Optimal Parameter and Uncertainty Estimation of a Land Surface Model: Sensitivity to Parameter Ranges and Model Complexities

Most previous land-surface model calibration studies have defined globalranges for their parameters to search for optimal parameter sets. Little work has been conducted tostudy the impacts of realistic versus global ranges as well as model complexities on the calibrationand uncertainty estimates. The primary purpose of this paper is to investigate these impacts byemploying Bayesian Stochastic Inversion (BSI) to the Chameleon Surface Model (CHASM). The CHASM wasdesigned to explore the general aspects of land-surface energy balance representation within acommon modeling framework that can be run from a simple energy balance formulation to a complexmosaic type structure. The BSI is an uncertainty estimation technique based on Bayes theorem,importance sampling, and very fast simulated annealing. The model forcing data and surface flux datawere collected at seven sites representing a wide range of climate and vegetation conditions. Foreach site, four experiments were performed with simple and complex CHASM formulations as well asrealistic and global parameter ranges. Twenty eight experiments were conducted and 50 000 parametersets were used for each run. The results show that the use of global and realistic ranges givessimilar simulations for both modes for most sites, but the global ranges tend to produce someunreasonable optimal parameter values. Comparison of simple and complex modes shows that the simplemode has more parameters with unreasonable optimal values. Use of parameter ranges and modelcomplexities have significant impacts on frequency distribution of parameters, marginal posteriorprobability density functions, and estimates of uncertainty of simulated sensible and latent heatfluxes. Comparison between model complexity and parameter ranges shows that the former has moresignificant impacts on parameter and uncertainty estimations.

Spatial Scale Effects of Water Erosion Dynamics:Complexities, Variabilities, and Uncertainties

Severe water erosion is notorious for its harmful effects on land-water resources as well as local societies. The scale effects of water erosion, however, greatly exacerbate the difficulties of accurate erosion evaluation and hazard control in the real world. Analyzing the related scale issues is thus urgent for a better understanding of erosion variations as well as reducing such erosion. In this review article, water erosion dynamics across three spatial scales including plot, watershed, and regional scales were selected and discussed. For the study purposes and objectives, the advantages and disadvantages of these scales all demonstrate clear spatial-scale dependence. Plot scale studies are primarily focused on abundant data collection and mechanism discrimination of erosion generation, while watershed scale studies provide valuable information for watershed management and hazard control as well as the development of quantitatively distributed models. Regional studies concentrate more on large-scale erosion assessment, and serve policymakers and stakeholders in achieving the basis for regulatory policy for comprehensive land uses. The results of this study show that the driving forces and mechanisms of water erosion variations among the scales are quite different. As a result, several major aspects contributing to variations in water erosion across the scales are stressed: differences in the methodologies across various scales, different sink-source roles on water erosion processes, and diverse climatic zones and morphological regions. This variability becomes more complex in the context of accelerated global change. The changing climatic factors and earth surface features are considered the fourth key reason responsible for the increased variability of water erosion across spatial scales.

全耳内镜下耳屏软骨-软骨膜复合体在上鼓室外侧壁重建中的临床应用

目的探究全耳内镜下耳屏软骨-软骨膜复合体在上鼓室外侧壁重建中的临床应用。方法选取2020年6月~2023年6月间徐州市中心医院收治的58例进行上鼓室外侧壁重建的胆脂瘤患者,根据采用修复材料的不同分为对照组及观察组各29例。对照组采用耳屏软骨膜进行上鼓室外侧壁重建,观察组采用耳屏软骨-软骨膜复合体进行上鼓室外侧壁重建,两组患者均于全耳内镜下进行手术。统计患者术中出血量、手术时间,术后3个月复查听力情况、干耳情况及耳道功能改善情况,术后随访6个月鼓膜愈合情况。结果与术前比较,术后3个月气导听阈、气骨导差降低(P<0.05),与对照组比较,观察组气导听阈、气骨导差降低(P<0.05);与对照组比较,观察组干耳率、耳道功能良好率、听力提高成功率均升高,干耳时间缩短,但耳屏愈合时间增加(P<0.05);与对照组比较,观察组手术时间缩短,术中出血量减少(P<0.05);观察组、对照组术后并发症总发生率分别为13.79%、24.14%,二者比较差异无统计学意义(P>0.05);观察组愈合良好率高于对照组(86.21%vs.62.07%)(P<0.05),两组患者耳膜穿孔(10.34%vs.24.14%)、鼓膜移位(3.45%vs.6.90%)及鼓膜回缩(0.00%vs.6.90%)发生情况比较,差异无统计学意义(P>0.05)。结论全耳内镜下耳屏软骨-软骨膜复合体在上鼓室外侧壁重建中有利于改善患者听力,有利于术后恢复。

Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications

Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, respectively.Then, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation significantly.Finally, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.

