Late spring cold reduces grain number at various spike positions by regulating spike growth and assimilate distribution in winter wheat

Late spring cold(LSC) occurred in the reproductive period of wheat impairs spike and floret differentiation during the reproductive period,when young spikelets are very cold-sensitive.However,under LSC,the responses of wheat spikelets at various positions,leaves,and stems and the interactions between them at physiological levels remain unclear.In the present study,two-year treatments at terminal spikelet stage under two temperatures(2 C,-2 C) and durations(1,2,and 3 days) were imposed in an artificial climate chamber to compare the effects of LSC on grain number and yield in the wheat cultivars Yannong 19(YN19,cold-tolerant) and Xinmai 26(XM26,cold-sensitive).The night temperature regimes were designed to reproduce natural temperature variation.LSC delayed plant growth and inhibited spike and floret differentiation,leading to high yield losses in both cultivars.LSC reduced dry matter accumulation(DMA,g) in spikes,stems,and leaves,reducing the DMA ratios of the spike to leaf and spike to stem.Plant cell wall invertase(CWINV) activity increased in upper and basal spikelets in YN19,whereas CWINV increased in middle spikelets in XM26.Under LSC,soluble sugar and glucose were transported and distributed mainly in upper and basal spikelets for glume and rachis development,so that spike development was relatively complete in YN19,whereas the upper and basal spikelets were severely damaged and most of the glumes in middle spikelets were relatively completely developed in XM26,resulting in pollen abortion mainly in upper and basal spikelets.The development of glumes and rachides was influenced and grain number per spike was decreased after LSC,with kernels present mainly in middle spikelets.Overall,reduced total DMA and dry matter partitioning to spikes under LSC results in poor spikelet development,leading to high losses of grain yield.

Nonlinear total least-squares variance component estimation for GM(1,1)model

The solution of the grey model(GM(1,1)model)generally involves equal-precision observations,and the(co)variance matrix is established from the prior information.However,the data are generally available with unequal-precision measurements in reality.To deal with the errors of all observations for GM(1,1)model with errors-in-variables(EIV)structure,we exploit the total least-squares(TLS)algorithm to estimate the parameters of GM(1,1)model in this paper.Ignoring that the effect of the improper prior stochastic model and the homologous observations may degrade the accuracy of parameter estimation,we further present a nonlinear total least-squares variance component estimation approach for GM(1,1)model,which resorts to the minimum norm quadratic unbiased estimation(MINQUE).The practical and simulative experiments indicate that the presented approach has significant merits in improving the predictive accuracy in comparison with control methods.

A complete solution of an improved universal 3D coordinate similarity transformation model

When linearizing three-dimensional(3 D)coordinate similarity transformation model with large rotations,we usually encounter the ill-posed normal matrix which may aggravate the instability of solutions.To alleviate the problem,a series of conversions are contributed to the 3 D coordinate similarity transformation model in this paper.We deduced a complete solution for the 3 D coordinate similarity transformation at any rotation with the nonlinear adjustment methodology,which involves the errors of the common and the non-common points.Furthermore,as the large condition number of the normal matrix resulted in an intractable form,we introduced the bary-centralization technique and a surrogate process for deterministic element of the normal matrix,and proved its benefit for alleviating the condition number.The experimental results show that our approach can obtain the smaller condition number to stabilize the convergence of the interested parameters.Especially,our approach can be implemented for considering the errors of the common and the non-common points,thus the accuracy of the transformed coordinates improves.

Attention-Based CNN Fusion Model for Emotion Recognition During Walking Using Discrete Wavelet Transform on EEG and Inertial Signals

Walking as a unique biometric tool conveys important information for emotion recognition.Individuals in different emotional states exhibit distinct walking patterns.For this purpose,this paper proposes a novel approach to recognizing emotion during walking using electroencephalogram(EEG)and inertial signals.Accurate recognition of emotion is achieved by training in an end-to-end deep learning fashion and taking into account multi-modal fusion.Subjects wear virtual reality head-mounted display(VR-HMD)equipment to immerse in strong emotions during walking.VR environment shows excellent imitation and experience ability,which plays an important role in awakening and changing emotions.In addition,the multi-modal signals acquired from EEG and inertial sensors are separately represented as virtual emotion images by discrete wavelet transform(DWT).These serve as input to the attention-based convolutional neural network(CNN)fusion model.The designed network structure is simple and lightweight while integrating the channel attention mechanism to extract and enhance features.To effectively improve the performance of the recognition system,the proposed decision fusion algorithm combines Critic method and majority voting strategy to determine the weight values that affect the final decision results.An investigation is made on the effect of diverse mother wavelet types and wavelet decomposition levels on model performance which indicates that the 2.2-order reverse biorthogonal(rbio2.2)wavelet with two-level decomposition has the best recognition performance.Comparative experiment results show that the proposed method outperforms other existing state-of-the-art works with an accuracy of 98.73%.

