Electromagnetic Spatiotemporal Differentiators

Spatiotemporal optical computing devices which could perform mathematical operations in both spatial and temporal domains can provide unprecedented measures to build efficient and real-time information processing systems. It is particularly important to realize the comprehensive functions in a compact design for better integration with electronic components. In this work, we experimentally demonstrated an analogue spatiotemporal differentiator in microwaves based on an asymmetrical metasurface which has a phase singularity in the spatiotemporal domain. We showed that this structure could give rise to a spatiotemporal transfer function required by an ideal first-order differentiator in both spatial and temporal domains by tailoring the unidirectional excitation of spoof surface plasmon polaritons (SSPPs). The spatial edge detection was performed utilizing a metallic slit and the temporal differentiation capability of the device was examined by Gaussian-like temporal pulses of different width. We further confirmed the differentiator demonstrated here could detect sharp changes of spatiotemporal pulses even with intricate profiles and theoretically estimated the resolution limits of the spatial and temporal edge detection. We also show that the pulse input after passing the spatiotemporal differentiator implemented here could carry a transverse orbital angular momentum (OAM) with a fractal topology charge which further increases the information quantity.Comment: 6 figure

Carbon nitride nanotubes with in situ grafted hydroxyl groups for highly efficient spontaneous H2O2 production

An active and inexpensive photocatalyst for H2O2 production is desirable for industrial applications. However, obtaining high photocatalytic activity from metal-free catalysts without the use of sacrificial electron donors is difficult. Herein, g-C3N4 (CN) nanotubes functionalized with surface > OH groups that are grafted in situ were successfully synthesized via a novel alkalinization process. The nanotube structures provide a large surface area and improved mass transfer properties. In situ grafted > OH groups can capture photogenerated holes to promote separation of photogenerated charge, enabling the ready availability of electrons and hydrogen ions for H2O2 production. Further, the surface > OH groups help to suppress H2O2 self-decomposition. Consequently, a high rate of 240.36 μmol h−1 g−1 of H2O2 production can be achieved without sacrificial agents, which is the highest H2O2 production in a spontaneous system for metal-free photocatalysts. This work provides a new strategy for an efficient and spontaneous H2O2 production method using a metal-free CN photocatalyst. © 2021 Elsevier B.V.1

Mice with Shank3 Mutations Associated with ASD and Schizophrenia Display Both Shared and Distinct Defects

Genetic studies have revealed significant overlaps of risk genes among psychiatric disorders. However, it is not clear how different mutations of the same gene contribute to different disorders. We characterized two lines of mutant mice with Shank3 mutations linked to ASD and schizophrenia. We found both shared and distinct synaptic and behavioral phenotypes. Mice with the ASD-linked InsG3680 mutatio n manifest striatal synaptic transmission defects before weaning age and impaired juvenile social interaction, coinciding with the early onset of ASD symptoms. On the other hand, adult mice carrying the schizophrenia-linked R1117X mutation show profound synaptic defects in prefrontal cortex and social dominance behavior. Furthermore, we found differential Shank3 mRNA stability and SHANK1/2 upregulation in these two lines. These data demonstrate that different alleles of the same gene may have distinct phenotypes at molecular, synaptic, and circuit levels in mice, which may inform exploration of these relationships in human patients.National Institute of Mental Health (U.S.) (Grant 5R01MH097104)National Institute of Mental Health (U.S.) (Grant 5DP1-MH100706)National Institutes of Health (U.S.) (Grant R01-NS 07312401

Working Memory Cells' Behavior May Be Explained by Cross-Regional Networks with Synaptic Facilitation

