Community structure and species diversity dynamics of a subtropical evergreen broad-leaved forest in China:2005 to 2020

Here,we characterize the temporal and spatial dynamics of forest community structure and species diversity in a subtropical evergreen broad-leaved forest in China.We found that community structure in this forest changed over a 15-year period.Specifically,renewal and death of common species was large,with the renewal of individuals mainly concentrated within a few populations,especially those of Aidia canthioides and Cryptocarya concinna.The numbers of individual deaths for common species were concentrated in the small and mid-diameter level.The spatial distribution of community species diversity fluctuated in each monitoring period,showing a more dispersed diversity after the 15-year study period,and the coefficient of variation on quadrats increased.In 2010,the death and renewal of the community and the spatial variation of species diversity were different compared to other survey years.Extreme weather may have affected species regeneration and community stability in our subtropical monsoon evergreen broad-leaved forests.Our findings suggest that strengthening the monitoring and management of the forest community will help better understand the long-and short-term causes of dynamic fluctuations of community structure and species diversity,and reveal the factors that drive changes in community structure.

The impact of demographic dynamics on food consumption and its environmental outcomes:Evidence from China

With increasing population and changing demographics,food consumption has experienced a significant transition in quantity and quality.However,a dearth of knowledge remains regarding its environmental impacts and how it responds to demographic dynamics,particularly in emerging economies like China.Using the two-stage Quadratic Almost Demand System(QUAIDS)model,this study empirically examines the impact of demographic dynamics on food consumption and its environmental outcomes based on the provincial data from 2000 to 2020 in China.Under various scenarios,according to changes in demographics,we extend our analysis to project the long-term trend of food consumption and its environmental impacts,including greenhouse gas(GHG)emissions,water footprint(WF),and land appropriation(LA).The results reveal that an increase in the proportion of senior people significantly decreases the consumption of grain and livestock meat and increases the consumption of poultry,egg,and aquatic products,particularly for urban residents.Moreover,an increase in the proportion of males in the population leads to higher consumption of poultry and aquatic products.Correspondingly,in the current scenario of an increased aging population and sex ratio,it is anticipated that GHG emissions,WF,and LA are likely to decrease by 1.37,2.52,and 3.56%,respectively.More importantly,in the scenario adhering to the standards of nutritional intake according to the Dietary Guidelines for Chinese Residents in 2022,GHG emissions,WF,and LA in urban areas would increase by 12.78,20.94,and 18.32%,respectively.Our findings suggest that changing demographics should be considered when designing policies to mitigate the diet-environment-health trilemma and achieve sustainable food consumption.

Millisecond dynamics of colloidal suspension studied by X-ray photon correlation spectroscopy at the Shanghai Synchrotron Radiation Facility

X-ray photon correlation spectroscopy(XPCS)has emerged as a powerful tool for probing the nanoscale dynamics of soft condensed matter and strongly correlated materials owing to its high spatial resolution and penetration capabilities.This technique requires high brilliance and beam coherence,which are not directly available at modern synchrotron beamlines in China.To facilitate future XPCS experiments,we modified the optical setup of the newly commissioned BL10U1 USAXS beamline at the Shanghai Synchrotron Radiation Facility(SSRF).Subsequently,we performed XPCS measurements on silica suspensions in glycerol,which were opaque owing to their high concentrations.Images were collected using a high frame rate area detector.A comprehensive analysis was performed,yielding correlation functions and several key dynamic parameters.All the results were consistent with the theory of Brownian motion and demonstrated the feasibility of XPCS at SSRF.Finally,by carefully optimizing the setup and analyzing the algorithms,we achieved a time resolution of 2 ms,which enabled the characterization of millisecond dynamics in opaque systems.

