Implication of community-level ecophysiological parameterization to modelling ecosystem productivity:a case study across nine contrasting forest sites in eastern China

Parameterization is a critical step in modelling ecosystem dynamics.However,assigning parameter values can be a technical challenge for structurally complex natural plant communities;uncertainties in model simulations often arise from inappropriate model parameterization.Here we compared five methods for defining community-level specific leaf area(SLA)and leaf C:N across nine contrasting forest sites along the North-South Transect of Eastern China,including biomass-weighted average for the entire plant community(AP_BW)and four simplified selective sampling(biomass-weighted average over five dominant tree species[5DT_BW],basal area weighted average over five dominant tree species[5DT_AW],biomass-weighted average over all tree species[AT_BW]and basal area weighted average over all tree species[AT_AW]).We found that the default values for SLA and leaf C:N embedded in the Biome-BGC v4.2 were higher than the five computational methods produced across the nine sites,with deviations ranging from 28.0 to 73.3%.In addition,there were only slight deviations(<10%)between the whole plant community sampling(AP_BW)predicted NPP and the four simplified selective sampling methods,and no significant difference between the predictions of AT_BW and AP_BW except the Shennongjia site.The findings in this study highlights the critical importance of computational strategies for community-level parameterization in ecosystem process modelling,and will support the choice of parameterization methods.

Bidirectional rotating direct-current triboelectric nanogenerator with self-adaptive mechanical switching for harvesting reciprocating motion

Amid the growing interest in triboelectric nanogenerators(TENGs)as novel energy-harvesting devices,several studies have focused on direct current(DC)TENGs to generate a stable DC output for operating electronic devices.However,owing to the working mechanisms of conventional DC TENGs,generating a stable DC output from reciprocating motion remains a challenge.Accordingly,we propose a bidirectional rotating DC TENG(BiR-TENG),which can generate DC outputs,regardless of the direction of rotation,from reciprocating motions.The distinct design of the BiR-TENG enables the mechanical rectification of the alternating current output into a rotational-direction-dependent DC output.Furthermore,it allows the conversion of the rotational-direction-dependent DC output into a unidirectional DC output by adapting the configurations depending on the rotational direction.Owing to these tailored design strategies and subsequent optimizations,the BiR-TENG could generate an effective unidirectional DC output.Applications of the BiR-TENG for the reciprocating motions of swinging doors and waves were demonstrated by harnessing this output.This study demonstrates the potential of the BiR-TENG design strategy as an effective and versatile solution for energy harvesting from reciprocating motions,highlighting the suitability of DC outputs as an energy source for electronic devices.

Performance of physical-informed neural network (PINN) for the key parameter inference in Langmuir turbulence parameterization scheme

The Stokes production coefficient(E_(6))constitutes a critical parameter within the Mellor-Yamada type(MY-type)Langmuir turbulence(LT)parameterization schemes,significantly affecting the simulation of turbulent kinetic energy,turbulent length scale,and vertical diffusivity coefficient for turbulent kinetic energy in the upper ocean.However,the accurate determination of its value remains a pressing scientific challenge.This study adopted an innovative approach by leveraging deep learning technology to address this challenge of inferring the E_(6).Through the integration of the information of the turbulent length scale equation into a physical-informed neural network(PINN),we achieved an accurate and physically meaningful inference of E_(6).Multiple cases were examined to assess the feasibility of PINN in this task,revealing that under optimal settings,the average mean squared error of the E_(6) inference was only 0.01,attesting to the effectiveness of PINN.The optimal hyperparameter combination was identified using the Tanh activation function,along with a spatiotemporal sampling interval of 1 s and 0.1 m.This resulted in a substantial reduction in the average bias of the E_(6) inference,ranging from O(10^(1))to O(10^(2))times compared with other combinations.This study underscores the potential application of PINN in intricate marine environments,offering a novel and efficient method for optimizing MY-type LT parameterization schemes.

