Optimising the use of gene expression data to predict plant metabolic pathway memberships
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Plant Sciences↗
SEARCH · Engineering Papers
Results for “spatiotemporal correlations”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Revealing meso-structure dynamics in additive manufacturing of energy storage via operando coherent X-ray scattering
3D printing is an emerging technology for the fabrication of energy storage devices, offering advantages over traditional manufacturing methods. However, optimization and design of such devices requires an understanding of the meso-structure formation during the 3D printing process. This study utilizes operando coherent X-ray scattering, X-ray Photon Correlation Spectroscopy (XPCS), to study the spatiotemporally-resolved far-from-equilibrium dynamics during direct ink writing 3D printing. Here, Lithium Titanate (LTO) based ink is prepared and rheologically tested for its shear-thinning properties. Two-time intensity-intensity functions are calculated to be used in subsequent quantitative analysis, which allows for an overall characterization of the dynamics, description of an initial fast decorrelation and identification of sudden rearrangements of subdomains of the sample. The results show the dynamics to be anisotropic, spatiotemporally heterogenous and marked by distinct rearrangement events, all of which impact the electrochemical performance of energy storage devices. The studied 3D printing ink is used to fabricate electrodes which are then electrochemically tested, showing good performance in cycling and retaining structural integrity. This work furthers the understanding of the far-from-equilibrium material dynamics during 3D printing, giving quantitative characterization of this process, and highlights aspects of structure formation relevant to the electrochemical performance of the resultant energy storage device.
36 MATERIALS SCIENCE↗
Grain Growth and Coarsening Dynamics in a Compositionally Asymmetric Block Copolymer Revealed by X-ray Photon Correlation Spectroscopy
The dynamics of nanostructured soft materials crucially impact their associated macroscopic material properties, yet they are often difficult to measure due to spatiotemporal limitations of conventional instrumentation. Herein, we use X-ray photon correlation spectroscopy (XPCS) to directly observe particle-scale dynamics during grain growth and coarsening in a BCC- forming diblock polymer melt, with specific attention to the distribution of structural relaxation times associated with the interplanar (110) distance. Following sample quenching from the disordered state, these dynamical phenomena surprisingly exhibit little dependence on time and thermal quench depth. We posit that these relaxations stem from collective particle motions during grain rotation. We also observe unusual internally-referenced heterodyne correlations, which enable measurements of speed distributions within the sample. In this work, these speeds are significantly slower and appear at much longer annealing times than those previously reported during grain nucleation and growth in microphase separated block polymer melts. Drawing on analogies between polycrystalline hard and soft materials, we ascribe these speed distributions to misorientation-dependent grain boundary migration during ordered domain coarsening and anomalously fast, cooperative string-like particle motion along the grain boundaries. Thus, these coherent X-ray measurements provide new opportunities to interrogate grain boundary structure and dynamics in polycrystalline soft materials.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Mesoscopic two-point collective dynamics of glass-forming liquids
The collective density–density and hydrostatic pressure–pressure correlations of glass-forming liquids are spatiotemporally mapped out using molecular dynamics simulations. It is shown that the sharp rise of structural relaxation time below the Arrhenius temperature coincides with the emergence of slow, nonhydrodynamic collective dynamics on mesoscopic scales. The observed long-range, nonhydrodynamic mode is independent of wave numbers and closely coupled to the local structural dynamics. Below the Arrhenius temperature, it dominates the slow collective dynamics on length scales immediately beyond the first structural peak in contrast to the well-known behavior at high temperatures. Furthermore, these results highlight a key connection between the qualitative change in mesoscopic two-point collective dynamics and the dynamic crossover phenomenon.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Spatiotemporal spread of perturbations in power-law models at low temperatures: Exact results for classical out-of-time-order correlators
Here, we present exact results for the classical version of the out-of-time-order commutator (OTOC) for a family of power-law models consisting of N particles in one dimension and confined by an external harmonic potential. These particles are interacting via power-law interaction of the form ∝ $Σ_{i,j=(i≠j)}^{N}$ |x i -x j | -k for every k>1 where x i is the position of the i th particle. We present numerical results for the OTOC for finite N at low temperatures and short enough times so that the system is well approximated by the linearized dynamics around the many-body ground state. In the large-N limit, we compute the ground-state dispersion relation in the absence of external harmonic potential exactly and use it to arrive at analytical results for OTOC. We find excellent agreement between our analytical results and the numerics. We further obtain analytical results in the limit where only linear and leading nonlinear (in momentum) terms in the dispersion relation are included. The resulting OTOC is in agreement with numerics in the vicinity of the edge of the “light cone.” We find remarkably distinct features in OTOC below and above k=3 in terms of going from non-Airy behavior (1 < k < 3) to an Airy universality class (k>3). We present certain additional rich features for the case k=2 that stem from the underlying integrability of the Calogero-Moser model. We present a field theory approach that also assists in understanding certain aspects of OTOC such as the sound speed. Our findings are a step forward towards a more general understanding of the spatiotemporal spread of perturbations in long-range interacting systems.
