field of molecules into the electric field of ions and quantitatively describing the ionic-scale electric field by the...A novel method is introduced to optimize the traditional Skanavi model by decomposing the electric field of molecules into the electric field of ions and quantitatively describing the ionic-scale electric field by the structural coefficient of the effective electric field.Furthermore,the optimization of the Skanavi model is demonstrated and the ferroelectric phase transition of BaTiO_(3)crystals is revealed by calculating the optical and static permittivities of BaTiO_(3),CaTiO_(3),and SrTiO_(3)crystals and the structure coefficients of the effective electric field of BT crystals after Ti4+displacement.This research compensates for the deficiencies of the traditional Skanavi model and refines the theoretical framework for analyzing dielectric properties in high permittivity materials.展开更多
Purpose:Evaluating the quality of academic journal articles is a time consuming but critical task for national research evaluation exercises,appointments and promotion.It is therefore important to investigate whether ...Purpose:Evaluating the quality of academic journal articles is a time consuming but critical task for national research evaluation exercises,appointments and promotion.It is therefore important to investigate whether Large Language Models(LLMs)can play a role in this process.Design/methodology/approach:This article assesses which ChatGPT inputs(full text without tables,figures,and references;title and abstract;title only)produce better quality score estimates,and the extent to which scores are affected by ChatGPT models and system prompts.Findings:The optimal input is the article title and abstract,with average ChatGPT scores based on these(30 iterations on a dataset of 51 papers)correlating at 0.67 with human scores,the highest ever reported.ChatGPT 4o is slightly better than 3.5-turbo(0.66),and 4o-mini(0.66).Research limitations:The data is a convenience sample of the work of a single author,it only includes one field,and the scores are self-evaluations.Practical implications:The results suggest that article full texts might confuse LLM research quality evaluations,even though complex system instructions for the task are more effective than simple ones.Thus,whilst abstracts contain insufficient information for a thorough assessment of rigour,they may contain strong pointers about originality and significance.Finally,linear regression can be used to convert the model scores into the human scale scores,which is 31%more accurate than guessing.Originality/value:This is the first systematic comparison of the impact of different prompts,parameters and inputs for ChatGPT research quality evaluations.展开更多
Moiré superlattices provide a new platform to engineer various many-body problems. In this work, we consider arrays of quantum dots(QD) realized on semiconductor moiré superlattices with a deep moiré po...Moiré superlattices provide a new platform to engineer various many-body problems. In this work, we consider arrays of quantum dots(QD) realized on semiconductor moiré superlattices with a deep moiré potential. We diagonalize single QD with multiple electrons, and find degenerate ground states serving as local degrees of freedom(qudits) in the superlattice. With a deep moiré potential, the hopping and exchange interaction between nearby QDs become irrelevant,and the direct Coulomb interaction of the density–density type dominates. Therefore, nearby QDs must arrange the spatial densities to optimize the Coulomb energy. When the local Hilbert space has a two-fold orbital degeneracy, we find that a square superlattice realizes an anisotropic XY model, while a triangular superlattice realizes a generalized XY model with geometric frustration.展开更多
DNA double-strand breaks(DSBs)may result in cellular mutations,apoptosis,and cell death,making them critical determinants of cellular survival and functionality,as well as major mechanisms underlying cell death.The su...DNA double-strand breaks(DSBs)may result in cellular mutations,apoptosis,and cell death,making them critical determinants of cellular survival and functionality,as well as major mechanisms underlying cell death.The success of nanodosimetry lies in the reduction in the number of modeling parameters to be adjusted for the model to predict experimental data on radiation biology.Based on this background,this study modified and simplified the logistic nanodosimetry model(LNDM)based on radiation-induced DSB probability.The probability distribution of ionization cluster size P(v|Q)under irradiation with carbon-ion beams was obtained through a track-structure Monte Carlo(MC)simulation,and then,the nanodosimetric quantities and DSB probability were calculated.Combining the assumptions of the linear quadratic(LQ)model and LNDM,DSB probability-based modification and simplification of the LNDM were conducted.Additionally,based on the radiobiological experimental data of human salivary gland(HSG),Chinese hamster lung(V79),and Chinese hamster ovary(CHO-K1)cells,the least-squares method was used to optimize the parameters of the modified LNDM(mLNDM).The mLNDM accurately reproduced the experimental data of HSG,V79,and CHO-K1 cells,and the results showed that the model parameters r and m_(0) were independent of the cell type,that is,the biological effects of cells with different radiosensitivities can be characterized by adjusting only the model parameters k and P_(s→l).Compared with HSG and CHO-K1 cells,V79 cells had smaller k and P_(s→l)values,indicating that that DSBs have a lower probability of eventually causing lethal damage,and sublethal events are less likely to interact to form lethal events,thereby having radioresistant characteristics.Compared with the LNDM,the mLNDM eliminates the tedious derivation process and connects the quantities characterizing radiation quality at the nanoscale level using radiation biological effects in a more direct and easy-to-understand manner,thus providing a simpler and more accurate method for calculating relative biological effectiveness for ion-beam treatment planning.展开更多