Dynamic Economic Scheduling with Self-Adaptive Uncertainty in Distribution Network Based on Deep Reinforcement Learning

Traditional optimal scheduling methods are limited to accurate physical models and parameter settings, which aredifficult to adapt to the uncertainty of source and load, and there are problems such as the inability to make dynamicdecisions continuously. This paper proposed a dynamic economic scheduling method for distribution networksbased on deep reinforcement learning. Firstly, the economic scheduling model of the new energy distributionnetwork is established considering the action characteristics of micro-gas turbines, and the dynamic schedulingmodel based on deep reinforcement learning is constructed for the new energy distribution network system with ahigh proportion of new energy, and the Markov decision process of the model is defined. Secondly, Second, for thechanging characteristics of source-load uncertainty, agents are trained interactively with the distributed networkin a data-driven manner. Then, through the proximal policy optimization algorithm, agents adaptively learn thescheduling strategy and realize the dynamic scheduling decision of the new energy distribution network system.Finally, the feasibility and superiority of the proposed method are verified by an improved IEEE 33-node simulationsystem.

Parameterization, sensitivity, and uncertainty of 1-D thermodynamic thin-ice thickness retrieval

Retrieval of Thin-Ice Thickness(TIT)using thermodynamic modeling is sensitive to the parameterization of the independent variables(coded in the model)and the uncertainty of the measured input variables.This article examines the deviation of the classical model’s TIT output when using different parameterization schemes and the sensitivity of the output to the ice thickness.Moreover,it estimates the uncertainty of the output in response to the uncertainties of the input variables.The parameterized independent variables include atmospheric longwave emissivity,air density,specific heat of air,latent heat of ice,conductivity of ice,snow depth,and snow conductivity.Measured input parameters include air temperature,ice surface temperature,and wind speed.Among the independent variables,the results show that the highest deviation is caused by adjusting the parameterization of snow conductivity and depth,followed ice conductivity.The sensitivity of the output TIT to ice thickness is highest when using parameterization of ice conductivity,atmospheric emissivity,and snow conductivity and depth.The retrieved TIT obtained using each parameterization scheme is validated using in situ measurements and satellite-retrieved data.From in situ measurements,the uncertainties of the measured air temperature and surface temperature are found to be high.The resulting uncertainties of TIT are evaluated using perturbations of the input data selected based on the probability distribution of the measurement error.The results show that the overall uncertainty of TIT to air temperature,surface temperature,and wind speed uncertainty is around 0.09 m,0.049 m,and−0.005 m,respectively.

A New Result on Regular Designs under Baseline Parameterization

The study on designs for the baseline parameterization has aroused attention in recent years. This paper focuses on two-level regular designs for the baseline parameterization. A general result on the relationship between K-aberration and word length pattern is developed.

Impact of Revised Trigger and Closure of the Double-Plume Convective Parameterization on Precipitation Simulations over East Asia

A double-plume convective parameterization scheme is revised to improve the precipitation simulation of a global model(Global-to-Regional Integrated Forecast System;GRIST).The improvement is achieved by considering the effects of large-scale dynamic processes on the trigger of deep convection.The closure,based on dynamic CAPE,is improved accordingly to allow other processes to consume CAPE under the more restricted convective trigger condition.The revised convective parameterization is evaluated with a variable-resolution model setup(110–35 km,refined over East Asia).The Atmospheric Model Intercomparison Project(AMIP)simulations demonstrate that the revised convective parameterization substantially delays the daytime precipitation peaks over most land areas,leading to an improved simulated diurnal cycle,evidenced by delayed and less frequent afternoon precipitation.Meanwhile,changes to the threshold of the trigger function yield a small impact on the diurnal amplitude of precipitation because of the consistent setting of dCAPE-based trigger and closure.The simulated mean precipitation remains reasonable,with some improvements evident along the southern slopes of the Tibetan Plateau.The revised scheme increases convective precipitation at the lower levels of the windward slope and reduces the large-scale precipitation over the upper slope,ultimately shifting the rainfall peak southward,which is in better agreement with the observations.