1-dimensional systems↗
Spatiotemporal characterization of the field-induced insulator-to-metal transition
Watching a metal filament grow Resistive switching is a process in which the electrical resistance of a sample changes abruptly in response to a voltage pulse, often by orders of magnitude. This process is at the heart of many neuromorphic computing approaches but visualizing it in both space and time is tricky. del Valle et al . monitored the resistive switching in three different vanadium oxide compounds by measuring time- and space-resolved optical reflectivity (see the Perspective by Hilgenkamp and Gao). A characteristic conducting filament was quickly nucleated on the inhomogeneities in the sample and then propagated due to Joule heating. —JS
Science & Technology - Other Topics↗
Spatiotemporal quenches for efficient critical ground state preparation in the two-dimensional transverse field Ising model
Quantum simulators have the potential to shed light on the study of quantum many-body systems and materials, offering unique insights into various quantum phenomena. Although adiabatic evolution has been conventionally employed for state preparation, it faces challenges when the system evolves too quickly or the coherence time is limited. In such cases, shortcuts to adiabaticity, such as spatiotemporal quenches, provide a promising alternative. This paper numerically investigates the application of spatiotemporal quenches in the two-dimensional transverse field Ising model with ferromagnetic interactions, focusing on the emergence of the ground state and its correlation properties at criticality when the gap vanishes. We demonstrate the effectiveness of these quenches in rapidly preparing ground states in critical systems. Our simulations reveal the existence of an optimal quench front velocity at the emergent speed of light, leading to minimal excitation energy density and correlation lengths of the order of finite system sizes we can simulate. These findings emphasize the potential of spatiotemporal quenches for efficient ground state preparation in quantum systems, with implications for the exploration of strongly correlated phases and programmable quantum computing.
2-dimensional systems↗
Bioenergy Cropping Reduces the Spatiotemporal Scaling of Soil Bacterial Biodiversity
Widespread bioenergy cropping can transform landscapes, strongly affecting biodiversity. However, the impact of bioenergy cropping on the spatiotemporal scaling of soil biodiversity remains virtually unknown, despite its profound implications for the functioning of the ecological community. Here, we investigated how bioenergy cropping influenced the spatiotemporal scaling of soil bacterial biodiversity in marginal soils (sandy loam and clay loam soils) in Oklahoma, USA. We detected strong, significant species-time-area relationships (STARs) and phylogenetic-time-area relationships (PTARs) in bacterial communities and their lineages, suggesting that STARs and PTARs exist in microbial ecology within the studied system. Also, spatiotemporal scaling rates (the slopes of STAR and PTAR models) varied substantially among bacterial lineages and were positively correlated with their 16S rRNA gene copy numbers, a genomic trait indicative of microbial growth potentials. Strikingly, bioenergy cropping significantly reduced spatiotemporal scaling rates by 6.8%-14.1%, with a more pronounced reduction observed in sandy loam soils, where those rates were significantly lower than in clay loam soils. The heterogeneity of soil phosphorus and carbon resulted in variations in bacterial spatiotemporal scaling rates. Collectively, our findings suggest that bioenergy cropping may alleviate rapid shifts in soil biodiversity across space and time, thereby stabilizing soil biodiversity and supporting its role as part of sustainable land management and climate mitigation strategies.