Neurons in the cortex exhibit a number of patterns that correlate with working memory. Specifically, averaged across trials of working memory tasks, neurons exhibit different firing rate patterns during the delay of those tasks. These patterns include: 1) persistent fixed-frequency elevated rates above baseline, 2) elevated rates that decay throughout the tasks memory period, 3) rates that accelerate throughout the delay, and 4) patterns of inhibited firing (below baseline) analogous to each of the preceding excitatory patterns. Persistent elevated rate patterns are believed to be the neural correlate of working memory retention and preparation for execution of behavioral/motor responses as required in working memory tasks. Models have proposed that such activity corresponds to stable attractors in cortical neural networks with fixed synaptic weights. However, the variability in patterned behavior and the firing statistics of real neurons across the entire range of those behaviors across and within trials of working memory tasks are typical not reproduced. Here we examine the effect of dynamic synapses and network architectures with multiple cortical areas on the states and dynamics of working memory networks. The analysis indicates that the multiple pattern types exhibited by cells in working memory networks are inherent in networks with dynamic synapses, and that the variability and firing statistics in such networks with distributed architectures agree with that observed in the cortex

Simulation and Evaluation of Statistical Downscaling of Regional Daily Precipitation over North China Based on Self-Organizing Maps

A statistical downscaling method based on Self-Organizing Maps (SOM), of which the SOM Precipitation Statistical Downscaling Method (SOM-SD) is named, has received increasing attention. Herein, its applicability of downscaling daily precipitation over North China is evaluated. Six indices (total season precipitation, daily precipitation intensity, mean number of precipitation days, percentage of rainfall from events beyond the 95th percentile value of overall precipitation, maximum consecutive wet days, and maximum consecutive dry days) are selected, which represent the statistics of daily precipitation with regards to both precipitation amount and frequency, as well as extreme event. The large-scale predictors were extracted from the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) daily reanalysis data, while the prediction was the high resolution gridded daily observed precipitation. The results show that the method can establish certain conditional transformation relationships between large-scale atmospheric circulation and local-scale surface precipitation in a relatively simple way. This method exhibited a high skill in reproducing the climatologic statistical properties of the observed precipitation. The simulated daily precipitation probability distribution characteristics can be well matched with the observations. The values of Brier scores are between 0 and 1.5 × 10−4 and the significance scores are between 0.8 and 1 for all stations. The SOM-SD method, which is evaluated with the six selected indicators, shows a strong simulation capability. The deviations of the simulated daily precipitation are as follows: Total season precipitation (−7.4%), daily precipitation intensity (−11.6%), mean number of rainy days (−3.1 days), percentage of rainfall from events beyond the 95th percentile value of overall precipitation (+3.4%), maximum consecutive wet days (−1.1 days), and maximum consecutive dry days (+3.5 days). In addition, the frequency difference of wet-dry nodes is defined in the evaluation. It is confirmed that there was a significant positive correlation between frequency difference and precipitation. The findings of this paper imply that the SOM-SD method has a good ability to simulate the probability distribution of daily precipitation, especially the tail of the probability distribution curve. It is more capable of simulating extreme precipitation fields. Furthermore, it can provide some guidance for future climate projections over North China

Comparisons of the Synoptic Characteristics of 14-Day Extreme Precipitation Events in Different Regions of Eastern China

How to predict and study persistent extreme precipitation events (PEPEs) with a prediction period of 1–2 weeks is an important scientific problem faced by the meteorological circles at home and abroad. Based on the accurate description of the flood range caused by 14-day PEPEs, the comprehensive analysis method was used to obtain the weather characteristics related to 14-day PEPEs (including abnormal trough/ridge, westerly jet, atmospheric river (AR) activity, teleconnections, etc.). First, we selected three regions in China, North China (NC), the Yangtze River valley (YRV), and South China (SC), analyzed their 14-day PEPEs in summer (June to August), and composited them into an average circulation (500 hPa geopotential height field) to compare the weather patterns related to PEPEs in these regions. Then, several variables are composited to understand the evolution of the atmospheric state before and during the occurrence of PEPEs. Finally, potential applications of several teleconnection types and composites in advance prediction are studied. The main findings include: the common weather signals during the occurrence of PEPEs are characterized by obvious and continuous a high-low-high saddle field circulation configuration (conducive to the formation of frequent heavy rainfall), active westerly jet (westerly jet is the controlling factor of precipitation), and enhanced water vapor transport (significantly increased atmospheric river activity). In this study, some key characteristics and development of PEPEs were identified, the formation mechanism of China’s 14-day PEPEs was revealed, the role of ARs in PEPEs was recognized, and the PEPEs precursor signal was extracted. Furthermore, PEPEs in different regions were also compared, which played an important role in understanding and predicting similar events