Impact of seepage flow on sediment resuspension by internal solitary waves:parameterization and mechanism

Sediment incipient motion is the first step in sediment resuspension.Previous studies ignored the effect of seepage flow on the mobility of sediment particles and simplified the seabed surface as a rigid boundary.A flume experiment was designed to innovatively divide the seabed into two parts to control the dynamic response of the seabed and control the seepage conditions.In the experiment,the seabed sediments and the amplitude of internal solitary waves(ISWs)were changed to compare and analyze the impact of seepage flow on the sediment resuspension by shoaling ISWs.Moreover,parametric research and verification were carried out.Results indicate that seepage flow can greatly influence fine sand,promote sediment resuspension,and increase the amount of suspension by two times on average.However,seepage flow had a little effect on the suspension of clayey silt and sandy silt.Besides,seepage force was added to the traditional gravity,drag force,and uplift force,and the parameterization of threshold starting shear stress of coarse-grained sediments was developed.The results of this parameterization were verified,and seepage force was critical to parameterization.The threshold starting shear stress was reduced by 54.6%after increasing the seepage force.The physical mechanism of this process corresponded to the vertical reciprocating transient seepage in and out the seabed interface caused by the wave-induced transient excess pore water pressure.This quantitative study on seepage flow for shear stress of coarse-grained sediments induced by ISWs is critical to geohazard assessment.

Parameterization Transfer for a Planar Computational Domain in Isogeometric Analysis

In this paper,we propose a parameterization transfer algorithm for planar domains bounded by B-spline curves,where the shapes of the planar domains are similar.The domain geometries are considered to be similar if their simplified skeletons have the same structures.One domain we call source domain,and it is parameterized using multi-patch B-spline surfaces.The resulting parameterization is C1 continuous in the regular region and G1 continuous around singular points regardless of whether the parameterization of the source domain is C1/G1 continuous or not.In this algorithm,boundary control points of the source domain are extracted from its parameterization as sequential points,and we establish a correspondence between sequential boundary control points of the source domain and the target boundary through discrete sampling and fitting.Transfer of the parametrization satisfies C1/G1 continuity under discrete harmonic mapping with continuous constraints.The new algorithm has a lower calculation cost than a decomposition-based parameterization with a high-quality parameterization result.We demonstrate that the result of the parameterization transfer in this paper can be applied in isogeometric analysis.Moreover,because of the consistency of the parameterization for the two models,this method can be applied in many other geometry processing algorithms,such as morphing and deformation.

Variable Parameter Self-Adaptive Control Strategy Based on Driving Condition Identification for Plug-In Hybrid Electric Bus

A variable parameter self-adaptive control strategy based on driving condition identification is proposed to take full advantage of the fuel saving potential of the plug-in hybrid electric bus(PHEB).Firstly,the principal component analysis(PCA)and the fuzzy c-means clustering(FCM)algorithm is used to construct the comprehensive driving cycle,congestion driving cycle,urban driving cycle and suburban driving cycle of Chinese urban buses.Secondly,an improved particle swarm optimization(IPSO)algorithm is proposed,and is used to optimize the control parameters of PHEB under different driving cycles,respectively.Then,the variable parameter self-adaptive control strategy based on driving condition identification is given.Finally,for an actual running vehicle,the driving condition is identified by relevance vector machine(RVM),and the corresponding control parameters are selected to control the vehicle.The simulation results show that the fuel consumption of using the variable parameter self-adaptive control strategy is reduced by 4.2% compared with that of the fixed parameter control strategy,and the feasibility of the variable parameter self-adaptive control strategy is verified.