The integration of artificial intelligence(AI)technology,particularly large language models(LLMs),has become essential across various sectors due to their advanced language comprehension and generation capabilities.De...The integration of artificial intelligence(AI)technology,particularly large language models(LLMs),has become essential across various sectors due to their advanced language comprehension and generation capabilities.Despite their transformative impact in fields such as machine translation and intelligent dialogue systems,LLMs face significant challenges.These challenges include safety,security,and privacy concerns that undermine their trustworthiness and effectiveness,such as hallucinations,backdoor attacks,and privacy leakage.Previous works often conflated safety issues with security concerns.In contrast,our study provides clearer and more reasonable definitions for safety,security,and privacy within the context of LLMs.Building on these definitions,we provide a comprehensive overview of the vulnerabilities and defense mechanisms related to safety,security,and privacy in LLMs.Additionally,we explore the unique research challenges posed by LLMs and suggest potential avenues for future research,aiming to enhance the robustness and reliability of LLMs in the face of emerging threats.展开更多
BACKGROUND:Intracranial hemorrhage (ICH),a severe complication among adults receiving extracorporeal membrane oxygenation (ECMO),is often related to poor outcomes.This study aimed to establish a predictive model for I...BACKGROUND:Intracranial hemorrhage (ICH),a severe complication among adults receiving extracorporeal membrane oxygenation (ECMO),is often related to poor outcomes.This study aimed to establish a predictive model for ICH in adults receiving ECMO treatment.METHODS:Adults who received ECMO between January 2017 and June 2022 were the subjects of a single-center retrospective study.Patients under the age of 18 years old,with acute ICH before ECMO,with less than 24 h of ECMO support,and with incomplete data were excluded.ICH was diagnosed by a head computed tomography scan.The outcomes included the incidence of ICH,in-hosptial mortality and 28-day mortality.Multivariate logistic regression analysis was used to identify relevant risk factors of ICH,and a predictive model of ICH with a nomogram was constructed.RESULTS:Among the 227 patients included,22 developed ICH during ECMO.Patients with ICH had higher in-hospital mortality (90.9%vs.47.8%,P=0.001) and higher 28-day mortality (81.8%vs.47.3%,P=0.001) than patients with non-ICH.ICH was associated with decreased grey-white-matter ratio (GWR)(OR=0.894,95%CI:0.841–0.951,P<0.001),stroke history (OR=4.265,95%CI:1.052–17.291,P=0.042),fresh frozen plasma (FFP) transfusion (OR=1.208,95%CI:1.037–1.408,P=0.015)and minimum platelet (PLT) count during ECMO support (OR=0.977,95%CI:0.958–0.996,P=0.019).The area under the receiver operating characteristic curve of the ICH predictive model was 0.843 (95%CI:0.762–0.924,P<0.001).CONCLUSION:ECMO-treated patients with ICH had a higher risk of death.GWR,stroke history,FFP transfusion,and the minimum PLT count were independently associated with ICH,and the ICH predictive model showed that these parameters performed well as diagnostic tools.展开更多
The rate of fire spread is a key indicator for assessing forest fire risk and developing fire management plans.The Rothermel model is the most widely used fire spread model,established through laboratory experiments o...The rate of fire spread is a key indicator for assessing forest fire risk and developing fire management plans.The Rothermel model is the most widely used fire spread model,established through laboratory experiments on homogeneous fuels but has not been validated for conifer-deciduous mixed fuel.In this study,Pinus koraiensis and Quercus mongolica litter was used in a laboratory burning experiment to simulate surface fire spread in the field.The effects of fuel moisture content,mixed fuel ratio and slope on spread rate were analyzed.The optimum packing ratio,moisture-damping coefficient and slope parameters in the Rothermel model were modified using the measured spread rate which was positively correlated with slope and negatively with fuel moisture content.As the Q.mongolica load increased,the spread rate increased and was highest at a fuel ratio of 4:6.The model with modified optimal packing ratio and slope parameters has a significantly lower spread rate prediction error than the unmodified model.The spread rate prediction accuracy was significantly improved after modifying the model parameters based on spread rates from laboratory burning simulations.展开更多