bacterial diversity↗
Spatiotemporal Variations of Evapotranspiration in Amazonia Using the Wavelet Phase Difference Analysis
The relationships and seasonal-to-annual variations among evapotranspiration (ET), precipitation (P), terrestrial water storage anomalies (TWSA), radiation (downward shortwave radiation, DSR), and phenology (leaf area index, LAI) are complex across the Amazon basin. Here, to analyze how ET is controlled by these influencing factors, we used wavelet phase difference (WPD) to investigate the effects of P, TWSA, DSR, and LAI on ET at different spatiotemporal scales. The Amazon-scale averaged ET has strong correlations with these factors at the annual and multi-year periodicities. The patterns of WPDs have south-north and west-east divides due to the significant variation in climatic conditions. The results demonstrate that ET is mainly affected by water and energy availability while vegetation regulates both processes. The deep soil moisture/groundwater can provide strong subsidies to ET during the meteorological dry season in the water-limited area of Amazon. The WPD can well reflect the responses of ET to the variations of P, TWSA, DSR, and LAI, and the process of vegetation sustaining ET in the dry years in the water-limited area of the Amazon.
54 ENVIRONMENTAL SCIENCES↗
Unveiling defect-mediated carrier dynamics in monolayer semiconductors by spatiotemporal microwave imaging
The optoelectronic properties of atomically thin transition-metal dichalcogenides are strongly correlated with the presence of defects in the materials, which are not necessarily detrimental for certain applications. For instance, defects can lead to an enhanced photoconduction, a complicated process involving charge generation and recombination in the time domain and carrier transport in the spatial domain. Here, we report the simultaneous spatial and temporal photoconductivity imaging in two types of WS 2 monolayers by laser-illuminated microwave impedance microscopy. The diffusion length and carrier lifetime were directly extracted from the spatial profile and temporal relaxation of microwave signals, respectively. Time-resolved experiments indicate that the critical process for photoexcited carriers is the escape of holes from trap states, which prolongs the apparent lifetime of mobile electrons in the conduction band. As a result, counterintuitively, the long-lived photoconductivity signal is higher in chemical-vapor deposited (CVD) samples than exfoliated monolayers due to the presence of traps that inhibits recombination. Overall, our work reveals the intrinsic time and length scales of electrical response to photoexcitation in van der Waals materials, which is essential for their applications in optoelectronic devices.
42 ENGINEERING↗
MSASGCN : Multi-Head Self-Attention Spatiotemporal Graph Convolutional Network for Traffic Flow Forecasting
Traffic flow forecasting is an essential task of an intelligent transportation system (ITS), closely related to intelligent transportation management and resource scheduling. Dynamic spatial-temporal dependencies in traffic data make traffic flow forecasting to be a challenging task. Most existing research cannot model dynamic spatial and temporal correlations to achieve well-forecasting performance. The multi-head self-attention mechanism is a valuable method to capture dynamic spatial-temporal correlations, and combining it with graph convolutional networks is a promising solution. Therefore, we propose a multi-head self-attention spatiotemporal graph convolutional network (MSASGCN) model. It can effectively capture local correlations and potential global correlations of spatial structures, can handle dynamic evolution of the road network, and, in the time dimension, can effectively capture dynamic temporal correlations. Experiments on two real datasets verify the stability of our proposed model, obtaining a better prediction performance than the baseline algorithms. The correlation metrics get significantly reduced compared with traditional time series prediction methods and deep learning methods without using graph neural networks, according to MAE and RMSE results. Compared with advanced traffic flow forecasting methods, our model also has a performance improvement and a more stable prediction performance. We also discuss some problems and challenges in traffic forecasting.
