Search for: All records

Sudden stratospheric warmings (SSWs) are the most dramatic events in the wintertime stratosphere. Such extreme events are characterized by substantial disruption to the stratospheric polar vortex, which can be categorized into displacement and splitting types depending on the morphology of the disrupted vortex. Moreover, SSWs are usually followed by anomalous tropospheric circulation regimes that are important for subseasonal-to-seasonal prediction. Thus, monitoring the genesis and evolution of SSWs is crucial and deserves further advancement. Despite several analysis methods that have been used to study the evolution of SSWs, the ability of deep learning methods has not yet been explored, mainly due to the relative scarcity of observed events. To overcome the limited observational sample size, we use data from historical simulations of the Whole Atmosphere Community Climate Model version 6 to identify thousands of simulated SSWs, and use their spatial patterns to train the deep learning model. We utilize a convolutional neural network combined with a variational auto-encoder (VAE)—a generative deep learning model—to construct a phase diagram that characterizes the SSW evolution. This approach not only allows us to create a latent space that encapsulates the essential features of the vortex structure during SSWs, but also offers new insights into its spatiotemporal evolution mapping onto the phase diagram. The constructed phase diagram depicts a continuous transition of the vortex pattern during SSWs. Notably, it provides a new perspective for discussing the evolutionary paths of SSWs: the VAE gives a better-reconstructed vortex morphology and more clearly organized vortex regimes for both displacement-type and split-type events than those obtained from principal component analysis. Our results provide an innovative phase diagram to portray the evolution of SSWs, in which particularly the splitting SSWs are better characterized. Our findings support the future use of deep learning techniques to study the underlying dynamics of extreme stratospheric vortex phenomena, and to establish a benchmark to evaluate model performance in simulating SSWs.

 

The 2020–2021 record drought in Taiwan halted carbon sequestration in its predominantly evergreen subtropical forests. The analysis uncovers a significant correlation between net ecosystem exchange, radiative factors, groundwater levels, and wildfires, indicating that the severity of droughts leads to a shift from carbon absorption to emission in these forests, thereby inviting a broader examination of the climate–carbon nexus in future scenarios.

 

Functional magnetic resonance imaging faces inherent challenges when applied to deep-brain areas in rodents, e.g. entorhinal cortex, due to the signal loss near the ear cavities induced by susceptibility artifacts and reduced sensitivity induced by the long distance from the surface array coil. Given the pivotal roles of deep brain regions in various diseases, optimized imaging techniques are needed. To mitigate susceptibility-induced signal losses, we introduced baby cream into the middle ear. To enhance the detection sensitivity of deep brain regions, we implemented inductively coupled ear-bars, resulting in approximately a 2-fold increase in sensitivity in entorhinal cortex. Notably, the inductively coupled ear-bar can be seamlessly integrated as an add-on device, without necessitating modifications to the scanner interface. To underscore the versatility of inductively coupled ear-bars, we conducted echo-planner imaging-based task functional magnetic resonance imaging in rats modeling Alzheimer’s disease. As a proof of concept, we also demonstrated resting-state-functional magnetic resonance imaging connectivity maps originating from the left entorhinal cortex—a central hub for memory and navigation networks-to amygdala hippocampal area, Insular Cortex, Prelimbic Systems, Cingulate Cortex, Secondary Visual Cortex, and Motor Cortex. This work demonstrates an optimized procedure for acquiring large-scale networks emanating from a previously challenging seed region by conventional magnetic resonance imaging detectors, thereby facilitating improved observation of functional magnetic resonance imaging outcomes.

 

We present updated atmospheric tables suitable for calculating the post-formation evolution and cooling of Jupiter and Jupiter-like exoplanets. These tables are generated using a 1D radiative transfer modeling code that incorporates the latest opacities and realistic prescriptions for stellar irradiation and ammonia clouds. To ensure the accuracy of our model parameters, we calibrate them against the measured temperature structure and geometric albedo spectrum of Jupiter, its effective temperature, and its inferred internal temperature. As a test case, we calculate the cooling history of Jupiter using an adiabatic and homogeneous interior and compare with extant models now used to evolve Jupiter and the giant planets. We find that our model reasonably matches Jupiter after evolving a hot-start initial condition to the present age of the solar system, with a discrepancy in brightness temperature/radius within 2%. Our algorithm allows us to customize for different cloud, irradiation, and metallicity parameters. This class of boundary conditions can be used to study the evolution of solar system giant planets and exoplanets with more complicated interior structures and nonadiabatic, inhomogeneous internal profiles.