An Immune Self-adaptive Differential Evolution Algorithm with Application to Estimate Kinetic Parameters for Homogeneous Mercury Oxidation

A new version of differential evolution （DE） algorithm, in which immune concepts and methods are applied to determine the parameter setting, named immune self-adaptive differential evolution （ISDE）, is proposed to improve the performance of the DE algorithm. During the actual operation, ISDE seeks the optimal parameters arising from the evolutionary process, which enable ISDE to alter the algorithm for different optimization problems and improve the performance of ISDE by the control parameters＇ self-adaptation. The .performance of the proposed method is studied with the use of nine benchmark problems and compared with original DE algorithm ~nd-other well-known self-adaptive DE algorithms. The experiments conducted show that the ISDE clearly outperforms the other DE algorithms in all benchmark functions. Furthermore, ISDE is applied to develop the kinetic model for homogeneous mercury. （Hg） oxidation in flue gas, and satisfactory results are obtained.

Drilling-based measuring method for the c-φ parameter of rock and its field application

The technology of drilling tests makes it possible to obtain the strength parameter of rock accurately in situ. In this paper, a new rock cutting analysis model that considers the influence of the rock crushing zone(RCZ) is built. The formula for an ultimate cutting force is established based on the limit equilibrium principle. The relationship between digital drilling parameters(DDP) and the c-φ parameter(DDP-cφ formula, where c refers to the cohesion and φ refers to the internal friction angle) is derived, and the response of drilling parameters and cutting ratio to the strength parameters is analyzed. The drillingbased measuring method for the c-φ parameter of rock is constructed. The laboratory verification test is then completed, and the difference in results between the drilling test and the compression test is less than 6%. On this basis, in-situ rock drilling tests in a traffic tunnel and a coal mine roadway are carried out, and the strength parameters of the surrounding rock are effectively tested. The average difference ratio of the results is less than 11%, which verifies the effectiveness of the proposed method for obtaining the strength parameters based on digital drilling. This study provides methodological support for field testing of rock strength parameters.

Formal Modeling of Self-Adaptive Resource Scheduling in Cloud

A self-adaptive resource provisioning on demand is a critical factor in cloud computing.The selection of accurate amount of resources at run time is not easy due to dynamic nature of requests.Therefore,a self-adaptive strategy of resources is required to deal with dynamic nature of requests based on run time change in workload.In this paper we proposed a Cloud-based Adaptive Resource Scheduling Strategy(CARSS)Framework that formally addresses these issues and is more expressive than traditional approaches.The decision making in CARSS is based on more than one factors.TheMAPE-K based framework determines the state of the resources based on their current utilization.Timed-Arc Petri Net(TAPN)is used to model system formally and behaviour is expressed in TCTL,while TAPAAL model checker verifies the underline properties of the system.

In-situ interfacial passivation and self-adaptability synergistically stabilizing all-solid-state lithium metal batteries

The function of solid electrolytes and the composition of solid electrolyte interphase(SEI)are highly significant for inhibiting the growth of Li dendrites.Herein,we report an in-situ interfacial passivation combined with self-adaptability strategy to reinforce Li_(0.33)La_(0.557)TiO_(3)(LLTO)-based solid-state batteries.Specifically,a functional SEI enriched with LiF/Li_(3)PO_(4) is formed by in-situ electrochemical conversion,which is greatly beneficial to improving interface compatibility and enhancing ion transport.While the polarized dielectric BaTiO_(3)-polyamic acid(BTO-PAA,BP)film greatly improves the Li-ion transport kinetics and homogenizes the Li deposition.As expected,the resulting electrolyte offers considerable ionic conductivity at room temperature(4.3 x 10~(-4)S cm^(-1))and appreciable electrochemical decomposition voltage(5.23 V)after electrochemical passivation.For Li-LiFePO_(4) batteries,it shows a high specific capacity of 153 mA h g^(-1)at 0.2C after 100 cycles and a long-term durability of 115 mA h g^(-1)at 1.0 C after 800 cycles.Additionally,a stable Li plating/stripping can be achieved for more than 900 h at 0.5 mA cm^(-2).The stabilization mechanisms are elucidated by ex-situ XRD,ex-situ XPS,and ex-situ FTIR techniques,and the corresponding results reveal that the interfacial passivation combined with polarization effect is an effective strategy for improving the electrochemical performance.The present study provides a deeper insight into the dynamic adjustment of electrode-electrolyte interfacial for solid-state lithium batteries.