Dynamics and genetic regulation of macronutrient concentrations during grain development in maize

Nitrogen(N), phosphorus(P), and potassium(K) are essential macronutrients that are crucial not only for maize growth and development, but also for crop yield and quality. The genetic basis of macronutrient dynamics and accumulation during grain filling in maize remains largely unknown. In this study, we evaluated grain N, P, and K concentrations in 206 recombinant inbred lines generated from a cross of DH1M and T877 at six time points after pollination. We then calculated conditional phenotypic values at different time intervals to explore the dynamic characteristics of the N, P, and K concentrations. Abundant phenotypic variations were observed in the concentrations and net changes of these nutrients. Unconditional quantitative trait locus(QTL) mapping revealed 41 non-redundant QTLs, including 17, 16, and 14 for the N, P, and K concentrations, respectively. Conditional QTL mapping uncovered 39 non-redundant QTLs related to net changes in the N, P, and K concentrations. By combining QTL, gene expression, co-expression analysis, and comparative genomic data, we identified 44, 36, and 44 candidate genes for the N, P, and K concentrations, respectively, including GRMZM2G371058 encoding a Doftype zinc finger DNA-binding family protein, which was associated with the N concentration, and GRMZM2G113967encoding a CBL-interacting protein kinase, which was related to the K concentration. The results deepen our understanding of the genetic factors controlling N, P, and K accumulation during maize grain development and provide valuable genes for the genetic improvement of nutrient concentrations in maize.

A stable implicit nodal integration-based particle finite element method(N-PFEM)for modelling saturated soil dynamics

In this study,we present a novel nodal integration-based particle finite element method(N-PFEM)designed for the dynamic analysis of saturated soils.Our approach incorporates the nodal integration technique into a generalised Hellinger-Reissner(HR)variational principle,creating an implicit PFEM formulation.To mitigate the volumetric locking issue in low-order elements,we employ a node-based strain smoothing technique.By discretising field variables at the centre of smoothing cells,we achieve nodal integration over cells,eliminating the need for sophisticated mapping operations after re-meshing in the PFEM.We express the discretised governing equations as a min-max optimisation problem,which is further reformulated as a standard second-order cone programming(SOCP)problem.Stresses,pore water pressure,and displacements are simultaneously determined using the advanced primal-dual interior point method.Consequently,our numerical model offers improved accuracy for stresses and pore water pressure compared to the displacement-based PFEM formulation.Numerical experiments demonstrate that the N-PFEM efficiently captures both transient and long-term hydro-mechanical behaviour of saturated soils with high accuracy,obviating the need for stabilisation or regularisation techniques commonly employed in other nodal integration-based PFEM approaches.This work holds significant implications for the development of robust and accurate numerical tools for studying saturated soil dynamics.

Hierarchical multihead self-attention for time-series-based fault diagnosis

Fault diagnosis is important for maintaining the safety and effectiveness of chemical process.Considering the multivariate,nonlinear,and dynamic characteristic of chemical process,many time-series-based data-driven fault diagnosis methods have been developed in recent years.However,the existing methods have the problem of long-term dependency and are difficult to train due to the sequential way of training.To overcome these problems,a novel fault diagnosis method based on time-series and the hierarchical multihead self-attention(HMSAN)is proposed for chemical process.First,a sliding window strategy is adopted to construct the normalized time-series dataset.Second,the HMSAN is developed to extract the time-relevant features from the time-series process data.It improves the basic self-attention model in both width and depth.With the multihead structure,the HMSAN can pay attention to different aspects of the complicated chemical process and obtain the global dynamic features.However,the multiple heads in parallel lead to redundant information,which cannot improve the diagnosis performance.With the hierarchical structure,the redundant information is reduced and the deep local time-related features are further extracted.Besides,a novel many-to-one training strategy is introduced for HMSAN to simplify the training procedure and capture the long-term dependency.Finally,the effectiveness of the proposed method is demonstrated by two chemical cases.The experimental results show that the proposed method achieves a great performance on time-series industrial data and outperforms the state-of-the-art approaches.