Cao, Yang↗
Applying Gaussian Process Machine Learning and Modern Probabilistic Programming to Satellite Data to Infer CO 2 Emissions
Satellite data provides essential insights into the spatiotemporal distribution of CO 2 concentrations. However, many atmospheric inverse models fail to adequately incorporate the spatial and temporal correlations inherent in satellite observations and often lack rigorous methods for estimating parameters like spatial length scales. We introduce an inference model that processes the spatiotemporal covariance in satellite data and estimates hyperparameters such as covariance length scales. Our approach uses the Gaussian process (GP) machine learning (ML) and modern probabilistic programming languages (PPLs) to perform atmospheric inversions of emissions from satellite data. We develop a GP ML inversion system based on modern PPLs and the GEOS-Chem chemical transport model, simulating atmospheric CO 2 concentrations corresponding to the Orbiting Carbon Observatory-2/3 (OCO-2/3) data for July 2020. In our supervised learning framework, we treat the GEOS-Chem simulated data set as the target, with predictors derived by scaling the target with sector-specific factors hidden from the GP machine. Our results show that the GP model, combined with GPU-enabled PPLs, effectively retrieves true emission scaling factors and infers noise levels concealed within the data. This suggests that our method could be applied over larger areas with more complex covariance structures, enabling comprehensive analysis of the spatiotemporal patterns observed in OCO-2/3 and similar satellite data sets.
54 ENVIRONMENTAL SCIENCES↗
Enhanced imaging of electronic hot spots using quantum squeezed light
Detecting electronic hot spots is important for understanding the heat dissipation and thermal management of electronic and semiconductor devices. Optical thermoreflective imaging is being used to perform precise temporal and spatial imaging of heat on wires and semiconductor materials. We apply quantum squeezed light to perform thermoreflective imaging on micro-wires, surpassing the shot-noise limit of classical approaches. We obtain a far-field temperature sensing accuracy of 42 mK after 50 ms of averaging and show that a 256×256 pixel image can be constructed with such sensitivity in 10 min. We can further obtain single-shot temperature sensing of 1.6 K after only 10 μs of averaging, enabling a dynamical study of heat dissipation. Not only do the quantum images provide accurate spatiotemporal information about heat distribution but also the measure of quantum correlation provides additional information, inaccessible by classical techniques, which can lead to a better understanding of the dynamics. We apply the technique to both aluminum and niobium microwires and discuss the applications of the technique in studying electron dynamics at low temperatures.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Revealing complex subsurface dynamics with continuous seismic monitoring: Observations using distributed acoustic sensing and surface orbital vibrators during hydraulic fracturing
Understanding hydraulic fracturing is crucial to improving the stimulation of unconventional reservoirs and increasing fluid production. This study develops a novel seismic monitoring technology using distributed acoustic sensing (DAS) and surface orbital vibrators (SOV) to capture fracture seismic response and mechanical properties at high temporal intervals. We analyze continuous time-lapse vertical seismic profiling (VSP) data acquired every hour during the first nine days of treatment of an unconventional reservoir in the Austin Chalk/Eagle Field Laboratory. The VSP data contain clear seismic signals scattered from the activated fractures. The spatiotemporal changes of the fracture reflectivity revealed by the SOV/DAS data correlate well with the observations of fracture locations inferred from low-frequency DAS data. These results capture the fracture opening and closure processes, as well as highlight potential prestage activations of the fractures due to hydraulic connectivity with preexisting fracture systems. Therefore, analysis of the presented data set provides a unique opportunity to understand fracture initiation and subsequent evolution, not only in the context of unconventional resources but also in enhanced geothermal systems.
