An encounter-based approach for restricted diffusion with a gradient drift

We develop an encounter-based approach for describing restricted diffusion with a gradient drift towards a partially reactive boundary. For this purpose, we introduce an extension of the Dirichlet-to-Neumann operator and use its eigenbasis to derive a spectral decomposition for the full propagator, i.e., the joint probability density function for the particle position and its boundary local time. This is the central quantity that determines various characteristics of diffusion-influenced reactions such as conventional propagators, survival probability, first-passage time distribution, boundary local time distribution, and reaction rate. As an illustration, we investigate the impact of a constant drift onto the boundary local time for restricted diffusion on an interval. More generally, this approach accesses how external forces may influence the statistics of encounters of a diffusing particle with the reactive boundary.

Degeneracy of light scattering and absorption by a single nanowire

We theoretically and numerically prove that under an electromagnetic plane wave with linear polarization incident normally to a single nanowire, there exists a power diagram that could indicate scattering properties for any system configurations, material parameters, and operating wavelength. We demonstrate the distinct power distribution boundary in absorption, scattering, and extinction for a generalized nanowire with any partial wave modes dominant. In the boundary, each dominant scattering coefficients remain constant, and its energy performance would display superabsorbers or superscatterers. Interestingly, for a system with larger partial wave modes dominant, the occupied domain in the power diagram could completely cover that with lower ones. Hence, a system with different levels of partial wave modes can display the same power results, reflecting the degeneracy. This degenerate property could release more degrees of freedom in design of energy harvesting devices and sensors. We demonstrate several systems based on realistic materials to support our findings.

Identifying meteorological influences on marine low-cloud mesoscale morphology using satellite classifications

Marine low-cloud mesoscale morphology in the southeastern Pacific Ocean is analyzed using a large dataset of classifications spanning 3 years generated by machine learning methods. Meteorological variables and cloud properties are composited by the mesoscale cloud type of the classification, showing distinct meteorological regimes of marine low-cloud organization from the tropics to the midlatitudes. The presentation of mesoscale cellular convection, with respect to geographic distribution, boundary layer structure, and large-scale environmental conditions, agrees with prior knowledge. Two tropical and subtropical cumuliform boundary layer regimes, suppressed cumulus and clustered cumulus, are studied in detail. The patterns in precipitation, circulation, column water vapor, and cloudiness are consistent with the representation of marine shallow mesoscale convective self-aggregation by large eddy simulations of the boundary layer. Although they occur under similar large-scale conditions, the suppressed and clustered low-cloud types are found to be well separated by variables associated with low-level mesoscale circulation, with surface wind divergence being the clearest discriminator between them, regardless of whether reanalysis or satellite observations are used. Clustered regimes are associated with surface convergence, while suppressed regimes are associated with surface divergence.

Degeneracy of Light Scattering and Absorption by a Single Nanowire

We theoretically and numerically prove that under an electromagnetic plane wave with linear polarization incident normally to a single nanowire, there exists a power diagram that could indicate scattering properties for any system configurations. We demonstrate the distinct power distribution boundary in absorption, scattering, and extinction for a generalized nanowire with any partial wave modes dominant. In the boundary, each dominant scattering coefficients remain constant, and its energy performance would display superabsorbers or superscatterers. Interestingly, for a system with larger partial wave modes dominant, the occupied domain in the power diagram could completely cover that with lower ones. Hence, a system with different levels of partial wave modes can display the same power results, reflecting the degeneracy. This degenerate property could release more degrees of freedom in designs of energy harvesting devices and sensors. We demonstrate several systems based on realistic materials to support our findings.

The Application of Neural Operator in subsurface process simulation

<p>Numerical simulations of subsurface processes are essential to the success of many geoengineering projects. These simulations often contain significant uncertainties due to imperfect knowledge of material properties and their spatial distribution, boundary conditions, and initial conditions. However, efficient implementations for the quantification of uncertainties for such simulations are big challenges in Computational Geoscience, mainly due to the curse of dimensionality. Process simulations often involve solving high-dimensional Partial Differential Equations (PDE) by using discretization methods such as Finite Difference (FD) or Finite Elements (FE) methods. Although such methods often give good approximations, they are computationally intensive and expensive and therefore infeasible in the applications such as MCMC where thousands of evaluations of the forward simulation are required. Previous work by Degen et.al. (2020) has addressed this problem by using a model order reduction method, the so-called reduced basis (RB) method. However, the method has limitations when considering complex (i.e., hyperbolic and non-linear) PDEs. In this work, we aim to employ the recently developed Fourier Neural Operator (FNO) (Li, 2020) as a tool to implement efficient approximation of PDEs in the application of Geothermal reservoir simulation. FNO involves a Fast Fourier transform to directly learn the mapping from the input function to the output function. FNO has the advantage of being independent of the resolution and complexity of the governing PDE. Our preliminary results show that FNO can provide good approximation results in solving four-dimensional PDEs and thus can be used as a tool for further probability studies of the parameters of interest.</p>

Identifying meteorological influences on marine low cloud mesoscale morphology using deep learning classifications

Marine low cloud mesoscale morphology in the southeastern Pacific Ocean is analyzed using a large dataset of machine-learning generated classifications spanning three years. Meteorological variables and cloud properties are composited by mesoscale cloud type, showing distinct meteorological regimes of marine low cloud organization from the tropics to the midlatitudes. The presentation of mesoscale cellular convection, with respect to geographic distribution, boundary layer structure, and large-scale environmental conditions, agrees with prior knowledge. Two tropical and subtropical cumuliform boundary layer regimes, suppressed cumulus and clustered cumulus, are studied in detail. The patterns in precipitation, circulation, column water vapor, and cloudiness are consistent with the representation of marine shallow mesoscale convective self-aggregation by large eddy simulations of the boundary layer. Although they occur under similar large-scale conditions, the suppressed and clustered low cloud types are found to be well-separated by variables associated with low-level mesoscale circulation, with surface wind divergence being the clearest discriminator between them, whether reanalysis or satellite observations are used. Clustered regimes are associated with surface convergence and suppressed regimes are associated with surface divergence.