Modeling urban redevelopment:A novel approach using time-series remote sensing data and machine learning

Accurate mapping and timely monitoring of urban redevelopment are pivotal for urban studies and decisionmakers to foster sustainable urban development.Traditional mapping methods heavily depend on field surveys and subjective questionnaires,yielding less objective,reliable,and timely data.Recent advancements in Geographic Information Systems(GIS)and remote-sensing technologies have improved the identification and mapping of urban redevelopment through quantitative analysis using satellite-based observations.Nonetheless,challenges persist,particularly concerning accuracy and significant temporal delays.This study introduces a novel approach to modeling urban redevelopment,leveraging machine learning algorithms and remote-sensing data.This methodology can facilitate the accurate and timely identification of urban redevelopment activities.The study’s machine learning model can analyze time-series remote-sensing data to identify spatio-temporal and spectral patterns related to urban redevelopment.The model is thoroughly evaluated,and the results indicate that it can accurately capture the time-series patterns of urban redevelopment.This research’s findings are useful for evaluating urban demographic and economic changes,informing policymaking and urban planning,and contributing to sustainable urban development.The model can also serve as a foundation for future research on early-stage urban redevelopment detection and evaluation of the causes and impacts of urban redevelopment.

Missing Value Imputation for Radar-Derived Time-Series Tracks of Aerial Targets Based on Improved Self-Attention-Based Network

The frequent missing values in radar-derived time-series tracks of aerial targets(RTT-AT)lead to significant challenges in subsequent data-driven tasks.However,the majority of imputation research focuses on random missing(RM)that differs significantly from common missing patterns of RTT-AT.The method for solving the RM may experience performance degradation or failure when applied to RTT-AT imputation.Conventional autoregressive deep learning methods are prone to error accumulation and long-term dependency loss.In this paper,a non-autoregressive imputation model that addresses the issue of missing value imputation for two common missing patterns in RTT-AT is proposed.Our model consists of two probabilistic sparse diagonal masking self-attention(PSDMSA)units and a weight fusion unit.It learns missing values by combining the representations outputted by the two units,aiming to minimize the difference between the missing values and their actual values.The PSDMSA units effectively capture temporal dependencies and attribute correlations between time steps,improving imputation quality.The weight fusion unit automatically updates the weights of the output representations from the two units to obtain a more accurate final representation.The experimental results indicate that,despite varying missing rates in the two missing patterns,our model consistently outperforms other methods in imputation performance and exhibits a low frequency of deviations in estimates for specific missing entries.Compared to the state-of-the-art autoregressive deep learning imputation model Bidirectional Recurrent Imputation for Time Series(BRITS),our proposed model reduces mean absolute error(MAE)by 31%~50%.Additionally,the model attains a training speed that is 4 to 8 times faster when compared to both BRITS and a standard Transformer model when trained on the same dataset.Finally,the findings from the ablation experiments demonstrate that the PSDMSA,the weight fusion unit,cascade network design,and imputation loss enhance imputation performance and confirm the efficacy of our design.

Finite Deformation, Finite Strain Nonlinear Dynamics and Dynamic Bifurcation in TVE Solids with Rheology