Emerging contaminants:A One Health perspective

Environmental pollution is escalating due to rapid global development that often prioritizes human needs over planetary health.Despite global efforts to mitigate legacy pollutants,the continuous introduction of new substances remains a major threat to both people and the planet.In response,global initiatives are focusing on risk assessment and regulation of emerging contaminants,as demonstrated by the ongoing efforts to establish the UN’s Intergovernmental Science-Policy Panel on Chemicals,Waste,and Pollution Prevention.This review identifies the sources and impacts of emerging contaminants on planetary health,emphasizing the importance of adopting a One Health approach.Strategies for monitoring and addressing these pollutants are discussed,underscoring the need for robust and socially equitable environmental policies at both regional and international levels.Urgent actions are needed to transition toward sustainable pollution management practices to safeguard our planet for future generations.

Preparation of Fe–Co based MOF-74 and its effective adsorption of arsenic from aqueous solution

To obtain a cost-effective adsorbent for the removal of arsenic in water,a novel nanostructured Fe–Co based metal organic framework(MOF-74)adsorbent was successfully prepared via a simple solvothermal method.The adsorption experiments showed that the optimal molar ratio of Fe/Co in the adsorbent was 2:1.The Fe_2Co_1MOF-74 was characterized by various techniques and the results showed that the nanoparticle diameter ranged from60 to 80 nm and the specific surface area was 147.82 m^2/g.The isotherm and kinetic parameters of arsenic removal on Fe_2Co_1MOF-74 were well-fitted by the Langmuir and pseudo-second-order models.The maximum adsorption capacities toward As(III)and As(V)were 266.52 and 292.29 mg/g,respectively.The presence of sulfate,carbonate and humic acid had no obvious effect on arsenic adsorption.However,coexisting phosphate significantly hindered the removal of arsenic,especially at high concentrations(10 mmol/L).Electrostatic interaction and hydroxyl and metal–oxygen groups played important roles in the adsorption of arsenic.Furthermore,the prepared adsorbent had stable adsorption ability after regeneration and when used in a real-water matrix.The excellent adsorption performance of Fe_2Co_1MOF-74 material makes it a potentially promising adsorbent for the removal of arsenic.

High Order Energy-Preserving Method of the “Good” Boussinesq Equation

The fourth order average vector field(AVF)method is applied to solve the“Good”Boussinesq equation.The semi-discrete system of the“good”Boussi-nesq equation obtained by the pseudo-spectral method in spatial variable,which is a classical finite dimensional Hamiltonian system,is discretizated by the fourth order average vector field method.Thus,a new high order energy conservation scheme of the“good”Boussinesq equation is obtained.Numerical experiments confirm that the new high order scheme can preserve the discrete energy of the“good”Boussinesq equation exactly and simulate evolution of different solitary waves well.

Multi-SymplecticMethod for the Zakharov-Kuznetsov Equation

A newscheme for the Zakharov-Kuznetsov(ZK)equationwith the accuracy order of O(△t^(2)+△x+△y^(2))is proposed.The multi-symplectic conservation property of the new scheme is proved.The backward error analysis of the newmulti-symplectic scheme is also implemented.The solitary wave evolution behaviors of the Zakharov-Kunetsov equation is investigated by the new multi-symplectic scheme.The accuracy of the scheme is analyzed.

Effects of latitude gradient and seasonal variation on the community structure and biodiversity of commercially important crustaceans in the Yellow Sea and the northern East China Sea

To evaluate the spatio-temporal variations in the community structure and biodiversity of commercially important crustaceans in the Yellow Sea and the northern East China Sea(NECS),the seasonal and regional changes in species composition,biomass structure,biodiversity and distribution of commercially important species were analyzed using bottom trawl survey data during 2014-2015.The results showed that the latitudinal gradient was obvious in species richness,dominant species and biodiversity.The indices of biodiversity increased with the decreasing latitude.When the sampling sites shifted south by one latitudinal degree,Margalefs richness index(D),Pielou's evenness index(J')and Shannon-Wiener diversity index(H')increased by 0.10.0.03 and 0.09,respectively.The biomass proportion of the cold-temperate species represented by Crangon affinis declined with the decreasing latitude,and the warm-temperate species represented by Ovalipes punctatus and Portunus trituberculatus in creased.Because of the growth regulatio n of crustaceans and the fishing moratorium,the biomass of commercially important crustaceans in the Yellow Sea and NECS was highest in October and August,respectively.Salinity had a more significant influence on H'of commercially important crustaceans than other environmental factors(including zooplankton density,sea bottom temperature and water depth).Overall,the results of this study contribute to a better understanding of community dynamics of crustaceans in the Yellow Sea and NECS,and provide evidence to verify the latitudinal gradient theory in biodiversity.