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The dynamics of neuronal systems are characterized by hallmark features such as oscillations and synchrony. However, it has remained unclear whether these characteristics are epiphenomena or are exploited for computation. Due to the challenge of selectively interfering with oscillatory network dynamics in neuronal systems, we simulated recurrent networks of damped harmonic oscillators in which oscillatory activity is enforced in each node, a choice well supported by experimental findings. When trained on standard pattern recognition tasks, these harmonic oscillator recurrent networks (HORNs) outperformed nonoscillatory architectures with respect to learning speed, noise tolerance, and parameter efficiency. HORNs also reproduced a many characteristic features of neuronal systems, such as the cerebral cortex and the hippocampus. In trained HORNs, stimulus-induced interference patterns holistically represent the result of comparing sensory evidence with priors stored in recurrent connection weights, and learning-induced weight changes are compatible with Hebbian principles. Implementing additional features characteristic of natural networks, such as heterogeneous oscillation frequencies, inhomogeneous conduction delays, and network modularity, further enhanced HORN performance without requiring additional parameters. Taken together, our model allows us to give plausible a posteriori explanations for features of natural networks whose computational role has remained elusive. We conclude that neuronal systems are likely to exploit the unique dynamics of recurrent oscillator networks whose computational superiority critically depends on the oscillatory patterning of their nodal dynamics. Implementing the proposed computational principles in analog hardware is expected to enable the design of highly energy-efficient and self-adapting devices that could ideally complement existing digital technologies., Competing Interests: Competing interests statement:F.E. and W.S. have filed a patent application for the hardware implementation of the described network models at the European Patent Office, Application No. EP22183567.1.

We have carried out a systematic investigation on the impact of Fe3O4 nanoparticles (NPs) and Rhizobium inoculation on nodulation and growth of common bean plants (cv. Red Guama, Phaseolus vulgaris). Three distinct treatments were conducted on the common bean plants: (i) exposure to Fe3O4 NPs; (ii) Rhizobium inoculation; and (iii) a combined treatment involving Fe3O4 NPs + Rhizobium inoculation, with non-treated plants as controls. Temperature and magnetic field dependence of magnetization, M(T, H), measurements were performed on both the soil, and dried organs of the plants including roots, nodules, stems, and leaves. M(T, H) analyses indicated a systematic increase in magnetization across organs of plants treated with Fe3O4 NPs and combined Fe3O4 NPs + Rhizobium. We have found the magnetic contribution, generally related to Fe content in the soil and plant organs, significantly increased in plants exposed to Fe3O4 NPs, further indicating absorption, translocation, and accumulation of Fe3O4 NPs in the areal parts of the plants. Plants treated with Fe3O4 NPs and combined Fe3O4 NPs + Rhizobium exhibited Fe3O4 NPs accumulation in all organs with increasing concentrations of 69.7 to 74.1 NNPs/g in roots, 5.6 to 7.7 NNPs/g in stems, and 3.1 to 5.5 NNPs/g in leaves, respectively. The iron concentration in nodules was found to be close to 65 NNPs/g. No appreciable difference in the absorption index AI of roots between plants treated with Fe3O4 NPs (~ 1.73%) and Fe3O4 NPs + Rhizobium (~ 1.79%) has been observed. The translocation index TI increased by ~ 46% in plants treated with Fe3O4 NPs + Rhizobium (6.9%) compared to Fe3O4 NPs (4.3%). Treated plants showed improved symbiotic performance including nodule leghaemoglobin and iron content, number of active nodules per plant, and nodule dry weight. The best result was obtained using the combined treatment of Fe3O4 NPs + Rhizobium. This study provides evidence that M(T,H) measurements constitute a valuable tool in monitoring the uptake, translocation, and accumulation of Fe3O4 NPs in plant organs of common bean plants. [ABSTRACT FROM AUTHOR]