 

The nonlinear propagation of picosecond or femtosecond optical pulses in multimode fiber amplifiers underlies a variety of intriguing physical phenomena as well as the potential for scaling sources of ultrashort pulses to higher powers. However, existing theoretical models of ultrashort-pulse amplification do not include some critical processes, and, as a result, they fail to capture basic features of experiments. We introduce a numerical model that combines steady-state rate equations with the unidirectional pulse propagation equation, incorporating dispersion, Kerr and Raman nonlinearities, and gain/loss-spectral effects in a mode-resolved treatment that is computationally efficient. This model allows investigation of spatiotemporal processes that are strongly affected by gain dynamics. Its capabilities are illustrated through examinations of amplification in few-mode gain fiber, multimode nonlinear amplification, and beam cleaning in a multimode fiber amplifier.

 

A commonplace sight is seeing other people walk. Our visual system specializes in processing such actions. Notably, we are not only quick to recognize actions, but also quick to judge how elegantly (or not) people walk. What movements appear appealing, and why do we have such aesthetic experiences? Do aesthetic preferences for body movements arise simply from perceiving others’ positive emotions? To answer these questions, we showed observers different point-light walkers who expressed neutral, happy, angry, or sad emotions through their movements and measured the observers’ impressions of aesthetic appeal, emotion positivity, and naturalness of these movements. Three experiments were conducted. People showed consensus in aesthetic impressions even after controlling for emotion positivity, finding prototypical walks more aesthetically pleasing than atypical walks. This aesthetic prototype effect could be accounted for by a computational model in which walking actions are treated as a single category (as opposed to multiple emotion categories). The aesthetic impressions were affected both directly by the objective prototypicality of the movements, and indirectly through the mediation of perceived naturalness. These findings extend the boundary of category learning, and hint at possible functions for action aesthetics.

 

Despite extensive efforts to identify interhemispheric functional connectivity (FC) with resting-state (rs-) fMRI, correlated low-frequency rs-fMRI signal fluctuation across homotopic cortices originates from multiple sources. It remains challenging to differentiate circuit-specific FC from global regulation. Here, we developed a bilateral line-scanning fMRI method to detect laminar-specific rs-fMRI signals from homologous forepaw somatosensory cortices with high spatial and temporal resolution in rat brains. Based on spectral coherence analysis, two distinct bilateral fluctuation spectral features were identified: ultra-slow fluctuation (<0.04 Hz) across all cortical laminae versus Layer (L) 2/3-specific evoked BOLD at 0.05 Hz based on 4 s on/16 s off block design and resting-state fluctuations at 0.08–0.1 Hz. Based on the measurements of evoked BOLD signal at corpus callosum (CC), this L2/3-specific 0.05 Hz signal is likely associated with neuronal circuit-specific activity driven by the callosal projection, which dampened ultra-slow oscillation less than 0.04 Hz. Also, the rs-fMRI power variability clustering analysis showed that the appearance of L2/3-specific 0.08–0.1 Hz signal fluctuation is independent of the ultra-slow oscillation across different trials. Thus, distinct laminar-specific bilateral FC patterns at different frequency ranges can be identified by the bilateral line-scanning fMRI method. 

Abstract Self-assembled systems have recently attracted extensive attention because they can display a wide range of phase morphologies in nanocomposites, providing a new arena to explore novel phenomena. Among these morphologies, a bicontinuous structure is highly desirable based on its high interface-to-volume ratio and 3D interconnectivity. A bicontinuous nickel oxide (NiO) and tin dioxide (SnO 2 ) heteroepitaxial nanocomposite is revealed here. By controlling their concentration, we fabricated tuneable self-assembled nanostructures from pillars to bicontinuous structures, as evidenced by TEM-energy-dispersive X-ray spectroscopy with a tortuous compositional distribution. The experimentally observed growth modes are consistent with predictions by first-principles calculations. Phase-field simulations are performed to understand 3D microstructure formation and extract key thermodynamic parameters for predicting microstructure morphologies in SnO 2 :NiO nanocomposites of other concentrations. Furthermore, we demonstrate significantly enhanced photovoltaic properties in a bicontinuous SnO 2 :NiO nanocomposite macroscopically and microscopically. This research shows a pathway to developing innovative solar cell and photodetector devices based on self-assembled oxides. 

Kerr beam cleaning is a nonlinear phenomenon in graded-index multimode fiber where power flows toward the fundamental mode, generating bell-shaped output beams. Here we study beam cleaning of femtosecond pulses accompanied by gain in a multimode fiber amplifier. Mode-resolved energy measurements and numerical simulations showed that the amplifier generates beams with high fundamental mode content (greater than 30% of the overall pulse energy) for a wide range of amplification levels. Control experiments using stretched pulses that evolve without strong Kerr nonlinear effects showed a degrading beam profile, in contrast to nonlinear beam cleaning. Temporal measurements showed that seed pulse parameters have a strong effect on the amplified pulse quality. These results may influence the design of future high-performance fiber lasers and amplifiers.