In order to solve the problems of short network lifetime and high data transmission delay in data gathering for wireless sensor network(WSN)caused by uneven energy consumption among nodes,a hybrid energy efficient clu...In order to solve the problems of short network lifetime and high data transmission delay in data gathering for wireless sensor network(WSN)caused by uneven energy consumption among nodes,a hybrid energy efficient clustering routing base on firefly and pigeon-inspired algorithm(FF-PIA)is proposed to optimise the data transmission path.After having obtained the optimal number of cluster head node(CH),its result might be taken as the basis of producing the initial population of FF-PIA algorithm.The L′evy flight mechanism and adaptive inertia weighting are employed in the algorithm iteration to balance the contradiction between the global search and the local search.Moreover,a Gaussian perturbation strategy is applied to update the optimal solution,ensuring the algorithm can jump out of the local optimal solution.And,in the WSN data gathering,a onedimensional signal reconstruction algorithm model is developed by dilated convolution and residual neural networks(DCRNN).We conducted experiments on the National Oceanic and Atmospheric Administration(NOAA)dataset.It shows that the DCRNN modeldriven data reconstruction algorithm improves the reconstruction accuracy as well as the reconstruction time performance.FF-PIA and DCRNN clustering routing co-simulation reveals that the proposed algorithm can effectively improve the performance in extending the network lifetime and reducing data transmission delay.展开更多
基金Supported by National Natural Science Foundation of China(61911530398,12231012)Consultancy Project by the Chinese Academy of Engineering(2022-JB-06,2023-JB-12)+3 种基金the Natural Science Foundation of Fujian Province of China(2021J01621)Special Projects of the Central Government Guiding Local Science and Technology Development(2021L3018)Royal Society of Edinburgh(RSE1832)Engineering and Physical Sciences Research Council(EP/W522521/1).
文摘A stochastic epidemic model with two age groups is established in this study,in which the susceptible(S),the exposed(E),the infected(I),the hospitalized(H)and the recovered(R)are involved within the total population,the aging rates between two age groups are set to be constant.The existence-and-uniqueness of global positive solution is firstly showed.Then,by constructing several appropriate Lyapunov functions and using the high-dimensional Itô’s formula,the sufficient conditions for the stochastic extinction and stochastic persistence of the exposed individuals and the infected individuals are obtained.The stochastic extinction indicator and the stochastic persistence indicator are less-valued expressions compared with the basic reproduction number.Meanwhile,the main results of this study are modified into multi-age groups.Furthermore,by using the surveillance data for Fujian Provincial Center for Disease Control and Prevention,Fuzhou COVID-19 epidemic is chosen to carry out the numerical simulations,which show that the age group of the population plays the vital role when studying infectious diseases.
基金financial support from the National Key Research and Development Program of China(2021YFB 3501501)the National Natural Science Foundation of China(No.22225803,22038001,22108007 and 22278011)+1 种基金Beijing Natural Science Foundation(No.Z230023)Beijing Science and Technology Commission(No.Z211100004321001).
文摘The high porosity and tunable chemical functionality of metal-organic frameworks(MOFs)make it a promising catalyst design platform.High-throughput screening of catalytic performance is feasible since the large MOF structure database is available.In this study,we report a machine learning model for high-throughput screening of MOF catalysts for the CO_(2) cycloaddition reaction.The descriptors for model training were judiciously chosen according to the reaction mechanism,which leads to high accuracy up to 97%for the 75%quantile of the training set as the classification criterion.The feature contribution was further evaluated with SHAP and PDP analysis to provide a certain physical understanding.12,415 hypothetical MOF structures and 100 reported MOFs were evaluated under 100℃ and 1 bar within one day using the model,and 239 potentially efficient catalysts were discovered.Among them,MOF-76(Y)achieved the top performance experimentally among reported MOFs,in good agreement with the prediction.
基金Supported by the National Natural Science Foundation of China(62174092)the Open Fund of State Key Laboratory of Infrared Physics(SITP-NLIST-ZD-2023-04)the Strategic Priority Research Program of the Chinese Academy of Sciences(XDB0580000)。
文摘In this paper,we propose an RLC equivalent circuit model theory which can accurately predict the spectral response and resonance characteristics of metamaterial absorption structures,extend its design,and characterize the parameters of the model in detail.By employing this model,we conducted computations to characterize the response wavelength and bandwidth of variously sized metamaterial absorbers.A comparative analysis with Finite Difference Time Domain(FDTD)simulations demonstrated a remarkable level of consistency in the results.The designed absorbers were fabricated using micro-nano fabrication processes,and were experimentally tested to demonstrate absorption rates exceeding 90%at a wavelength of 9.28μm.The predicted results are then compared with test results.The comparison reveals good consistency in two aspects of the resonance responses,thereby confirming the rationality and accuracy of this model.