Historical biogeography and evolutionary diversification of Lilium(Liliaceae): New insights from plastome phylogenomics

Here, we infer the historical biogeography and evolutionary diversification of the genus Lilium. For this purpose, we used the complete plastomes of 64 currently accepted species in the genus Lilium(14plastomes were newly sequenced) to recover the phylogenetic backbone of the genus and a timecalibrated phylogenetic framework to estimate biogeographical history scenarios and evolutionary diversification rates of Lilium. Our results suggest that ancient climatic changes and geological tectonic activities jointly shaped the distribution range and drove evolutionary radiation of Lilium, including the Middle Miocene Climate Optimum(MMCO), the late Miocene global cooling, as well as the successive uplift of the Qinghai-Tibet Plateau(QTP) and the strengthening of the monsoon climate in East Asia during the late Miocene and the Pliocene. This case study suggests that the unique geological and climatic events in the Neogene of East Asia, in particular the uplift of QTP and the enhancement of monsoonal climate, may have played an essential role in formation of uneven distribution of plant diversity in the Northern Hemisphere.

COMPLETE KAHLER METRICS WITH POSITIVE HOLOMORPHIC SECTIONAL CURVATURES ON CERTAIN LINE BUNDLES(RELATED TO A COHOMOGENEITY ONE POINT OF VIEW ON A YAU CONJECTURE)

In this article,we study Kahler metrics on a certain line bundle over some compact Kahler manifolds to find complete Kahler metrics with positive holomorphic sectional(or bisectional)curvatures.Thus,we apply a strategy to a famous Yau conjecture with a co-homogeneity one geometry.

Role of renin-angiotensin system/angiotensin converting enzyme-2 mechanism and enhanced COVID-19 susceptibility in type 2 diabetes mellitus

Coronavirus disease 2019(COVID-19)is a disease that caused a global pandemic and is caused by infection of severe acute respiratory syndrome coronavirus 2 virus.It has affected over 768 million people worldwide,resulting in approx-imately 6900000 deaths.High-risk groups,identified by the Centers for Disease Control and Prevention,include individuals with conditions like type 2 diabetes mellitus(T2DM),obesity,chronic lung disease,serious heart conditions,and chronic kidney disease.Research indicates that those with T2DM face a hei-ghtened susceptibility to COVID-19 and increased mortality compared to non-diabetic individuals.Examining the renin-angiotensin system(RAS),a vital regulator of blood pressure and pulmonary stability,reveals the significance of the angiotensin-converting enzyme(ACE)and ACE2 enzymes.ACE converts angiotensin-I to the vasoconstrictor angiotensin-II,while ACE2 counters this by converting angiotensin-II to angiotensin 1-7,a vasodilator.Reduced ACE2 exp-ression,common in diabetes,intensifies RAS activity,contributing to conditions like inflammation and fibrosis.Although ACE inhibitors and angiotensin receptor blockers can be therapeutically beneficial by increasing ACE2 levels,concerns arise regarding the potential elevation of ACE2 receptors on cell membranes,potentially facilitating COVID-19 entry.This review explored the role of the RAS/ACE2 mechanism in amplifying severe acute respiratory syndrome cor-onavirus 2 infection and associated complications in T2DM.Potential treatment strategies,including recombinant human ACE2 therapy,broad-spectrum antiviral drugs,and epigenetic signature detection,are discussed as promising avenues in the battle against this pandemic.