This paper presents a mathematical model consisting of conservation and balance laws (CBL) of classical continuum mechanics (CCM) and ordered rate constitutive theories in Lagrangian description derived using entropy inequality and the representation theorem for thermoviscoelastic solids (TVES) with rheology. The CBL and the constitutive theories take into account finite deformation and finite strain deformation physics and are based on contravariant deviatoric second Piola-Kirchhoff stress tensor and its work conjugate covariant Green’s strain tensor and their material derivatives of up to order m and n respectively. All published works on nonlinear dynamics of TVES with rheology are mostly based on phenomenological mathematical models. In rare instances, some aspects of CBL are used but are incorrectly altered to obtain mass, stiffness and damping matrices using space-time decoupled approaches. In the work presented in this paper, we show that this is not possible using CBL of CCM for TVES with rheology. Thus, the mathematical models used currently in the published works are not the correct description of the physics of nonlinear dynamics of TVES with rheology. The mathematical model used in the present work is strictly based on the CBL of CCM and is thermodynamically and mathematically consistent and the space-time coupled finite element methodology used in this work is unconditionally stable and provides solutions with desired accuracy and is ideally suited for nonlinear dynamics of TVES with memory. The work in this paper is the first presentation of a mathematical model strictly based on CBL of CCM and the solution of the mathematical model is obtained using unconditionally stable space-time coupled computational methodology that provides control over the errors in the evolution. Both space-time coupled and space-time decoupled finite element formulations are considered for obtaining solutions of the IVPs described by the mathematical model and are presented in the paper. Factors or the physics influencing dynamic response and dynamic bifurcation for TVES with rheology are identified and are also demonstrated through model problem studies. A simple model problem consisting of a rod (1D) of TVES material with memory fixed at one end and subjected to harmonic excitation at the other end is considered to study nonlinear dynamics of TVES with rheology, frequency response as well as dynamic bifurcation phenomenon.

Ultrafast magneto-optical dynamics in nickel(111)single crystal studied by the integration of ultrafast reflectivity and polarimetry probes

With the integration of ultrafast reflectivity and polarimetry probes,we observed carrier relaxation and spin dynamics induced by ultrafast laser excitation of Ni(111)single crystals.The carrier relaxation time within the linear excitation range reveals that electron-phonon coupling and dissipation of photon energy into the bulk of the crystal take tens of picoseconds.On the other hand,the observed spin dynamics indicate a longer time of about 120 ps.To further understand how the lattice degree of freedom is coupled with these dynamics may require the integration of an ultrafast diffraction probe.

Semiclassical approach to spin dynamics of a ferromagnetic S = 1 chain

Motivated by recent experimental progress on the quasi-one-dimensional quantum magnet Ni Nb2O6, we study the spin dynamics of an S = 1 ferromagnetic Heisenberg chain with single-ion anisotropy by using a semiclassical molecular dynamics approach. This system undergoes a quantum phase transition from a ferromagnetic to a paramagnetic state under a transverse magnetic field, and the magnetic response reflecting this transition is well described by our semiclassical method.We show that at low temperature the transverse component of the dynamical structure factor depicts clearly the magnon dispersion, and the longitudinal component exhibits two continua associated with single-and two-magnon excitations,respectively. These spin excitation spectra show interesting temperature dependence as effects of magnon interactions. Our findings shed light on the experimental detection of spin excitations in a large class of quasi-one-dimensional magnets.

Transient Response and Ionic Dynamics in Organic Electrochemical Transistors

The rapid development of organic electrochemical transistors(OECTs)has ushered in a new era in organic electronics,distinguishing itself through its application in a variety of domains,from high-speed logic circuits to sensitive biosensors,and neuromorphic devices like artificial synapses and organic electrochemical random-access memories.Despite recent strides in enhancing OECT performance,driven by the demand for superior transient response capabilities,a comprehensive understanding of the complex interplay between charge and ion transport,alongside electron–ion interactions,as well as the optimization strategies,remains elusive.This review aims to bridge this gap by providing a systematic overview on the fundamental working principles of OECT transient responses,emphasizing advancements in device physics and optimization approaches.We review the critical aspect of transient ion dynamics in both volatile and non-volatile applications,as well as the impact of materials,morphology,device structure strategies on optimizing transient responses.This paper not only offers a detailed overview of the current state of the art,but also identifies promising avenues for future research,aiming to drive future performance advancements in diversified applications.