Residual connections have been proposed as an architecture-based inductive bias to mitigate the problem of exploding and vanishing gradients and increased task performance in both feed-forward and recurrent networks (RNNs) when trained with the backpropagation algorithm. Yet, little is known about how residual connections in RNNs influence their dynamics and fading memory properties. Here, we introduce weakly coupled residual recurrent networks (WCRNNs) in which residual connections result in well-defined Lyapunov exponents and allow for studying properties of fading memory. We investigate how the residual connections of WCRNNs influence their performance, network dynamics, and memory properties on a set of benchmark tasks. We show that several distinct forms of residual connections yield effective inductive biases that result in increased network expressivity. In particular, those are residual connections that (i) result in network dynamics at the proximity of the edge of chaos, (ii) allow networks to capitalize on characteristic spectral properties of the data, and (iii) result in heterogeneous memory properties. In addition, we demonstrate how our results can be extended to non-linear residuals and introduce a weakly coupled residual initialization scheme that can be used for Elman RNNs., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Convolutional neural networks (CNNs) are one of the most successful computer vision systems to solve object recognition. Furthermore, CNNs have major applications in understanding the nature of visual representations in the human brain. Yet it remains poorly understood how CNNs actually make their decisions, what the nature of their internal representations is, and how their recognition strategies differ from humans. Specifically, there is a major debate about the question of whether CNNs primarily rely on surface regularities of objects, or whether they are capable of exploiting the spatial arrangement of features, similar to humans. Here, we develop a novel feature-scrambling approach to explicitly test whether CNNs use the spatial arrangement of features (i.e. object parts) to classify objects. We combine this approach with a systematic manipulation of effective receptive field sizes of CNNs as well as minimal recognizable configurations (MIRCs) analysis. In contrast to much previous literature, we provide evidence that CNNs are in fact capable of using relatively long-range spatial relationships for object classification. Moreover, the extent to which CNNs use spatial relationships depends heavily on the dataset, e.g. texture vs. sketch. In fact, CNNs even use different strategies for different classes within heterogeneous datasets (ImageNet), suggesting CNNs have a continuous spectrum of classification strategies. Finally, we show that CNNs learn the spatial arrangement of features only up to an intermediate level of granularity, which suggests that intermediate rather than global shape features provide the optimal trade-off between sensitivity and specificity in object classification. These results provide novel insights into the nature of CNN representations and the extent to which they rely on the spatial arrangement of features for object classification., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Combining in situ measurements of energetic electrons and remote sensing observations of hard X‐rays and type III radio bursts, we examine the release times of energetic electrons in the July 23, 2016 event. We find that the releases of in situ energetic electrons from the Sun are delayed from those electrons that are responsible for the hard X‐rays. We further find that the release time of in situ electrons is a function of electron energy. Under the assumption that the acceleration mechanism for the upward propagating electrons is of Fermi‐type and is controlled by an energy‐dependent diffusion coefficient, we fit these release times by a simple functional form, related to the turbulence spectral index. Implications of our study on the underlying electron acceleration mechanisms and the magnetic reconnection process in solar flares are discussed. Our results demonstrate the power of the recently developed fractional velocity dispersion analysis (FVDA) method in solar flare studies. Plain Language Summary: Solar flares are efficient particle accelerators. Electrons and ions are accelerated to very high energies at solar flares. Magnetic reconnection is believed to be the main energy convertor at solar flares. Observations and simulations in the past decade have shown that when magnetic reconnection occurs, electrons can be accelerated at both the reconnection site and the reconnection exhausts, which are plasma shooting away from the reconnection site. Energetic electrons precipitating down on the solar surface will cause hard X‐ray and gamma ray. Energetic electrons escape outward can be observed in situ. Are these two populations of electron released at the same reconnection site, or they have different acceleration history, perhaps at the two oppositely propagating exhausts? In this study, we examine this question using timing studies of in‐situ electrons and hard X‐ray observations of the solar flare from Fermi observation. We show that outward propagating electrons are undergoing a longer acceleration process than those downward propagating electrons, suggesting an acceleration process that is volume‐filling and is consistent with a second‐order Fermi acceleration at the reconnection exhaust propagating upward. Key Points: Release of in situ electrons at the Sun is delayed from the release of hard X‐ray generating electrons in impulsive SEP eventsThe release delay of in‐situ electrons at the Sun shows a clear energy dependence which can be fitted by a power law of electron momentumThe power law index from the above fitting is related to the turbulence dissipation range spectral index at the flare site [ABSTRACT FROM AUTHOR]