Correa, Julia↗
Spatiotemporal mapping of mesoscopic liquid dynamics
The study of liquid dynamics at mesoscopic scales is still strewn with difficulty due to limitations in theory and experiment. Historically, significant attention has been given to the analysis of space-time correlation functions and their frequency-Fourier transforms at a few discrete wave numbers. The massive computing power afforded by modern high performance computing clusters and the advent of a wide-angle neutron spin-echo spectrometer, however, have unlocked a more intuitive and fruitful approach to this problem. Using molecular dynamics simulations, here we demonstrate the benefits of spatiotemporally mapping intermediate scattering functions on a dense grid of correlation times and wave numbers. Four model systems are investigated: a Lennard-Jones liquid, a coarse-grained bead-spring polymer, a molten sodium chloride, and a poly(ethylene oxide) melt. We show that the spatiotemporal mapping approach is particularly useful for elucidating the mesoscopic dynamics in these liquids, where several underlying mechanisms, such as molecular relaxations, hydrodynamic modes, and nonhydrodynamic excitations, are potentially at play. Therefore, compared to the traditional method, direct visualization of density space-time correlation functions on two-dimensional color maps permits appraisals of complicated dynamical behavior at mesoscales in a global manner. For example, the scaling relations between space and time for different types of molecular motions can be straightforwardly identified on these plots, without any model-dependent analysis. Additionally, we show how theoretical ideas regarding collective mesoscopic dynamics, such as the classical hydrodynamic theory, the convolution approximation, and a recently proposed phenomenological model, can be discussed in terms of the global features of spatiotemporal maps of intermediate scattering functions. The new perspective offered by the spatiotemporal mapping method should prove useful for the study of liquid dynamics in general.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
An artificial spiking quantum neuron
Abstract Artificial spiking neural networks have found applications in areas where the temporal nature of activation offers an advantage, such as time series prediction and signal processing. To improve their efficiency, spiking architectures often run on custom-designed neuromorphic hardware, but, despite their attractive properties, these implementations have been limited to digital systems. We describe an artificial quantum spiking neuron that relies on the dynamical evolution of two easy to implement Hamiltonians and subsequent local measurements. The architecture allows exploiting complex amplitudes and back-action from measurements to influence the input. This approach to learning protocols is advantageous in the case where the input and output of the system are both quantum states. We demonstrate this through the classification of Bell pairs which can be seen as a certification protocol. Stacking the introduced elementary building blocks into larger networks combines the spatiotemporal features of a spiking neural network with the non-local quantum correlations across the graph.
Kristensen, Lasse Bjørn (ORCID:0000000239398170)↗
Operando methods: A new era of electrochemistry
One of the grand challenges facing electrochemistry is to directly resolve the complex nature of (electro)catalyst active sites and capture real-time “movies” of reaction dynamics, i.e. “watching chemistry in action”. The need for such fundamental understanding has stimulated the development of operando/in situ methods, which have greatly enhanced our ability to identify activity descriptors of electrocatalysts and establish structure–property relationships of energy materials. Here, this review summarizes the frontiers of operando electrochemical liquid-cell scanning transmission electron microscopy and correlative synchrotron X-ray methods, which are complementary tools to comprehensively investigate reaction dynamics across multiple spatiotemporal scales. In an effort to encourage greater adoption of advanced operando methods by the general electrochemistry community, this review points out the need to benchmark electrochemistry in confined and heterogenous liquid environment with minimal beam-induced damage. We anticipate that multimodal operando methods will become indispensable for understanding interfacial reaction mechanisms for the broad chemistry and energy materials communities.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Upregulation of PPPDE1 contributes to anorectal malformations via the mitochondrial apoptosis pathway during hindgut development in rats
Congenital anorectal malformations (ARMs) are among the most prominent deformities of the gastrointestinal tract; however, their precise aetiology remains obscure. Immunohistochemistry demonstrated that, in the ARM group, the PPPDE1-positive cells were widely distributed in the hindgut epithelial tissue from GD13 to GD16. Immunofluorescence revealed that most TUNEL-, Bax-, and Cytochrome C (Cyt C)-positive cells overlapped with PPPDE1-positive cells in the urorectal septum (URS). Western blotting and quantitative real-time RT-PCR revealed that PPPDE1 levels were significantly higher in the ARM group from GD13 to GD14 (p < 0.05). IEC-6 cells were transfected with PPPDE1 overexpression plasmid/NC (negative control) or si-PPPDE1/si-NC. Flow cytometry analysis and CCK-8 assay (used to detect apoptosis and proliferation, respectively), as well as western blotting, showed that the levels of PPPDE1 were positively correlated with the pro-apoptotic molecules Bax and Cyt C. Accordingly, aberrantly high expression of PPPDE1 caused a spatiotemporal imbalance in foetal rats with ARMs during hindgut development. Therefore, the upregulation of PPPDE1 may promote epithelial apoptosis and reduce proliferation in the hindgut via the mitochondrial apoptotic pathway. This could affect the fusion of the URS and cloacal membrane, ultimately inhibiting the hindgut development and resulting in ARMs.
60 APPLIED LIFE SCIENCES↗