Discovery of nano organo-clay complex pore-fractures in shale and its scientific significance:A case study of Cretaceous Qingshankou Formation shale,Songliao Basin,NE China

A new pore type,nano-scale organo-clay complex pore-fracture was first discovered based on argon ion polishing-field emission scanning electron microscopy,energy dispersive spectroscopy and three-dimensional reconstruction by focused ion-scanning electron in combination with analysis of TOC,R_(o)values,X-ray diffraction etc.in the Cretaceous Qingshankou Formation shale in the Songliao Basin,NE China.Such pore characteristics and evolution study show that:(1)Organo-clay complex pore-fractures are developed in the shale matrix and in the form of spongy and reticular aggregates.Different from circular or oval organic pores discovered in other shales,a single organo-clay complex pore is square,rectangular,rhombic or slaty,with the pore diameter generally less than 200 nm.(2)With thermal maturity increasing,the elements(C,Si,Al,O,Mg,Fe,etc.)in organo-clay complex change accordingly,showing that organic matter shrinkage due to hydrocarbon generation and clay mineral transformation both affect organo-clay complex pore-fracture formation.(3)At high thermal maturity,the Qingshankou Formation shale is dominated by nano-scale organo-clay complex pore-fractures with the percentage reaching more than 70%of total pore space.The spatial connectivity of organo-clay complex pore-fractures is significantly better than that of organic pores.It is suggested that organo-complex pore-fractures are the main pore space of laminar shale at high thermal maturity and are the main oil and gas accumulation space in the core area of continental shale oil.The discovery of nano-scale organo-clay complex pore-fractures changes the conventional view that inorganic pores are the main reservoir space and has scientific significance for the study of shale oil formation and accumulation laws.

A novel complex-high-order graph convolutional network paradigm:ChyGCN

In recent years,there has been a growing interest in graph convolutional networks(GCN).However,existing GCN and variants are predominantly based on simple graph or hypergraph structures,which restricts their ability to handle complex data correlations in practical applications.These limitations stem from the difficulty in establishing multiple hierarchies and acquiring adaptive weights for each of them.To address this issue,this paper introduces the latest concept of complex hypergraphs and constructs a versatile high-order multi-level data correlation model.This model is realized by establishing a three-tier structure of complexes-hypergraphs-vertices.Specifically,we start by establishing hyperedge clusters on a foundational network,utilizing a second-order hypergraph structure to depict potential correlations.For this second-order structure,truncation methods are used to assess and generate a three-layer composite structure.During the construction of the composite structure,an adaptive learning strategy is implemented to merge correlations across different levels.We evaluate this model on several popular datasets and compare it with recent state-of-the-art methods.The comprehensive assessment results demonstrate that the proposed model surpasses the existing methods,particularly in modeling implicit data correlations(the classification accuracy of nodes on five public datasets Cora,Citeseer,Pubmed,Github Web ML,and Facebook are 86.1±0.33,79.2±0.35,83.1±0.46,83.8±0.23,and 80.1±0.37,respectively).This indicates that our approach possesses advantages in handling datasets with implicit multi-level structures.

A novel variable-order fractional chaotic map and its dynamics

In recent years,fractional-order chaotic maps have been paid more attention in publications because of the memory effect.This paper presents a novel variable-order fractional sine map(VFSM)based on the discrete fractional calculus.Specially,the order is defined as an iterative function that incorporates the current state of the system.By analyzing phase diagrams,time sequences,bifurcations,Lyapunov exponents and fuzzy entropy complexity,the dynamics of the proposed map are investigated comparing with the constant-order fractional sine map.The results reveal that the variable order has a good effect on improving the chaotic performance,and it enlarges the range of available parameter values as well as reduces non-chaotic windows.Multiple coexisting attractors also enrich the dynamics of VFSM and prove its sensitivity to initial values.Moreover,the sequence generated by the proposed map passes the statistical test for pseudorandom number and shows strong robustness to parameter estimation,which proves the potential applications in the field of information security.

A Case Study Applying Mesoscience to Deep Learning

In this paper,we propose mesoscience-guided deep learning(MGDL),a deep learning modeling approach guided by mesoscience,to study complex systems.When establishing sample dataset based on the same system evolution data,different from the operation of conventional deep learning method,MGDL introduces the treatment of the dominant mechanisms of complex system and interactions between them according to the principle of compromise in competition(CIC)in mesoscience.Mesoscience constraints are then integrated into the loss function to guide the deep learning training.Two methods are proposed for the addition of mesoscience constraints.The physical interpretability of the model-training process is improved by MGDL because guidance and constraints based on physical principles are provided.MGDL was evaluated using a bubbling bed modeling case and compared with traditional techniques.With a much smaller training dataset,the results indicate that mesoscience-constraint-based model training has distinct advantages in terms of convergence stability and prediction accuracy,and it can be widely applied to various neural network configurations.The MGDL approach proposed in this paper is a novel method for utilizing the physical background information during deep learning model training.Further exploration of MGDL will be continued in the future.