Machine learning tools, particularly artificial neural networks (ANN), have become ubiquitous in many scientific disciplines, and machine learning-based techniques flourish not only because of the expanding computational power and the increasing availability of labeled data sets but also because of the increasingly powerful training algorithms and refined topologies of ANN. Some refined topologies were initially motivated by neuronal network architectures found in the brain, such as convolutional ANN. Later topologies of neuronal networks departed from the biological substrate and began to be developed independently as the biological processing units are not well understood or are not transferable to in silico architectures. In the field of neuroscience, the advent of multichannel recordings has enabled recording the activity of many neurons simultaneously and characterizing complex network activity in biological neural networks (BNN). The unique opportunity to compare large neuronal network topologies, processing, and learning strategies with those that have been developed in state-of-the-art ANN has become a reality. The aim of this review is to introduce certain basic concepts of modern ANN, corresponding training algorithms, and biological counterparts. The selection of these modern ANN is prone to be biased (e.g., spiking neural networks are excluded) but may be sufficient for a concise overview., (© 2023 Massachusetts Institute of Technology.)

Self-assembled monolayer (SAM) layers on hydroxylated Si surfaces with molecules having functional aromatic end groups at the end of the alkyl chain are studied. Results suggest the possibility of nanomolecular transistor with the functional end group as an active layer without any additional deposition of an organic conducting layer on the SAM dielectric layer.

The Earth‐Sun Lagrangian point 4 is a meta‐stable location at 1 AU from the Sun, 60° ahead of Earth's orbit. It has an uninterrupted view of the solar photosphere centered on W60, the Earth's nominal magnetic field connection to the Sun. Such a mission on its own would serve as a solar remote sensing observatory that would oversee the entire solar radiation hemisphere with significant relevance for protecting Moon and Mars explorers from radiation exposure. In combination with appropriately planned observatories at L1 and L5, the three spacecraft would provide 300° longitude coverage of photospheric magnetic field structure, and allow continuous viewing of both solar poles, with >3.6° elevation. Ideally, the L4 and L5 missions would orbit the Sun with a 7.2° inclination out of the heliographic equator, 14.5° out of the ecliptic plane. We discuss the impact of extending solar magnetic field observations in both longitude and latitude to improve global solar wind modeling and, with the development of local helioseismology, the potential for long‐term solar activity forecasting. Such a mission would provide a unique opportunity for interplanetary and interstellar dust science. It would significantly add to reliability of operational observations on fast coronal mass ejections directed at Earth and for human Mars explorers on their round‐trip journey. The L4 mission concept is technically feasible, and is scientifically compelling. Plain Language Summary: This work describes the advantages of placing a spacecraft at a point 60° ahead of Earth in its orbit, which offers advantages for viewing areas on the surface of the Sun that hold critical clues about solar ionizing radiation that may affect astronauts on/at the moon or on the way to and from Mars. We describe that a combination of missions ahead and behind the Earth in its orbit would provide additional benefits, even more so if injected in an orbital plane that is tilted by ∼14°. Solar remote sensing observations from these platforms would significantly improve models of the magnetic field and solar wind of the Sun and in the inner solar system, by covering more solar surface area in longitude, but also of the polar regions. This would help us better understand when solar eruptions affect the Earth's magnetosphere. Other benefits, including long‐term advance forecasting of solar activity and the understanding of dust populations in the near‐Sun environment are also discussed. An analysis of launch capabilities shows that such a mission concept, even launching both suggested missions together, is feasible. Key Points: First thorough analysis of advantages of Earth‐Sun L4 location as observation platform, and combination of L4/L5 at moderate inclinationThe L4 view of solar radiation hemisphere is ideal for forecasting/warning Earth‐Moon system and Mars journey of solar energetic particlesCombination of L4/L5 observations would boost inner‐heliosphere solar wind modeling and validation, and long‐term solar activity forecasting [ABSTRACT FROM AUTHOR]