文摘We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpreting these parameters is crucial for effectively exploring and developing oil and gas.However,with the increasing complexity of geological conditions in this industry,there is a growing demand for improved accuracy in reservoir parameter prediction,leading to higher costs associated with manual interpretation.The conventional logging interpretation methods rely on empirical relationships between logging data and reservoir parameters,which suffer from low interpretation efficiency,intense subjectivity,and suitability for ideal conditions.The application of artificial intelligence in the interpretation of logging data provides a new solution to the problems existing in traditional methods.It is expected to improve the accuracy and efficiency of the interpretation.If large and high-quality datasets exist,data-driven models can reveal relationships of arbitrary complexity.Nevertheless,constructing sufficiently large logging datasets with reliable labels remains challenging,making it difficult to apply data-driven models effectively in logging data interpretation.Furthermore,data-driven models often act as“black boxes”without explaining their predictions or ensuring compliance with primary physical constraints.This paper proposes a machine learning method with strong physical constraints by integrating mechanism and data-driven models.Prior knowledge of logging data interpretation is embedded into machine learning regarding network structure,loss function,and optimization algorithm.We employ the Physically Informed Auto-Encoder(PIAE)to predict porosity and water saturation,which can be trained without labeled reservoir parameters using self-supervised learning techniques.This approach effectively achieves automated interpretation and facilitates generalization across diverse datasets.
基金supported by Ministry of Science and Technology of the People’s Republic of China(2020YFB1808101)the Project“5G evolution wireless air interface intelligent R&D and verification public platform project”supported by Ministry of Industry and Information Technology of the People’s Republic of China(TC220A04M).
文摘In this paper,a physical model of RIS of bistatic polarized radar cross section is derived starting from the Stratton-Chu equations under the assumptions of physical optics,PEC,far field and rectangular RIS element.In the context of important physical characteristics of the backscattering polarization of RIS,the modeling of the RIS wireless channel requires a tradeoff between complexity and accuracy,as well as usability and simplicity.For channel modeling of RIS systems,RIS is modelled as multi-equivalent virtual base stations(BSs)induced by multi polarized electromagnetic waves from different incident directions.The comparison between test and simulation results demonstrates that the proposed algorithm effectively captures the key characteristics of the general RIS element polarization physical model and provides accurate results.
基金Fundamental Research Funds for the Central Universities(M23JBZY00050)National Natural Science Foundation of China(22278032)。
文摘The kinetic characteristics of plasma-assisted oxidative pyrolysis of ammonia are studied by using the global/fluid models hybrid solution method.Firstly,the stable products of plasma-assisted oxidative pyrolysis of ammonia are measured.The results show that the consumption of NH_(3)/O_(2)and the production of N_(2)/H_(2)change linearly with the increase of voltage,which indicates the decoupling of nonequilibrium molecular excitation and oxidative pyrolysis of ammonia at low temperatures.Secondly,the detailed reaction kinetics mechanism of ammonia oxidative pyrolysis stimulated by a nanosecond pulse voltage at low pressure and room temperature is established.Based on the reaction path analysis,the simplified mechanism is obtained.The detailed and simplified mechanism simulation results are compared with experimental data to verify the accuracy of the simplified mechanism.Finally,based on the simplified mechanism,the fluid model of ammonia oxidative pyrolysis stimulated by the nanosecond pulse plasma is established to study the pre-sheath/sheath behavior and the resultant consumption and formation of key species.The results show that the generation,development,and propagation of the pre-sheath have a great influence on the formation and consumption of species.The consumption of NH_(3)by the cathode pre-sheath is greater than that by the anode pre-sheath,but the opposite is true for OH and O(1S).However,within the sheath,almost all reactions do not occur.Further,by changing the parameters of nanosecond pulse power supply voltage,it is found that the electron number density,electron current density,and applied peak voltages are not the direct reasons for the structural changes of the sheath and pre-sheath.Furthermore,the discharge interval has little effect on the sheath structure and gas mixture breakdown.The research results of this paper not only help to understand the kinetic promotion of non-equilibrium excitation in the process of oxidative pyrolysis but also help to explore the influence of transport and chemical reaction kinetics on the oxidative pyrolysis of ammonia.