Taxonomic revision of Schausiana Viette with two new species from Guatemala and notes on biogeography and correlated tectonics (Lepidoptera: Hepialidae)

We morphologically validate the genus Schausiana Viette, 1950 as a monophyletic group comprising five species—S. phalerus (Druce, 1887) comb. n. and S. trojesa (Schaus, 1901) from Mexico, S. chalciope sp. n. and S. maishei sp. n. from Guatemala and S. pharus (Druce, 1887) comb. n. from Mexico to Costa Rica. Potential morphological monophyly of the genus is supported by the unique shared presence of specialized scales spine-like, elongate spinous, or piliform, located along the forewing veins. We investigate possible close phylogenetic relationships of this genus with the Mexican and Central American genera Pallas C. Mielke & Grehan, 2015, and Phassus Walker, 1856, and the southeastern Brazilian genus, Phthius C. Mielke & Grehan, 2017. Also discussed are the shared presence of several characters supporting the morphological monophyly of Schausiana, Phassus, and Pallas as ‘phassine’ Hepialidae. The distribution range of Schausiana is bounded in its northwestern range by the Guerrero terrane. Other than the widespread S. pharus with a disjunct record from Costa Rica, the southern distribution boundary corresponds to the Motagua-Polochic Fracture Zone in Guatemala. We suggest that the ancestral range of Schausiana did not include the Guerrero terrane and was possibly also absent from the Chocos Block (Honduras, El Salvador, Nicaragua) and the Costa Rica-Panama arc of Central America. Further divergence of Schausiana is predicted to have been influenced by tectonic events during late Cretaceous or early Cenozoic time. Colour photos are presented for all species for the first time. Holotypes of S. maishei, sp. n., and S. chalciope, sp. n., are deposited in the Universidad del Valle de Guatemala, Guatemala.

Transcriptome analysis of flavonoid biosynthesis in safflower flowers grown under different light intensities

Background Safflower (Carthamus tinctorius L.) is a domesticated species with a long history of cultivation and widespread distribution across the globe, and light plays an important role in controlling its distribution boundary. Flowers from safflower have been widely used in traditional Chinese medicine because of their ability to improve cerebral blood flow. Flavonoids are the main active compounds in safflower and have many pharmacological effects. In this study, we aimed to explore the relationship between different light intensities and flavonoid biosynthesis in safflower flowers cultivated in greenhouse. Methods The transcriptome of safflower flowers grown under different light intensities were sequenced through BGISEQ-500 platform. After assembled and filtered, Unigenes were annotated by aligning with seven functional databases. Differential expression analysis of two samples was performed with the DEseq2 package. Differentially expressed genes (DEGs) related with flavonoids biosynthesis were analyzed by Real-time PCR (RT-PCR). Flavonoids accumulation in flowers were determined by high performance liquid chromatography and spectrophotometer. Results Transcriptome analysis of safflower flowers cultivated under different light intensities was performed. A total of 99.16 Gb data were obtained, and 78,179 Unigenes were annotated. Among the DEGs, 13 genes were related to flavonoid biosynthesis. The differential expressions of seven key genes were confirmed by RT-PCR. In addition, the levels of some flavonoids were measured in safflower flowers grown under different light intensities. CtHCT3 gene expression showed a significantly negative correlation with kaempferol content in safflower grown under different light intensities. Conclusion Our results strongly suggested that the reduction in light intensity in a suitable range promoted flavonoid biosynthesis in safflower flowers. We suggest that the expressions of HCT genes played an important role in flavonoid accumulation in safflower flowers. Our study lays a foundation for further research on the effects of light on flavonoid biosynthesis in safflower.

Thermal Tolerance of Gloomy Scale (Hemiptera: Diaspididae) in the Eastern United States

An insect species’ geographic distribution is probably delimited in part by physiological tolerances of environmental temperatures. Gloomy scale (Melanaspis tenebricosa (Comstock)) is a native insect herbivore in eastern U.S. forests. In eastern U.S. cities, where temperatures are warmer than nearby natural areas, M. tenebricosa is a primary pest of red maple (Acer rubrum L.; Sapindales: Sapindaceae) With warming, M. tenebricosa may spread to new cities or become pestilent in forests. To better understand current and future M. tenebricosa distribution boundaries, we examined M. tenebricosa thermal tolerance under laboratory conditions. We selected five hot and five cold experimental temperatures representative of locations in the known M. tenebricosa distribution. We built models to predict scale mortality based on duration of exposure to warm or cold experimental temperatures. We then used these models to estimate upper and lower lethal durations, i.e., temperature exposure durations that result in 50% mortality. We tested the thermal tolerance for M. tenebricosa populations from northern, mid, and southern locations of the species’ known distribution. Scales were more heat and cold tolerant of temperatures representative of the midlatitudes of their distribution where their densities are the greatest. Moreover, the scale population from the northern distribution boundary could tolerate cold temperatures from the northern boundary for twice as long as the population collected near the southern boundary. Our results suggest that as the climate warms the M. tenebricosa distribution may expand poleward, but experience a contraction at its southern boundary.