Morphological Characterization of Brazil Nut Tree (Bertholletia excelsa) Fruit Pericarp

This article presents the overall morphological structure of the Brazil nut tree(Bertholletia excelsa)fruit pericarp,from macro to nano scale.The acquired knowledge would be used for the development of new applications,like using the materials as fillers for biocomposites,or as a hierarchical architecture model for biomimetics.This research was performed using stereo and light microscopy and conventional and force field emission scanning electron microscopy.The pericarp presents three layers:the exocarp,a dark gray,brittle and fragile outer layer;the mesocarp,a beige,dry,rigid,impermeable and fibrous intermediate layer;and the endocarp,an inner layer with similar characteristic as the exocarp,but formed next to the seeds.Morphologically,the exocarp and the endocarp presented minor regions of sclereids,fibers and vascular cell bundles,inside major regions of parenchyma cells.The mesocarp presents a structure of fiber cells regions alternating with sclereids and vascular cells regions,arranged in a composite like arrangement,with the fibers cells bundles acting as randomly oriented disperse phases in a sclereid cells matrix.This arrangement was associated with the mesocarp relative superior proprieties,indicating a great material for using as fillers for biocomposites or in biomimetics applications.

A New UWB Receiver in the Indoor Multipath Environment

In the literatures about Ultra Wide Band （UWB） to date, the receiver structure is mainly based on Rake receiver. But due to the wave distortion caused by overlapped between the received and the sent pulses, a lot of energy in demodulator will be lost. In this paper, a new receiver is developed by adopting maximum likelihood algorithm, in which RAKE structure is not needed and can also be implemented easily. The simulation showed that this method has BER advantage over the traditional RAKE receiver with maximal ratio combining at high SNR, and over the autocorrelation receiver as well.

Nonlinear Coordinated Passivation Control of Single Machine Infinite Bus Power System with Static Var Compensator

We propose a nonlinear coordinated control of the generator excitation and the static var compensator(SVC) in order to enhance the transient stability and voltage regulation of power system by the passivation approach. SVC is installed in the middle of the transmission line of power system and consists of a single machine infinite bus(SMIB) system. The design of the proposed controller consists of two parts. On one hand,the generator excitation controller is designed based on a backstepping controller. On the other hand, the conception of SVC control input is based on the coordinated passivation approach,which can guarantee the asymptotic stability of the closed-loop system. The simulation results show the effectiveness of the proposed controller compared with other methods, which ensures better performance than the uncoordinated control when the system is subjected to a disturbance.

Single-model uncertainty quantification in neural network potentials does not consistently outperform model ensembles

Neural networks(NNs)often assign high confidence to their predictions,even for points far out of distribution,making uncertainty quantification(UQ)a challenge.When they are employed to model interatomic potentials in materials systems,this problem leads to unphysical structures that disrupt simulations,or to biased statistics and dynamics that do not reflect the true physics.Differentiable UQ techniques can find new informative data and drive active learning loops for robust potentials.However,a variety of UQ techniques,including newly developed ones,exist for atomistic simulations and there are no clear guidelines for which are most effective or suitable for a given case.In this work,we examine multiple UQ schemes for improving the robustness of NN interatomic potentials(NNIPs)through active learning.In particular,we compare incumbent ensemble-based methods against strategies that use single,deterministic NNs:mean-variance estimation(MVE),deep evidential regression,and Gaussian mixture models(GMM).We explore three datasets ranging from in-domain interpolative learning to more extrapolative out-of-domain generalization challenges:rMD17,ammonia inversion,and bulk silica glass.Performance is measured across multiple metrics relating model error to uncertainty.Our experiments show that none of the methods consistently outperformed each other across the various metrics.Ensembling remained better at generalization and for NNIP robustness;MVE only proved effective for in-domain interpolation,while GMM was better out-of-domain;and evidential regression,despite its promise,was not the preferable alternative in any of the cases.More broadly,cost-effective,single deterministic models cannot yet consistently match or outperform ensembling for uncertainty quantification in NNIPs.