Features and Evolution of Autumn Weather Regimes in the Southeast China

Autumn is the transitional season when the atmospheric circulation pattern changes from summer to winter. The temperature and precipitation in Southeastern China in autumn are significantly influenced by the change in circulation patterns, and both show significant uniqueness. The clustering method can be used to observe the changes of circulation patterns in detail and to observe and analyze the transition from warm to cold seasons from a detailed view of the daily circulation pattern perspective. This method may have important research implications on how to study the generation and dissipation of extreme weather events. The Self-Organizing Maps (SOM) method is used to a 500 hPa geopotential height and 850 hPa wind and sea level pressure for 1981–2020 to identify the characteristic weather patterns (WTs) in autumn (September–November) over Southeastern China. Characteristics of the captured WTs are also analyzed in terms of the distribution characteristics of weather patterns, occurrence frequency, typical progression, precipitation and extreme precipitation (EP), temperature and extreme high temperature (EHT), and the relationship with atmospheric teleconnection. Nine WTs were identified in autumn, which represents a series of weather situations consisting of troughs and ridges. On this basis, these WTs were used to carry out the differentiation of seasonal differences between early and late autumn. The maximum mean and extreme precipitation occur in several early season patterns (WT1, WT2, WT4, and WT7). It is highly likely that extremely high temperatures occur in the WT1 and WT2 patterns. The most common progression between WTs is WT7−WT1−WT2−WT4 in the early season. This seasonality allows us to distinguish between early and late seasons based on daily weather types. A preliminary trend analysis suggests that patterns in the early season occur more frequently and last longer in the early season, and patterns in the late season occur less frequently and later. That is, the longer cool season pattern is shifting to the shorter warm season pattern. In addition, the persistence of both cool and warm patterns increased during 2001–2020 relative to 1981–2000, and the risk of both flooding and drought occurrence is on the rise

Simulation and Evaluation of Statistical Downscaling of Regional Daily Precipitation over North China Based on Self-Organizing Maps

A statistical downscaling method based on Self-Organizing Maps (SOM), of which the SOM Precipitation Statistical Downscaling Method (SOM-SD) is named, has received increasing attention. Herein, its applicability of downscaling daily precipitation over North China is evaluated. Six indices (total season precipitation, daily precipitation intensity, mean number of precipitation days, percentage of rainfall from events beyond the 95th percentile value of overall precipitation, maximum consecutive wet days, and maximum consecutive dry days) are selected, which represent the statistics of daily precipitation with regards to both precipitation amount and frequency, as well as extreme event. The large-scale predictors were extracted from the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) daily reanalysis data, while the prediction was the high resolution gridded daily observed precipitation. The results show that the method can establish certain conditional transformation relationships between large-scale atmospheric circulation and local-scale surface precipitation in a relatively simple way. This method exhibited a high skill in reproducing the climatologic statistical properties of the observed precipitation. The simulated daily precipitation probability distribution characteristics can be well matched with the observations. The values of Brier scores are between 0 and 1.5 × 10−4 and the significance scores are between 0.8 and 1 for all stations. The SOM-SD method, which is evaluated with the six selected indicators, shows a strong simulation capability. The deviations of the simulated daily precipitation are as follows: Total season precipitation (−7.4%), daily precipitation intensity (−11.6%), mean number of rainy days (−3.1 days), percentage of rainfall from events beyond the 95th percentile value of overall precipitation (+3.4%), maximum consecutive wet days (−1.1 days), and maximum consecutive dry days (+3.5 days). In addition, the frequency difference of wet-dry nodes is defined in the evaluation. It is confirmed that there was a significant positive correlation between frequency difference and precipitation. The findings of this paper imply that the SOM-SD method has a good ability to simulate the probability distribution of daily precipitation, especially the tail of the probability distribution curve. It is more capable of simulating extreme precipitation fields. Furthermore, it can provide some guidance for future climate projections over North China