The radiation belts of the Earth, filled with energetic electrons, comprise complex and dynamic systems that pose a significant threat to satellite operation. While various models of electron flux both for low and relativistic energies have been developed, the behavior of medium energy (120–600 keV) electrons, especially in the MEO region, remains poorly quantified. At these energies, electrons are driven by both convective and diffusive transport, and their prediction usually requires sophisticated 4D modeling codes. In this paper, we present an alternative approach using the Light Gradient Boosting (LightGBM) machine learning algorithm. The Medium Energy electRon fLux In Earth's outer radiatioN belt (MERLIN) model takes as input the satellite position, a combination of geomagnetic indices and solar wind parameters including the time history of velocity, and does not use persistence. MERLIN is trained on >15 years of the GPS electron flux data and tested on more than 1.5 years of measurements. Tenfold cross validation yields that the model predicts the MEO radiation environment well, both in terms of dynamics and amplitudes o f flux. Evaluation on the test set shows high correlation between the predicted and observed electron flux (0.8) and low values of absolute error. The MERLIN model can have wide space weather applications, providing information for the scientific community in the form of radiation belts reconstructions, as well as industry for satellite mission design, nowcast of the MEO environment, and surface charging analysis. Plain Language Summary: The radiation belts of the Earth, which are the zones of charged energetic particles trapped by the geomagnetic field, comprise complex and dynamic systems posing a significant threat to a variety of commercial and military satellites. While the inner belt is relatively stable, the outer belt is highly variable and depends substantially on solar activity; therefore, accurate and improved models of electron flux in the outer radiation belt are essential to understand the underlying physical processes. Although many models have been developed for the geostationary orbit and relativistic energies, prediction of electron flux in the 120–600 keV energy range still remains challenging. We present a data‐driven model of the medium energies (120–600 keV) differentialelectron flux in the outer radiation belt based on machine learning. We use 17 years of electron observations by Global Positioning System (GPS) satellites. We set up a 3D model for flux prediction in terms of L‐values, MLT, and magnetic latitude. The model gives reliable predictions of the radiation environment in the outer radiation belt and has wide space weather applications. Key Points: A machine learning model is created to predict electron flux at MEO for energies 120–600 keVThe model requires solar wind parameters and geomagnetic indices as input and does not use persistenceMERLIN model yields high accuracy and high correlation with observations (0.8) [ABSTRACT FROM AUTHOR]

Magnetic nanoparticles (NP) of magnetite (Fe3O4) coated with oleic acid (OA) and dodecanoic acid (DA) were synthesized and investigated through transmission electron microscopy (TEM), magnetization M, and ac magnetic susceptibility measurements. The OA coated samples were produced with different magnetic concentrations (78%, 76%, and 65%) and the DA sample with 63% of Fe3O4. Images from TEM indicate that the NP have a nearly spherical geometry and mean diameter ∼5.5 nm. Magnetization measurements, performed in zero-field cooled (ZFC) and field cooled processes under different external magnetic fields H, exhibited a maximum at a given temperature TB in the ZFC curves, which depends on the NP coating (OA or DA), magnetite concentration, and H. The temperature TB decreases monotonically with increasing H and, for a given H, the increase in the magnetite concentration results in an increase in TB. The observed behavior is related to the dipolar interaction between NP, which seems to be an important mechanism in all samples studied. This is supported by the results of the ac magnetic susceptibility χac measurements, where the temperature in which χ′ peaks for different frequencies follows the Vogel–Fulcher model, a feature commonly found in systems with dipolar interactions. Curves of H versus TB/TB(H=0) for samples with different coatings and magnetite concentrations collapse into a universal curve, indicating that the qualitative magnetic behavior of the samples may be described by the NP themselves, instead of the coating or the strength of the dipolar interaction. Below TB, M versus H curves show a coercive field (HC) that increases monotonically with decreasing temperature. The saturation magnetization (MS) follows the Bloch’s law and values of MS at room temperature as high as 78 emu/g were estimated, a result corresponding to ∼80% of the bulk value. The overlap of M/MS versus H/T curves for a given sample and the low HC at high temperatures suggest superparamagnetic behavior in all samples studied. The overlap of M/MS versus H curves at constant temperature for different samples indicates that the NP magnetization behavior is preserved, independently of the coating and magnetite concentration. [ABSTRACT FROM AUTHOR]