Solar hydrogen production from electrochemical ammonia splitting powered by a single perovskite solar cell

For carbon-free electrochemical fuel formation,the electrochemical cell must be powered by renewable energy.Obtaining solar-powered H_(2) fuel from water typically requires multiple photovoltaic cells and/or junctions to drive the water splitting reaction.Because of the lower thermodynamic requirements to oxidize ammonia compared to water,solar cells with smaller open circuit voltages can provide the required potential for ammonia splitting.In this work,a single perovskite solar cell with an open-circuit potential of 1.08 V is coupled to a 2-electrode electrochemical cell employing hybrid electroanodes functionalized with Ru-based molecular catalysts.The device is active for more than 30 min,producing N_(2) and H_(2) in a 1:2.9 ratio with 89%faradaic efficiency with no external applied bias.This work illustrates that hydrogen production from ammonia can be driven by conventional semiconductors.

Chimera states of phase oscillator populations with nonlocal higher-order couplings

The chimera states underlying many realistic dynamical processes have attracted ample attention in the area of dynamical systems.Here, we generalize the Kuramoto model with nonlocal coupling incorporating higher-order interactions encoded with simplicial complexes.Previous works have shown that higher-order interactions promote coherent states.However, we uncover the fact that the introduced higher-order couplings can significantly enhance the emergence of the incoherent state.Remarkably, we identify that the chimera states arise as a result of multi-attractors in dynamic states.Importantly, we review that the increasing higher-order interactions can significantly shape the emergent probability of chimera states.All the observed results can be well described in terms of the dimension reduction method.This study is a step forward in highlighting the importance of nonlocal higher-order couplings, which might provide control strategies for the occurrence of spatial-temporal patterns in networked systems.

Assessment of the Characteristics of Demersal Fish Communities Using Species-and Trait-Based Approaches in the Coastal Habitats Along Rongcheng Bay,Shandong Peninsula,China

Biodiversity declines have motivated many studies on the relationship between species diversity and ecosystem functioning.In this study,we described the spatial-temporal characteristics of demersal fish communities along a coastal habitat in Rongcheng Bay,Shandong Peninsula,China with both species-based and biological trait-based approaches.The field survey was carried out monthly using traps from April to October of 2018,and divided into three seasons(spring:April and May;summer:June,July and August;autumn:September,October and November).The study area included five distinct habitats:seagrass bed,natural rocky reef,bare sand,artificial reef together with natural rocky reef,and artificial reef together with bare sand.We analyzed the fish communities with three taxonomic diversity indices,including Shannon-Wiener,Simpson,and Pielou Evenness,as well as four functional diversity indices,including FRic,FEve,FDiv,and FDis,based on 7 functional groups which are categorized into 27 traits.The results showed that there were no significant differences in taxonomic diversity indices among different habitats in the three seasons.However,significant differences were found in the functional richness of fish communities among different habitats in three seasons.Seagrass bed represented the highest functional richness in spring and autumn.This study demonstrates that seagrass bed is very important in enhancing the functional diversity of fish communities in a complex habitat.The study also indicates that the combination of taxonomic diversity and functional diversity will provide a more detailed description of the characteristics of fish communities.

Prediction of collapse process and tipping points for mutualistic and competitive networks with k-core method

Ecosystems generally have the self-adapting ability to resist various external pressures or disturbances,which is always called resilience.However,once the external disturbances exceed the tipping points of the system resilience,the consequences would be catastrophic,and eventually lead the ecosystem to complete collapse.We capture the collapse process of ecosystems represented by plant-pollinator networks with the k-core nested structural method,and find that a sufficiently weak interaction strength or a sufficiently large competition weight can cause the structure of the ecosystem to collapse from its smallest k-core towards its largest k-core.Then we give the tipping points of structure and dynamic collapse of the entire system from the one-dimensional dynamic function of the ecosystem.Our work provides an intuitive and precise description of the dynamic process of ecosystem collapse under multiple interactions,and provides theoretical insights into further avoiding the occurrence of ecosystem collapse.