基金The National Key Research and Development Program of China(Grant No.2021YFD2201203)the 5·5 Engineering Research&Innovation Team Project of Beijing Forestry University(No.BLRC2023C05)the Key Research and Development Program of Shandong Province(No.2021SFGC02050102)。
文摘Short rotation plantation forestry(SRF)is being widely adopted to increase wood production,in order to meet global demand for wood products.However,to ensure maximum gains from SRF,optimised management regimes need to be established by integrating robust predictions and an understanding of mechanisms underlying tree growth.Hybrid ecophysiological models,such as potentially useable light sum equation(PULSE)models,are useful tools requiring minimal input data that meet the requirements of SRF.PULSE models have been tested and calibrated for different evergreen conifers and broadleaves at both juvenile and mature stages of tree growth with coarse soil and climate data.Therefore,it is prudent to question:can adding detailed soil and climatic data reduce errors in this type of model?In addition,PULSE techniques have not been used to model deciduous species,which are a challenge for ecophysiological models due to their phenology.This study developed a PULSE model for a clonal Populus tomentosa plantation in northern China using detailed edaphic and climatic data.The results showed high precision and low bias in height(m)and basal area(m^(2)·ha^(-1))predictions.While detailed edaphoclimatic data produce highly precise predictions and a good mechanistic understanding,the study suggested that local climatic data could also be employed.The study showed that PULSE modelling in combination with coarse level of edaphic and local climate data resulted in reasonably precise tree growth prediction and minimal bias.
基金funding enabled and organized by CAUL and its Member Institutions.
文摘The significant threat of wildfires to forest ecology and biodiversity,particularly in tropical and subtropical regions,underscores the necessity for advanced predictive models amidst shifting climate patterns.There is a need to evaluate and enhance wildfire prediction methods,focusing on their application during extended periods of intense heat and drought.This study reviews various wildfire modelling approaches,including traditional physical,semi-empirical,numerical,and emerging machine learning(ML)-based models.We critically assess these models’capabilities in predicting fire susceptibility and post-ignition spread,highlighting their strengths and limitations.Our findings indicate that while traditional models provide foundational insights,they often fall short in dynamically estimating parameters and predicting ignition events.Cellular automata models,despite their potential,face challenges in data integration and computational demands.Conversely,ML models demonstrate superior efficiency and accuracy by leveraging diverse datasets,though they encounter interpretability issues.This review recommends hybrid modelling approaches that integrate multiple methods to harness their combined strengths.By incorporating data assimilation techniques with dynamic forecasting models,the predictive capabilities of ML-based predictions can be significantly enhanced.This review underscores the necessity for continued refinement of these models to ensure their reliability in real-world applications,ultimately contributing to more effective wildfire mitigation and management strategies.Future research should focus on improving hybrid models and exploring new data integration methods to advance predictive capabilities.
基金supported by the National Natural Science Foundation of China (Grant No.52174065)the National Natural Science Foundation of China (Grant No.52304071)+1 种基金China University of Petroleum,Beijing (Grant No.ZX20220040)MOE Key Laboratory of Petroleum Engineering (China University of Petroleum,No.2462024PTJS002)。
文摘The carbon emissions and cost during the construction phase are significant contributors to the oilfield lifecycle.As oilfields enter the late stage,the adaptability of facilities decreases.To achieve sustainable development,oilfield reconstruction was usually conducted in discrete rather than continuous space.Motivated by economic and sustainability goals,a 3-phase heuristic model for oilfield reconstruction was developed to mine potential locations in continuous space.In phase 1,considering the process characteristics of the oil and gas gathering system,potential locations were mined in continuous space.In phase 2,incorporating comprehensive reconstruction measures,a reconstruction model was established in discrete space.In phase 3,the topology was further adjusted in continuous space.Subsequently,the model was transformed into a single-objective mixed integer linear programming model using the augmented ε-constraint method.Numerical experiments revealed that the small number of potential locations could effectively reduce the reconstruction cost,and the quality of potential locations mined in phase 1 surpassed those generated in random or grid form.Case studies showed that cost and carbon emissions for a new block were reduced by up to 10.45% and 7.21 %,respectively.These reductions were because the potential locations mined in 1P reduced the number of metering stations,and 3P adjusted the locations of metering stations in continuous space to shorten the pipeline length.For an old oilfield,the load and connection ratios of the old metering station increased to 89.7% and 94.9%,respectively,enhancing operation efficiency.Meanwhile,recycling facilitated the diversification of reconstruction measures and yielded a profit of 582,573 ￥,constituting 5.56% of the total cost.This study adopted comprehensive reconstruction measures and tapped into potential reductions in cost and carbon emissions for oilfield reconstruction,offering valuable insights for future oilfield design and construction.
基金supported by the Postgraduate Research&Practice Innovation Program of Jiangsu Province(Grant No.KYCX24_0714).