During geomagnetic storms, the rapid depletion of the high‐energy (several MeV) outer radiation belt electrons is the result of loss to the interplanetary medium through the magnetopause, outward radial diffusion, and loss to the atmosphere due to wave‐particle interactions. We have performed a statistical study of 110 storms using pitch angle resolved electron flux measurements from the Van Allen Probes mission and found that inside of the radiation belt (L* = 3 − 5) the number of storms that result in depletion of electrons with equatorial pitch angle αeq = 30∘ is higher than number of storms that result in depletion of electrons with equatorial pitch angle αeq = 75∘. We conclude that this result is consistent with electron scattering by whistler and electromagnetic ion cyclotron waves. At the outer edge of the radiation belt (L* ≥ 5.2) the number of storms that result in depletion is also large (~40–50%), emphasizing the significance of the magnetopause shadowing effect and outward radial transport. Key Points: Up to 49% of the studied storms result in a depletion of multi‐MeV electrons, with most depletions at L* < 5.2 are consistent with EMIC wavesThe percentage of storms that result in multi‐MeV electron depletions is dependent upon pitch angleThe number of storm depletions at small pitch angles is higher (increase up to 19%) than the number of depletions at large pitch angles Plain Language Summary: Protons and electrons form a radiation environment around Earth that can change drastically during so called geomagnetic storms. In this study, we looked at 110 storms to understand how high‐energy electrons can disappear due to different phenomena. We found that it is very common to observe a loss of high‐energy electrons after storms. More often such a loss happens far away from the Earth as the electrons cross the boundary of the magnetosphere. However, closer to Earth the electrons are lost most likely due to the interaction with whistler and electromagnetic ion cyclotron waves, which play an important role in the dynamics of the radiation environment. [ABSTRACT FROM AUTHOR]

Investigates novel model systems for photo-induced intramolecular electron-transfer processes in anthracene- or quinquethiophene-fullerene diads and triads. Occurrence of charge transfer after excitation; Detection of a free induction decay and a broad spin-echo spectrum in both diad and triads at low temperatures.

The role of agglomeration and magnetic interparticle interactions in heat generation of magnetic ferrofluids in an ac magnetic field is still unclear, with apparent discrepancy between the results presented in the literature. In this work, we measured the heat generating capability of agglomerated ferrite nanoparticles in a non-invasive ac magnetic field with f = 100 kHz and H0 = 13 kAm-1. The nanoparticles were morphologically and magnetically characterized, and the specific absorption rate (SAR) for our ac magnetic field presents a clear dependence on the diameter of the nanoparticles, with a maximum SAR = 48Wg-1 for 15 nm. Our agglomerated nanoparticles have large hydrodynamic diameters, thus the mechanical relaxation can be neglected as a heat generation mechanism. Therefore, we present a model that simulates the SAR dependence of the agglomerated samples on the diameter of the nanoparticles based on the hysteresis losses that is valid for the non-linear region (with H0 comparable to the anisotropy field). Our model takes into account the magnetic interactions among the nanoparticles in the agglomerate. For comparison, we also measured the SAR of non-agglomerated nanoparticles in a similar diameter range, in which Néel and Brown relaxations dominate the heat generation. [ABSTRACT FROM AUTHOR]

Galactic transport models for cosmic rays involve the diffusive motion of these particles in the interstellar medium. Owing to the large-scale structured Galactic magnetic field, this diffusion is anisotropic with respect to the local field direction. We included this transport effect along with continuous loss processes in a quantitative model of Galactic propagation for cosmic ray protons that is based on stochastic differential equations. We calculated energy spectra at different positions along the Sun’s Galactic orbit and compared them to the isotropic diffusion case. The results show that a larger amplitude of variation and different spectral shapes are obtained in the introduced anisotropic diffusion scenario, which in turn emphasizes the need for accurate Galactic magnetic field models. [ABSTRACT FROM AUTHOR]

The spatial diffusion of cosmic rays in turbulent magnetic fields can, in the most general case, be fully anisotropic, i.e., one has to distinguish three diffusion axes in a local, field-aligned frame. We reexamine the transformation for the diffusion tensor from this local to a global frame, in which the Parker transport equation for energetic particles is usually formulated and solved. Particularly, we generalize the transformation formulae to allow for an explicit choice of two principal local perpendicular diffusion axes. This generalization includes the "traditional" diffusion tensor in the special case of isotropic perpendicular diffusion. For the local frame, we describe the motivation for the choice of the Frenet--Serret trihedron, which is related to the intrinsic magnetic field geometry. We directly compare the old and the new tensor elements for two heliospheric magnetic field configurations, namely the hybrid Fisk and Parker fields. Subsequently, we examine the significance of the different formulations for the diffusion tensor in a standard three-dimensional model for the modulation of galactic protons. For this, we utilize a numerical code to evaluate a system of stochastic differential equations equivalent to the Parker transport equation and present the resulting modulated spectra. The computed differential fluxes based on the new tensor formulation deviate from those obtained with the "traditional" one (only valid for isotropic perpendicular diffusion) by up to 60% for energies below a few hundred MeV depending on heliocentric distance. [ABSTRACT FROM AUTHOR]