Non-perturbative dynamics of flat-band systems with correlated disorder

We develop a numerical method for the time evolution of Gaussian wave packets on flat-band lattices in the presence of correlated disorder.To achieve this,we introduce a method to generate random on-site energies with prescribed correlations.We verify this method with a one-dimensional(1D)cross-stitch model,and find good agreement with analytical results obtained from the disorder-dressed evolution equations.This allows us to reproduce previous findings,that disorder can mobilize 1D flat-band states which would otherwise remain localized.As explained by the corresponding disorder-dressed evolution equations,such mobilization requires an asymmetric disorder-induced coupling to dispersive bands,a condition that is generically not fulfilled when the flat-band is resonant with the dispersive bands at a Dirac point-like crossing.We exemplify this with the 1D Lieb lattice.While analytical expressions are not available for the two-dimensional(2D)system due to its complexity,we extend the numerical method to the 2D a–T3 model,and find that the initial flat-band wave packet preserves its localization when a=0,regardless of disorder and intersections.However,when a̸=0,the wave packet shifts in real space.We interpret this as a Berry phase controlled,disorder-induced wave-packet mobilization.In addition,we present density functional theory calculations of candidate materials,specifically Hg1−xCdxTe.The flat-band emerges near the G point(α=0)in the Brillouin zone.

Land Use and Gully Dynamics in the Kourfa Watershed, Matankari (Southwest Niger)

The main consequences of climate change in the Sahel have been the metamorphosis of surface conditions. These metamorphoses have resulted in surface degradation, of which silting up of watersheds is the main phenomenon. The objective of this study is to assess the environmental trends of the Kourfa pond watershed. The study is based on diachronic mapping with Landsat satellite images and Google Earth images, over the period 1986 to 2021. The study reveals that vegetation (whose rate of regression doubled between 1986 and 2021) has decreased to the benefit of crop areas (whose rate of increase multiplied by 3.61 between 1986 and 2021). Bare soil and encrusted areas have also decreased, with regression rates almost double than those of 1986. In addition, the Kourfa waterholes have experienced two types of changes over 35 years: one progressive between 2011 and 2016 and the other regressive between 2001 and 2021 compared to 1986. The ravine network has been multiplied by a factor of 2.4, with density more than doubled and the connectivity of the hydrographic networks has risen from 2 to 4, with significant bank recession. This dynamic of the Kourfa pond is linked to the high drainage, the increasing complexity of the gully network and the erosion due to the retreat of the watershed banks, all of which contribute to the silting-up of the Kourfa watershed.

Ab initio nonadiabatic molecular dynamics study on spin–orbit coupling induced spin dynamics in ferromagnetic metals

Understanding the photoexcitation induced spin dynamics in ferromagnetic metals is important for the design of photo-controlled ultrafast spintronic device.In this work,by the ab initio nonadiabatic molecular dynamics simulation,we have studied the spin dynamics induced by spin–orbit coupling(SOC)in Co and Fe using both spin-diabatic and spin-adiabatic representations.In Co system,it is found that the Fermi surface(E_(F))is predominantly contributed by the spin-minority states.The SOC induced spin flip will occur for the photo-excited spin-majority electrons as they relax to the E_(F),and the spin-minority electrons tend to relax to the EFwith the same spin through the electron–phonon coupling(EPC).The reduction of spin-majority electrons and the increase of spin-minority electrons lead to demagnetization of Co within100 fs.By contrast,in Fe system,the E_(F) is dominated by the spin-majority states.In this case,the SOC induced spin flip occurs for the photo-excited spin-minority electrons,which leads to a magnetization enhancement.If we move the E_(F) of Fe to higher energy by 0.6eV,the E_(F) will be contributed by the spin-minority states and the demagnetization will be observed again.This work provides a new perspective for understanding the SOC induced spin dynamics mechanism in magnetic metal systems.