文摘To examine the similarities and differences in the evolution of cavity,wetting and dynamics of a highspeed,oblique water-entry projectile with different positive angles of attack,a comparative analysis has been conducted based on the numerical results of two mathematical models,the rigid-body model and fluid-structure interaction model.In addition,the applicable scope of the above two methods,and the structural response characteristics of the projectile have also been investigated.Our results demonstrate that:(1) The impact loads and angular motion of the projectile of the rigid-body method are more likely to exhibit periodic variations due to the periodic tail slap,its range of positive angles of attack is about α<2°.(2) When the projectile undergone significant wetting,a strong coupling effect is observed among wetting,structural deformation,and projectile motion.With the applied projectile shape,it is observed that,when the projectile bends,the final wetting position is that of Part B(cylinder of body).With the occu rrence of this phenomenon,the projectile ballistics beco me completely unstable.(3) The force exerted on the lower surface of the projectile induced by wetting is the primary reason of the destabilization of the projectile traj ectory and structu ral deformation failure.Bending deformation is most likely to appear at the junction of Part C(cone of body) and Part D(tail).The safe angles of attack of the projectile stability are found to be about α≤2°.
文摘Using a dynamic laser monitoring technique,the solubility of 3-nitro-1,2,4-triazole-5-one(NTO)was investigated in two different binary systems,namely hydroxylamine nitrate(HAN)-water and boric acid(HB)-water ranging from 278.15 K to 318.15 K.The solubility in each system was found to be positively correlated with temperature.Furthermore,solubility data were analyzed using four equations:the modified Apelblat equation,Van’t Hoff equation,λh equation and CNIBS/R-K equations,and they provided satisfactory results for both two systems.The average root-mean-square deviation(105RMSD)values for these models were less than 13.93.Calculations utilizing the Van’t Hoff equation and Gibbs equations facilitated the derivation of apparent thermodynamic properties of NTO dissolution in the two systems,including values for Gibbs free energy,enthalpy and entropy.The%ζ_(H)is larger than%ζ_(TS),and all the%ζ_(H)data are≥58.63%,indicating that the enthalpy make a greater contribution than entropy to theΔG_(soln)^(Θ).
基金financially supported by the National Natural Science Foundation of China(No.52076036)。
文摘The effect of alcoholic polyethylene-vinyl acetate(EVA)product ethylene-vinyl alcohol copolymer(EVAL)on the low-temperature flow properties of model oil containing asphaltene(ASP)was investigated.The change of wax crystal microscopic morphology of model oil before and after modification were examined,and the influence of asphaltene mass fraction on the rheological improvement effect of EVAL was analyzed.The composite system of EVAL and asphaltene significantly reduced the pour point,gel point,apparent viscosity,storage modulus and loss modulus of waxy oil at low temperatures.When the EVAL concentration is 400 ppm and the asphaltene mass fraction is 0.5 wt%,the synergistic effect of the two is optimal,which can reduce the pour point by 17℃and the modulus value by more than 98%.The introduction of EVAL strengthens the interaction between asphaltenes and wax crystals,forming EVALASP aggregates,which promote the adsorption of wax crystals on asphaltenes to form composite particles,and the polar groups prevent the aggregation of wax crystals and reduce the size of wax crystals,thus greatly improving the fluidity of waxy oils.
基金Project supported by the National Natural Science Foundation of China(Grant No.51277138)the Natural Science Basic Research Program of Shaanxi Province of China(Grant No.2021JM-442)the Fund from the Shaanxi Provincial Science and Technology Department for Qin Chuangyuan Scientist+Engineer Team(Grant No.2024QCY-KXJ-194)。
文摘A novel method is introduced to optimize the traditional Skanavi model by decomposing the electric field of molecules into the electric field of ions and quantitatively describing the ionic-scale electric field by the structural coefficient of the effective electric field.Furthermore,the optimization of the Skanavi model is demonstrated and the ferroelectric phase transition of BaTiO_(3)crystals is revealed by calculating the optical and static permittivities of BaTiO_(3),CaTiO_(3),and SrTiO_(3)crystals and the structure coefficients of the effective electric field of BT crystals after Ti4+displacement.This research compensates for the deficiencies of the traditional Skanavi model and refines the theoretical framework for analyzing dielectric properties in high permittivity materials.
文摘Purpose:Evaluating the quality of academic journal articles is a time consuming but critical task for national research evaluation exercises,appointments and promotion.It is therefore important to investigate whether Large Language Models(LLMs)can play a role in this process.Design/methodology/approach:This article assesses which ChatGPT inputs(full text without tables,figures,and references;title and abstract;title only)produce better quality score estimates,and the extent to which scores are affected by ChatGPT models and system prompts.Findings:The optimal input is the article title and abstract,with average ChatGPT scores based on these(30 iterations on a dataset of 51 papers)correlating at 0.67 with human scores,the highest ever reported.ChatGPT 4o is slightly better than 3.5-turbo(0.66),and 4o-mini(0.66).Research limitations:The data is a convenience sample of the work of a single author,it only includes one field,and the scores are self-evaluations.Practical implications:The results suggest that article full texts might confuse LLM research quality evaluations,even though complex system instructions for the task are more effective than simple ones.Thus,whilst abstracts contain insufficient information for a thorough assessment of rigour,they may contain strong pointers about originality and significance.Finally,linear regression can be used to convert the model scores into the human scale scores,which is 31%more accurate than guessing.Originality/value:This is the first systematic comparison of the impact of different prompts,parameters and inputs for ChatGPT research quality evaluations.