Neurons collect their inputs from other neurons by sending out arborized dendritic structures. However, the relationship between the shape of dendrites and the precise organization of synaptic inputs in the neural tissue remains unclear. Inputs could be distributed in tight clusters, entirely randomly or else in a regular grid-like manner. Here, we analyze dendritic branching structures using a regularity index R, based on average nearest neighbor distances between branch and termination points, characterizing their spatial distribution. We find that the distributions of these points depend strongly on cell types, indicating possible fundamental differences in synaptic input organization. Moreover, R is independent of cell size and we find that it is only weakly correlated with other branching statistics, suggesting that it might reflect features of dendritic morphology that are not captured by commonly studied branching statistics. We then use morphological models based on optimal wiring principles to study the relation between input distributions and dendritic branching structures. Using our models, we find that branch point distributions correlate more closely with the input distributions while termination points in dendrites are generally spread out more randomly with a close to uniform distribution. We validate these model predictions with connectome data. Finally, we find that in spatial input distributions with increasing regularity, characteristic scaling relationships between branching features are altered significantly. In summary, we conclude that local statistics of input distributions and dendrite morphology depend on each other leading to potentially cell type specific branching features., Competing Interests: The authors have declared that no competing interests exist.

Polypropylene and nanocomposite polypropylene fibres were successfully prepared via a melt spinning process. The prepared fibres were characterized by TGA and SEM. The results obtained show that the addition of MMT clay in the fibre induces a higher thermal stability. Water absorption and mechanical properties of the fibres were improved by the introduction of MMT clay nanoparticles in the fibre. Dyeing studies underlined the effect of the clay on the dyeability of the fibres which improved the accessibility of the fibre for both acid and disperse dyes. Moreover, it was observed, that polypropylene and nanocomposites polypropylene fibres were satisfactorily dyed with disperse dyes more than with acid dyes. [ABSTRACT FROM AUTHOR]

Context. Current solar energetic particle (SEP) propagation models describe the effects of interplanetary plasma turbulence on SEPs as diffusion, using a Fokker-Planck (FP) equation. However, FP models cannot explain the observed fast access of SEPs across the average magnetic field to regions that are widely separated in longitude within the heliosphere without using unrealistically strong cross-field diffusion. Aims. We study whether the recently suggested early non-diffusive phase of SEP propagation can explain the wide SEP events with realistic particle transport parameters. Methods. We used a novel model that accounts for the SEP propagation along field lines that meander as a result of plasma turbulence. Such a non-diffusive propagation mode has been shown to dominate the SEP cross-field propagation early in the SEP event history. We compare the new model to the traditional approach, and to SEP observations. Results. Using the new model, we reproduce the observed longitudinal extent of SEP peak fluxes that are characterised by a Gaussian profile with σ = 30−50◦ , while current diffusion theory can only explain extents of 11◦ with realistic diffusion coefficients. Our model also reproduces the timing of SEP arrival at distant longitudes, which cannot be explained using the diffusion model. Conclusions. The early onset of SEPs over a wide range of longitudes can be understood as a result of the effects of magnetic field-line random walk in the interplanetary medium and requires an SEP transport model that properly describes the non-diffusive early phase of SEP cross-field propagation. [ABSTRACT FROM AUTHOR]

Dendrites form predominantly binary trees that are exquisitely embedded in the networks of the brain. While neuronal computation is known to depend on the morphology of dendrites, their underlying topological blueprint remains unknown. Here, we used a centripetal branch ordering scheme originally developed to describe river networks-the Horton-Strahler order (SO)-to examine hierarchical relationships of branching statistics in reconstructed and model dendritic trees. We report on a number of universal topological relationships with SO that are true for all binary trees and distinguish those from SO-sorted metric measures that appear to be cell type-specific. The latter are therefore potential new candidates for categorising dendritic tree structures. Interestingly, we find a faithful correlation of branch diameters with centripetal branch orders, indicating a possible functional importance of SO for dendritic morphology and growth. Also, simulated local voltage responses to synaptic inputs are strongly correlated with SO. In summary, our study identifies important SO-dependent measures in dendritic morphology that are relevant for neural function while at the same time it describes other relationships that are universal for all dendrites.