Self-adaptive bulk/surface engineering of Bi_(x)O_(y)Br_(z) towards enhanced photocatalysis:Current status and future challenges

The bulk/surface states of semiconductor photocatalysts are imperative parameters to maneuver their performance by significantly affecting the key processes of photocatalysis including light absorption,separation of charge carrier,and surface site reaction.Recent years have witnessed the encouraging progress of self-adaptive bulk/surface engineered Bi_(x)O_(y)Br_(z) for photocatalytic applications spanning various fields.However,despite the maturity of current research,the interaction between the bulk/surface state and the performance of Bi_(x)O_(y)Br_(z) has not yet been fully understood and highlighted.In this regard,a timely tutorial overview is quite urgent to summarize the most recent key progress and outline developing obstacles in this exciting area.Herein,the structural characteristics and fundamental principles of Bi_(x)O_(y)Br_(z)for driving photocatalytic reaction as well as related key issues are firstly reviewed.Then,we for the first time summarized different self-adaptive engineering processes over Bi_(x)O_(y)Br_(z)followed by a classification of the generation approaches towards diverse Bi_(x)O_(y)Br_(z)materials.The features of different strategies,the up-to-date characterization techniques to detect bulk/surface states,and the effect of bulk/surface states on improving the photoactivity of Bi_(x)O_(y)Br_(z)in expanded applications are further discussed.Finally,the present research status,challenges,and future research opportunities of self-adaptive bulk/surface engineered Bi_(x)O_(y)Br_(z)are prospected.It is anticipated that this critical review can trigger deeper investigations and attract upcoming innovative ideas on the rational design of Bi_(x)O_(y)Br_(z)-based photocatalysts.

The Importance of the Shape Parameter in a Bulk Parameterization Scheme to the Evolution of the Cloud Droplet Spectrum during Condensation

The shape parameter of the Gamma size distribution plays a key role in the evolution of the cloud droplet spectrum in the bulk parameterization schemes. However, due to the inaccurate specification of the shape parameter in the commonly used bulk double-moment schemes, the cloud droplet spectra cannot reasonably be described during the condensation process. Therefore, a newly-developed triple-parameter condensation scheme with the shape parameter diagnosed through the number concentration, cloud water content, and reflectivity factor of cloud droplets can be applied to improve the evolution of the cloud droplet spectrum. The simulation with the new parameterization scheme was compared to those with a high-resolution Lagrangian bin scheme, the double-moment schemes in a parcel model, and the observation in a 1.5D Eulerian model that consists of two cylinders. The new scheme with the shape parameter varying with time and space can accurately simulate the evolution of the cloud droplet spectrum. Furthermore, the volume-mean radius and cloud water content simulated with the new scheme match the Lagrangian analytical solutions well, and the errors are steady, within approximately 0.2%.

A WSN Node Fault Diagnosis Model Based on BRB with Self-Adaptive Quality Factor

Wireless sensor networks (WSNs) operate in complex and harshenvironments;thus, node faults are inevitable. Therefore, fault diagnosis ofthe WSNs node is essential. Affected by the harsh working environment ofWSNs and wireless data transmission, the data collected by WSNs containnoisy data, leading to unreliable data among the data features extracted duringfault diagnosis. To reduce the influence of unreliable data features on faultdiagnosis accuracy, this paper proposes a belief rule base (BRB) with a selfadaptivequality factor (BRB-SAQF) fault diagnosis model. First, the datafeatures required for WSN node fault diagnosis are extracted. Second, thequality factors of input attributes are introduced and calculated. Third, themodel inference process with an attribute quality factor is designed. Fourth,the projection covariance matrix adaptation evolution strategy (P-CMA-ES)algorithm is used to optimize the model’s initial parameters. Finally, the effectivenessof the proposed model is verified by comparing the commonly usedfault diagnosis methods for WSN nodes with the BRB method consideringstatic attribute reliability (BRB-Sr). The experimental results show that BRBSAQFcan reduce the influence of unreliable data features. The self-adaptivequality factor calculation method is more reasonable and accurate than thestatic attribute reliability method.