基金Project supported by the National Natural Science Foundation of China (Grant No. 12274005)the National Key Research and Development Program of China (Grant No. 2021YFA1401903)Innovation Program for Quantum Science and Technology (Grant No. 2021ZD0302403)。
文摘Moiré superlattices provide a new platform to engineer various many-body problems. In this work, we consider arrays of quantum dots(QD) realized on semiconductor moiré superlattices with a deep moiré potential. We diagonalize single QD with multiple electrons, and find degenerate ground states serving as local degrees of freedom(qudits) in the superlattice. With a deep moiré potential, the hopping and exchange interaction between nearby QDs become irrelevant,and the direct Coulomb interaction of the density–density type dominates. Therefore, nearby QDs must arrange the spatial densities to optimize the Coulomb energy. When the local Hilbert space has a two-fold orbital degeneracy, we find that a square superlattice realizes an anisotropic XY model, while a triangular superlattice realizes a generalized XY model with geometric frustration.
基金supported by the National Key Research and Development Program of China(No.2022YFC2401503)Key Research and Development Program of Gansu Province(No.23YFFA0010)+1 种基金Natural Science Foundation of Gansu Province(No.23JRRA625)Special Project of Science and Technology Cooperation between Hubei Province and Chinese Academy of Sciences(No.42000021817T300000050)。
文摘DNA double-strand breaks(DSBs)may result in cellular mutations,apoptosis,and cell death,making them critical determinants of cellular survival and functionality,as well as major mechanisms underlying cell death.The success of nanodosimetry lies in the reduction in the number of modeling parameters to be adjusted for the model to predict experimental data on radiation biology.Based on this background,this study modified and simplified the logistic nanodosimetry model(LNDM)based on radiation-induced DSB probability.The probability distribution of ionization cluster size P(v|Q)under irradiation with carbon-ion beams was obtained through a track-structure Monte Carlo(MC)simulation,and then,the nanodosimetric quantities and DSB probability were calculated.Combining the assumptions of the linear quadratic(LQ)model and LNDM,DSB probability-based modification and simplification of the LNDM were conducted.Additionally,based on the radiobiological experimental data of human salivary gland(HSG),Chinese hamster lung(V79),and Chinese hamster ovary(CHO-K1)cells,the least-squares method was used to optimize the parameters of the modified LNDM(mLNDM).The mLNDM accurately reproduced the experimental data of HSG,V79,and CHO-K1 cells,and the results showed that the model parameters r and m_(0) were independent of the cell type,that is,the biological effects of cells with different radiosensitivities can be characterized by adjusting only the model parameters k and P_(s→l).Compared with HSG and CHO-K1 cells,V79 cells had smaller k and P_(s→l)values,indicating that that DSBs have a lower probability of eventually causing lethal damage,and sublethal events are less likely to interact to form lethal events,thereby having radioresistant characteristics.Compared with the LNDM,the mLNDM eliminates the tedious derivation process and connects the quantities characterizing radiation quality at the nanoscale level using radiation biological effects in a more direct and easy-to-understand manner,thus providing a simpler and more accurate method for calculating relative biological effectiveness for ion-beam treatment planning.
基金supported by the National Key R&D Program of China under Grant No.2022YFB3103500the National Natural Science Foundation of China under Grants No.62402087 and No.62020106013+3 种基金the Sichuan Science and Technology Program under Grant No.2023ZYD0142the Chengdu Science and Technology Program under Grant No.2023-XT00-00002-GXthe Fundamental Research Funds for Chinese Central Universities under Grants No.ZYGX2020ZB027 and No.Y030232063003002the Postdoctoral Innovation Talents Support Program under Grant No.BX20230060.
文摘The integration of artificial intelligence(AI)technology,particularly large language models(LLMs),has become essential across various sectors due to their advanced language comprehension and generation capabilities.Despite their transformative impact in fields such as machine translation and intelligent dialogue systems,LLMs face significant challenges.These challenges include safety,security,and privacy concerns that undermine their trustworthiness and effectiveness,such as hallucinations,backdoor attacks,and privacy leakage.Previous works often conflated safety issues with security concerns.In contrast,our study provides clearer and more reasonable definitions for safety,security,and privacy within the context of LLMs.Building on these definitions,we provide a comprehensive overview of the vulnerabilities and defense mechanisms related to safety,security,and privacy in LLMs.Additionally,we explore the unique research challenges posed by LLMs and suggest potential avenues for future research,aiming to enhance the robustness and reliability of LLMs in the face of emerging threats.