Theoretical and experimental investigation of the resonance responses and chaotic dynamics of a bistable laminated composite shell in the dynamic snap-through mode

The dynamic model of a bistable laminated composite shell simply supported by four corners is further developed to investigate the resonance responses and chaotic behaviors.The existence of the 1:1 resonance relationship between two order vibration modes of the system is verified.The resonance response of this class of bistable structures in the dynamic snap-through mode is investigated,and the four-dimensional(4D)nonlinear modulation equations are derived based on the 1:1 internal resonance relationship by means of the multiple scales method.The Hopf bifurcation and instability interval of the amplitude frequency and force amplitude curves are analyzed.The discussion focuses on investigating the effects of key parameters,e.g.,excitation amplitude,damping coefficient,and detuning parameters,on the resonance responses.The numerical simulations show that the foundation excitation and the degree of coupling between the vibration modes exert a substantial effect on the chaotic dynamics of the system.Furthermore,the significant motions under particular excitation conditions are visualized by bifurcation diagrams,time histories,phase portraits,three-dimensional(3D)phase portraits,and Poincare maps.Finally,the vibration experiment is carried out to study the amplitude frequency responses and bifurcation characteristics for the bistable laminated composite shell,yielding results that are qualitatively consistent with the theoretical results.

Mitochondrial recruitment in myelin:an anchor for myelin dynamics and plasticity?

Optimal propagation of neuronal electrical impulses depends on the insulation of axons by myelin,produced in the central nervous system by oligodendrocytes.Myelin is an extension of the oligodendrocyte plasma membrane,which wraps around an axon to form a compact multi-layered sheath.Myelin is composed of a substantially higher proportion of lipids compared to other biological membranes and enriched in a small number of specialized proteins.

Conformational dynamics as an intrinsic determinant of prion protein misfolding and neurotoxicity

The prion protein(PrP) is the key molecular and pathological mediator of prion diseases,a heterogeneous group of brain disorders with fatal outcomes.Prion diseases are rare but deserve special attention because of their unique familial,sporadic,and transmissible etiologies,all caused by a single agent:misfolded conformations of PrP.

Target layer state estimation in multi-layer complex dynamical networks considering nonlinear node dynamics

In many engineering networks, only a part of target state variables are required to be estimated.On the other hand,multi-layer complex network exists widely in practical situations.In this paper, the state estimation of target state variables in multi-layer complex dynamical networks with nonlinear node dynamics is studied.A suitable functional state observer is constructed with the limited measurement.The parameters of the designed functional observer are obtained from the algebraic method and the stability of the functional observer is proven by the Lyapunov theorem.Some necessary conditions that need to be satisfied for the design of the functional state observer are obtained.Different from previous studies, in the multi-layer complex dynamical network with nonlinear node dynamics, the proposed method can estimate the state of target variables on some layers directly instead of estimating all the individual states.Thus, it can greatly reduce the placement of observers and computational cost.Numerical simulations with the three-layer complex dynamical network composed of three-dimensional nonlinear dynamical nodes are developed to verify the effectiveness of the method.

Effect of distribution shape on the melting transition, local ordering,and dynamics in a model size-polydisperse two-dimensional fluid

We study the effect of particle size polydispersity(δ) on the melting transition(T*), local ordering, solid–liquid coexistence phase and dynamics of two-dimensional Lennard–Jones fluids up to moderate polydispersity by means of computer simulations. The particle sizes are drawn at random from the Gaussian(G) and uniform(U) distribution functions.For these systems, we further consider two different kinds of particles, viz., particles having the same mass irrespective of size, and in the other case the mass of the particle scales with its size. It is observed that with increasing polydispersity,the value of T*initially increases due to improved packing efficiency(φ) followed by a decrease and terminates at δ ≈8%(U-system) and 14%(G-system) with no significant difference for both mass types. The interesting observation is that the particular value at which φ drops suddenly coincides with the peak of the heat capacity(CP) curve, indicating a transition. The quantification of local particle ordering through the hexatic order parameter(Q_6), Voronoi construction and pair correlation function reveals that the ordering decreases with increasing δ and T. Furthermore, the solid–liquid coexistence region for the G-system is shown to be comparatively wider in the T –δ plane phase diagram than that for the U system. Finally, the study of dynamics reveals that polydisperse systems relax faster compared to monodisperse systems;however, no significant qualitative differences, depending on the distribution type and mass polydispersity, are observed.