Effects of individual heterogeneity on social contagions

Despite having significant effects on social contagions,individual heterogeneity has frequently been overlooked in earlier studies.To better understand the complexity of social contagions,a non-Markovian model incorporating heterogeneous social influence and adoption thresholds is introduced.For theoretical analysis,a generalized edge-based compartmental theory which considers the heterogeneities of social influence and adoption thresholds is developed.Focusing on the final adoption size,the critical propagation probability,and the phase transition type,social contagions for adoption thresholds that follow normal distributions with various standard deviations,follow various distributions,and correlate with degrees are investigated.When thresholds follow normal distributions,a larger standard deviation results in a larger final adoption size when the information propagation probability is relatively low.However,when the information propagation probability is relatively high,a larger standard deviation results in a smaller final adoption size.When thresholds follow various distributions,crossover phenomena in phase transition are observed when investigating the relationship of the final adoption size versus the average adoption threshold for some threshold distributions.When thresholds are correlated with degrees,similar crossover phenomena occur when investigating the relationship of the final adoption size versus the degree correlation index.Additionally,we find that increasing the heterogeneity of social influence suppresses the effects of adoption threshold heterogeneity on social contagions in three cases.Our theory predictions agree well with the simulation results.

Quantum State-Resolved Nonadiabatic Dynamics of the H+NaF→Na+HF Reaction

The H+NaF reaction is investigated at the quantum state-resolved level using the time-dependent wavepacket method based on a set of accurate diabatic potential energy surfaces.Oscillatory structures in the total reaction probability indicate the presence of the short-lived intermediate complex.

Ecological network analysis reveals complex responses of tree species life stage interactions to stand variables

Tree interactions are essential for the structure,dynamics,and function of forest ecosystems,but variations in the architecture of life-stage interaction networks(LSINs)across forests is unclear.Here,we constructed 16 LSINs in the mountainous forests of northwest Hebei,China based on crown overlap from four mixed forests with two dominant tree species.Our results show that LSINs decrease the complexity of stand densities and basal areas due to the interaction cluster differentiation.In addition,we found that mature trees and saplings play different roles,the first acting as“hub”life stages with high connectivity and the second,as“bridges”controlling information flow with high centrality.Across the forests,life stages with higher importance showed better parameter stability within LSINs.These results reveal that the structure of tree interactions among life stages is highly related to stand variables.Our efforts contribute to the understanding of LSIN complexity and provide a basis for further research on tree interactions in complex forest communities.

On fractional discrete financial system:Bifurcation,chaos,and control

The dynamic analysis of financial systems is a developing field that combines mathematics and economics to understand and explain fluctuations in financial markets.This paper introduces a new three-dimensional(3D)fractional financial map and we dissect its nonlinear dynamics system under commensurate and incommensurate orders.As such,we evaluate when the equilibrium points are stable or unstable at various fractional orders.We use many numerical methods,phase plots in 2D and 3D projections,bifurcation diagrams and the maximum Lyapunov exponent.These techniques reveal that financial maps exhibit chaotic attractor behavior.This study is grounded on the Caputo-like discrete operator,which is specifically influenced by the variance of the commensurate and incommensurate orders.Furthermore,we confirm the presence and measure the complexity of chaos in financial maps by the 0-1 test and the approximate entropy algorithm.Additionally,we offer nonlinear-type controllers to stabilize the fractional financial map.The numerical results of this study are obtained using MATLAB.

Function and dysfunction of GEMIN5:understanding a novel neurodevelopmental disorder

The recent identification of a neurodevelopmental disorder with cerebellar atrophy and motor dysfunction(NEDCAM)has resulted in an increased interest in GEMIN5,a multifunction RNA-binding protein.As the largest member of the survival motor neuron complex,GEMIN5 plays a key role in the biogenesis of small nuclear ribonucleoproteins while also exhibiting translational regulatory functions as an independent protein.Although many questions remain regarding both the pathogenesis and pathophysiology of this new disorder,considerable progress has been made in the brief time since its discovery.In this review,we examine GEMIN5 within the context of NEDCAM,focusing on the structure,function,and expression of the protein specifically in regard to the disorder itself.Additionally,we explore the current animal models of NEDCAM,as well as potential molecular pathways for treatment and future directions of study.This review provides a comprehensive overview of recent advances in our understanding of this unique member of the survival motor neuron complex.