基金supported by the National Natural Science Foundation of China (82072159)。
文摘BACKGROUND:Intracranial hemorrhage (ICH),a severe complication among adults receiving extracorporeal membrane oxygenation (ECMO),is often related to poor outcomes.This study aimed to establish a predictive model for ICH in adults receiving ECMO treatment.METHODS:Adults who received ECMO between January 2017 and June 2022 were the subjects of a single-center retrospective study.Patients under the age of 18 years old,with acute ICH before ECMO,with less than 24 h of ECMO support,and with incomplete data were excluded.ICH was diagnosed by a head computed tomography scan.The outcomes included the incidence of ICH,in-hosptial mortality and 28-day mortality.Multivariate logistic regression analysis was used to identify relevant risk factors of ICH,and a predictive model of ICH with a nomogram was constructed.RESULTS:Among the 227 patients included,22 developed ICH during ECMO.Patients with ICH had higher in-hospital mortality (90.9%vs.47.8%,P=0.001) and higher 28-day mortality (81.8%vs.47.3%,P=0.001) than patients with non-ICH.ICH was associated with decreased grey-white-matter ratio (GWR)(OR=0.894,95%CI:0.841–0.951,P<0.001),stroke history (OR=4.265,95%CI:1.052–17.291,P=0.042),fresh frozen plasma (FFP) transfusion (OR=1.208,95%CI:1.037–1.408,P=0.015)and minimum platelet (PLT) count during ECMO support (OR=0.977,95%CI:0.958–0.996,P=0.019).The area under the receiver operating characteristic curve of the ICH predictive model was 0.843 (95%CI:0.762–0.924,P<0.001).CONCLUSION:ECMO-treated patients with ICH had a higher risk of death.GWR,stroke history,FFP transfusion,and the minimum PLT count were independently associated with ICH,and the ICH predictive model showed that these parameters performed well as diagnostic tools.
基金supported by the National Key Research and Development Program of China(Grant No.2020YFC1511603)the Fundamental Research Funds for the Central Universities(2572021BA04).
文摘The rate of fire spread is a key indicator for assessing forest fire risk and developing fire management plans.The Rothermel model is the most widely used fire spread model,established through laboratory experiments on homogeneous fuels but has not been validated for conifer-deciduous mixed fuel.In this study,Pinus koraiensis and Quercus mongolica litter was used in a laboratory burning experiment to simulate surface fire spread in the field.The effects of fuel moisture content,mixed fuel ratio and slope on spread rate were analyzed.The optimum packing ratio,moisture-damping coefficient and slope parameters in the Rothermel model were modified using the measured spread rate which was positively correlated with slope and negatively with fuel moisture content.As the Q.mongolica load increased,the spread rate increased and was highest at a fuel ratio of 4:6.The model with modified optimal packing ratio and slope parameters has a significantly lower spread rate prediction error than the unmodified model.The spread rate prediction accuracy was significantly improved after modifying the model parameters based on spread rates from laboratory burning simulations.
基金partially supported by the National Natural Science Foundation of China(62161016)the Key Research and Development Project of Lanzhou Jiaotong University(ZDYF2304)+1 种基金the Beijing Engineering Research Center of Highvelocity Railway Broadband Mobile Communications(BHRC-2022-1)Beijing Jiaotong University。
文摘In order to solve the problems of short network lifetime and high data transmission delay in data gathering for wireless sensor network(WSN)caused by uneven energy consumption among nodes,a hybrid energy efficient clustering routing base on firefly and pigeon-inspired algorithm(FF-PIA)is proposed to optimise the data transmission path.After having obtained the optimal number of cluster head node(CH),its result might be taken as the basis of producing the initial population of FF-PIA algorithm.The L′evy flight mechanism and adaptive inertia weighting are employed in the algorithm iteration to balance the contradiction between the global search and the local search.Moreover,a Gaussian perturbation strategy is applied to update the optimal solution,ensuring the algorithm can jump out of the local optimal solution.And,in the WSN data gathering,a onedimensional signal reconstruction algorithm model is developed by dilated convolution and residual neural networks(DCRNN).We conducted experiments on the National Oceanic and Atmospheric Administration(NOAA)dataset.It shows that the DCRNN modeldriven data reconstruction algorithm improves the reconstruction accuracy as well as the reconstruction time performance.FF-PIA and DCRNN clustering routing co-simulation reveals that the proposed algorithm can effectively improve the performance in extending the network lifetime and reducing data transmission delay.