Context. Spacecraft observations have motivated the need for a refined description of the phase-space distribution function. Of particular importance is the pitch-angle diffusion coefficient that occurs in the Fokker-Planck transport equation. Aims. Simulations and analytical test-particle theories are compared to verify the diffusion description of particle transport, which does not allow for non-Markovian behavior. Methods. A Monte-Carlo simulation code was used to trace the trajectories of test particles moving in turbulent magnetic fields. From the ensemble average, the pitch-angle Fokker-Planck coefficient is obtained via the mean square displacement. Results. It is shown that, while excellent agreement with analytical theories can be obtained for slab turbulence, considerable deviations are found for isotropic turbulence. In addition, all Fokker-Planck coefficients tend to zero for high time values. [ABSTRACT FROM AUTHOR]

Because new technology using ionic liquids (ILs) for cellulose processing enables the spinning of cellulose with various techniques, the resulting fiber property profiles differ significantly, depending on the process parameters. ILs are thermally stable, nontoxic, environmentally friendly solvents and dissolve cellulose directly; this leads to a high flexibility in the complete process chain for man-made cellulosics. A comparison of the manufacture of cellulosic fibers according to the Lyocell process and by means of ILs is presented. The rheological behavior of the spinning dopes, the structures, and the physical textile properties of the prepared fibers were determined. The fibers spun from solutions of cellulose in N-methyl morpholin- N-oxide monohydrate, 1-ethyl-3-methyl imidazolium acetate, and 1-ethyl-3-methyl imidazolium diethyl phosphate were compared. © 2012 Wiley Periodicals, Inc. J. Appl. Polym. Sci., 2013 [ABSTRACT FROM AUTHOR]

Nitrilases are the enzymes that catalyze the hydrolysis of nitriles to corresponding carboxylic acid and ammonia. They are broadly categorized into aromatic, aliphatic, and arylacetonitrilases based on their substrate specificity. Most of the studies pertaining to these enzymes in the literature have focused on aromatic and aliphatic nitrilases. However, arylacetonitrilases have attracted the attention of academia and industry in the last several years due to their aryl specificity and enantioselectivity. They have emerged as interesting biocatalytic tools in green chemistry to synthesize useful aryl acids such as mandelic acid and derivatives of phenylacetic acid. The aim of the present review is to collate information on the arylacetonitrilases and their catalytic properties including enantioselectivity and potential applications in organic synthesis., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

The article presents a study that performed the grafting emulsion polymerization of vinyl monomers onto cotton in the presence of double-modified montmorillonite clay. The study showed a higher rate of grafting can be achieved by grafting with glycidyl methacrylate/montmorillonite. It also revealed higher value of the grafting yield of glycidyl methacrylate monomer onto cotton in the presence of montmorillonite clay.

The article discusses a study which investigated the structure and properties of nano-composites prepared by in situ emulsion polymerization of polyglycidylmethacrylate (PGMA) and polymethylmethacrylate (PMMA) with double modified clay. The authors used the Radox Initiation System in their investigation. It was concluded that all weight loses temperatures for the nanocomposite samples are higher than that of pure polymer in both PGMA and PMMA.

In this paper we report on the vibrational Fourier transform infrared (FT-IR) and Fourier transform Raman (FT-Raman) spectra of a few donor-acceptor-substituted bi- ter-, and quaterthiophenes, as solids. The relevant vibrational information has been interpreted with the help of spectroscopic data previously collected on a model series of α,α′-dimethyl end-capped oligothiophenes, in their neutral, radical cationic, and dicationic forms. We have also performed density functional theory (DFT) quantum-mechanical calculations to analyze the effects of the intramolecular charge transfer on the geometries and vibrational spectra of these systems. © 2000 American Institute of Physics. [ABSTRACT FROM AUTHOR]