Performance Evaluation of Three Parameterizations on Internal Tidal Mixing in the Northern Pacific

The accurate assessment of the energy dissipation of internal tides(ITs)is of great importance because ITs contribute significantly to abyssal mixing.Thus,in this study,the IT-driven dissipation and diapycnal diffusion in the northern Pacific are esti-mated using parameterizations proposed by St.Laurent et al.(2002),Koch-Larrouy et al.(2007),and de Lavergne et al.(2020)(hereaf-ter referred to as LSJ02,KL07,and dL20,respectively).The performances of the three parameterizations are evaluated by comparing the calculated results with fine structure observations.In particular,the dissipation estimated by LSJ02 parameterization shows a bottom-intensified characteristic,with the patterns showing good agreement with the observations near seamounts.Moreover,43%of the results calculated using the LSJ02 parameterization have errors lower than one order of magnitude in the generation sites of ITs.Meanwhile,the strongest dissipation estimated by the KL07 parameterization shifts to the thermocline,with the results showing the highest level of consistency with observations in the generation sites.The proportion of results with errors lower than one order of magnitude is 80.7%.Furthermore,the results calculated by dL20 parameterization agree well with the observations in the upper and middle layers,with the parameterization showing an accurate estimation of the remote dissipation.The percentages of the errors lower than one order of magnitude between the dL20 parameterization and observations account for 77.1%and 88.7%in the genera-tion sites and far-field regions,respectively.

Evaluating Parameterizations for Turbulent Fluxes over the Landfast Sea-Ice Surface in Prydz Bay, Antarctica

It is crucial to appropriately determine turbulent fluxes in numerical models.Using data collected in East Antarctica from 8 April to 26 November 2016,this study evaluates parameterization schemes for turbulent fluxes over the landfast seaice surface in five numerical models.The Community Noah Land Surface Model with Multi-Parameterizations Options(Noah_mp)best replicates the turbulent momentum flux,while the Beijing Climate System Model(BCC_CSM)produces the optimum sensible and latent heat fluxes.In particular,two critical issues of parameterization schemes,stability functions and roughness lengths,are investigated.Sensitivity tests indicate that roughness lengths play a decisive role in model performance.Based on the observed turbulent fluxes,roughness lengths over the landfast sea-ice surface are calculated.The results,which can provide a basis for setting up model parameters,reveal that the dynamic roughness length(z0m)increases with the increase of frictional velocity(u*)when u*≤0.4 m s^(−1) and fluctuates around 10^(−3 )m when u*>0.4 m s^(−1);thermal roughness length(z0t)is linearly related to the temperature gradient between air and sea-ice surface(ΔT)with a relation of lg(z0t)=−0.29ΔT−3.86;and the mean water vapor roughness length(z0q)in the specific humidity gradient(Δq)range ofΔq≤−0.6 g kg^(−1) is 10^(−6) m,3.5 times smaller than that in the range ofΔq˃−0.6 g kg^(−1).

Skeleton-Based Volumetric Parameterizations for Lattice Structures

Lattice structures with excellent physical properties have attracted great research interest.In this paper,a novel volume parametric modeling method based on the skeleton model is proposed for the construction of threedimensional lattice structures.The skeleton model is divided into three types of nodes.And the corresponding algorithms are utilized to construct diverse types of volume parametric nodes.The unit-cell is assembled with distinct nodes according to the geometric features.The final lattice structure is created by the periodic arrangement of unit-cells.Several different types of volume parametric lattice structures are constructed to prove the stability and applicability of the proposed method.The quality is assessed in terms of the value of the Jacobian matrix.Moreover,the volume parametric lattice structures are tested with the isogeometric analysis to verify the feasibility of integration of modeling and simulation.