We propose and experimentally demonstrate a method for sampling frequency offset (SFO) estimation in optical communication systems based on periodically inserted identical binary prefix. Different from conventional cyclic prefix, binary prefix provides not only high tolerance to chromatic dispersion in dispersive fiber transmission, but also the ability to estimate SFO by simple receiver-side digital signal processing. Moreover, this binary prefix based scheme is generally applicable to any advanced modulation formats. A proof-of-concept experiment is conducted to quantify the accuracy and tolerance of the scheme in estimating SFO. It is found that over a wide SFO range up to 341 ppm, the estimation error is kept under 20 ppb and signals are recovered with the same quality as with zero-offset sampling. The experimental results also confirm that this method is tolerant to link signal-to-noise ratio loss and dispersion, showing no additional penalty after transmission over a 40-km standard single-mode fiber at 1550 nm.

Structural inhomogeneities in synaptic efficacies have a strong impact on population response dynamics of cortical networks and are believed to play an important role in their functioning. However, little is known about how such inhomogeneities could evolve by means of synaptic plasticity. Here we present an adaptive model of a balanced neuronal network that combines two different types of plasticity, STDP and synaptic scaling. The plasticity rules yield both long-tailed distributions of synaptic weights and firing rates. Simultaneously, a highly connected subnetwork of driver neurons with strong synapses emerges. Coincident spiking activity of several driver cells can evoke population bursts and driver cells have similar dynamical properties as leader neurons found experimentally. Our model allows us to observe the delicate interplay between structural and dynamical properties of the emergent inhomogeneities. It is simple, robust to parameter changes and able to explain a multitude of different experimental findings in one basic network.

We propose a power-efficient method for transmitting multiple frequency-division multiplexed (FDM) orthogonal frequency-division multiplexing (OFDM) signals in intensity-modulation direct-detection (IM-DD) optical systems. This method is based on quadratic soft clipping in combination with odd-only channel mapping. We show, both analytically and experimentally, that the proposed approach is capable of improving the power efficiency by about 3 dB as compared to conventional FDM OFDM signals under practical bias conditions, making it a viable solution in applications such as optical fiber-wireless integrated systems where both IM-DD optical transmission and OFDM signaling are important.

We experimentally demonstrate an end-to-end ultra-broadband cloud-DSL network using passive optical network (PON) based fronthaul with electronic code-division-multiple-access (eCDMA) encoding and decoding. Forty-eight signals that are compliant with the very-high-bit-rate digital subscriber line 2 (VDSL2) standard are transmitted with a record throughput of 5.76 Gb/s over a hybrid link consisting of a 20-km standard single-mode fiber and a 100-m twisted pair.

We demonstrate a flexible modulation and detection scheme for upstream transmission in passive optical networks using pulse position modulation at optical network unit, facilitating burst-mode detection with automatic decision threshold tracking, and DSP-enabled soft-combining at optical line terminal. Adaptive receiver sensitivities of -33.1 dBm, -36.6 dBm and -38.3 dBm at a bit error ratio of 10(-4) are respectively achieved for 2.5 Gb/s, 1.25 Gb/s and 625 Mb/s after transmission over a 20-km standard single-mode fiber without any optical amplification.

Transport of energetic electrons in the flare loop is important to understanding nonthermal emissions in solar flares. In this work, we model the propagation of electrons by numerically solving the particle transport equation which includes the physics of magnetic mirroring and turbulent pitch-angle diffusion. We find that both the fractions of electrons trapped in the looptop and precipitating into the solar surface display a non-monotonic behavior with increasing scattering rate. In the moderate diffusion regime, the precipitation fraction is highest and we expect intense nonthermal HXR and microwave emissions at the footpoints. With no or weak pitch-angle scattering, the velocity space distribution can be highly anisotropic both in the looptop and loopleg regions. Different patterns of stripes with positive gradients in the perpendicular direction can drive the electron cyclotron maser instability with higher efficiency than the classical loss-cone distribution, facilitating the excitation of coherent solar radio bursts. Our simulation results highlight the effects of turbulent pitch-angle scattering on electron trap/precipitation and anisotropic distribution in solar flares, which may help us understand the precipitation of magnetospheric electrons accounting for the aurora as well. [ABSTRACT FROM AUTHOR]