Direct-Current Generator Based on Dynamic Water-Semiconductor Junction with Polarized Water as Moving Dielectric Medium

There is a rising prospective in harvesting energy from water droplets, as microscale energy is required for the distributed sensors in the interconnected human society. However, achieving a sustainable direct-current generating device from water flow is rarely reported, and the quantum polarization principle of the water molecular remains uncovered. Herein, we propose a dynamic water-semiconductor junction with moving water sandwiched between two semiconductors as a moving dielectric medium, which outputs a sustainable direct-current voltage of 0.3 V and current of 0.64 uA with low internal resistance of 390 kilohm. The sustainable direct-current electricity is originating from the dynamic water polarization process in water-semiconductor junction, in which water molecules are continuously polarized and depolarized driven by the mechanical force and Fermi level difference, during the movement of the water on silicon. We further demonstrated an encapsulated portable power-generating device with simple structure and continuous direct-current voltage, which exhibits its promising potential application in the field of wearable electronic generators

Rabies virus pseudotyped with CVS-N2C glycoprotein as a powerful tool for retrograde neuronal network tracing

Abstract Background: Efficient viral vectors for mapping and manipulating long projection neuronal circuits are crucial in brain structural and functional studies. The glycoprotein gene-deleted SAD strain rabies virus pseudotyped with the N2C glycoprotein (SAD-RV(ΔG)-N2C(G)) shows high neuro-tropism in cell culture, but its in vivo retrograde infection efficiency and neuro-tropism have not been systematically characterized. Methods: SAD-RV(ΔG)-N2C(G) and two other broadly used retrograde tracers, SAD-RV(ΔG)-B19(G) and rAAV2-retro were respectively injected into the VTA or DG in C57BL/6 mice. The neuron numbers labeled across the whole brain regions were counted and analyzed by measuring the retrograde infection efficiencies and tropisms of these viral tools. The labeled neural types were analyzed using fluorescence immunohistochemistry or GAD67-GFP mice. Result: We found that SAD-RV (ΔG)-N2C (G) enhanced the infection efficiency of long-projecting neurons by ~ 10 times but with very similar neuro-tropism, compared with SAD-RV (ΔG)-B19(G). On the other hand, SAD-RV(ΔG)-N2C(G) showed comparable infection efficiency with rAAV2-retro, but had a more restricted diffusion range, and broader tropism to different types and regions of long-projecting neuronal populations. Conclusions: These results demonstrate that SAD-RV(ΔG)-N2C(G) can serve as an effective retrograde vector for studying neuronal circuits. Key words：Viral vector, N2C Glycoprotein, Neuronal circuits, Retrograde tracin

The Effects of Outdoor Source on Pollution Characteristics and Dynamic Changes of Particulate Matter in an Office

The indoor air quality has a direct impact on human health. Particulate matter is one of the important factors affecting the indoor air quality. The paper selects an office as the study object and studies the pollution characteristics and dynamic changes of indoor particulate matter in different outdoor pollution levels. The mass concentration of outdoor PM10 is used as the evaluation basis of the outdoor pollution level. The outdoor PM10 concentration levels are divided into the range of 200–300, 300–400, 400–500, 500–600, 600–700 μg·m−3, individually. Firstly, the change characteristics of the mass concentration and the number concentration of the particulate matter in the five outdoor conditions are analyzed. Secondly, the maximum increase values and the maximum increase rates of the mass concentrations of different particle sizes in the five conditions are compared. Then, the penetration factors of the particulates in different sizes are compared among the five conditions. Finally, the correlation between indoor particulate matter and outdoor particulate matter is studied. The study results show that the effect of outdoor infiltration has a great influence on the indoor PM1 mass concentration, and the penetrating factors of the particulate matter between 0.3 μm and 0.5 μm are higher than 0.6; their permeability is the most obvious

A Multiscale Dual Attention Network for the Automatic Classification of Polar Sea Ice and Open Water Based on Sentinel-1 SAR Images

Automatic classification of sea ice and open water plays a vital role in climate change research, polar shipping, and other applications. Many deep-learning-based methods are proposed to automatically classify sea ice and open water to address this issue. Even though these methods have achieved remarkable success, the noise phenomenon in synthetic aperture radar (SAR) images still causes considerable limitations in the model performance. Meanwhile, these existing methods ignore multiscale global information from large-scale SAR images, which tends to produce misclassification. In this article, we propose a novel multiscale dual attention network (MSDA-Net) for the task. To tackle the first drawback, we introduce the information of relative position and high-pass filtering as two extra channels to reduce the noisy effects. Moreover, we propose a patch dual attention mechanism and embed it into the ConvNeXt blocks to capture the multichannel and spatial features. To address the second problem, we propose a multiscale spatial attention module to capture multiscale global spatial information. The experiments show that the proposed method significantly outperforms state-of-the-art methods. In addition, comprehensive case studies are conducted, which verify the effectiveness of MSDA-Net in different SAR scenes

Interaction-Temporal GCN: A Hybrid Deep Framework For Covid-19 Pandemic Analysis

The Covid-19 pandemic is still spreading around the world and seriously imperils humankind's health. This swift spread has caused the public to panic and look to scientists for answers. Fortunately, these scientists already have a wealth of data—the Covid-19 reports that each country releases, reports with valuable spatial-temporal properties. These data point toward some key actions that humans can take in their fight against Covid-19. Technically, the Covid-19 records can be described as sequences, which represent spatial-temporal linkages among the data elements with graph structure. Therefore, we propose a novel framework, the Interaction-Temporal Graph Convolution Network (IT-GCN), to analyze pandemic data. Specifically, IT-GCN introduces ARIMA into GCN to model the data which originate on nodes in a graph, indicating the severity of the pandemic in different cities. Instead of regular spatial topology, we construct the graph nodes with the vectors via ARIMA parameterization to find out the interaction topology underlying in the pandemic data. Experimental results show that IT-GCN is able to capture the comprehensive interaction-temporal topology and achieve well-performed short-term prediction of the Covid-19 daily infected cases in the United States. Our framework outperforms state-of-art baselines in terms of MAE, RMSE and MAPE. We believe that IT-GCN is a valid and reasonable method to forecast the Covid-19 daily infected cases and other related time-series. Moreover, the prediction can assist in improving containment policies

Quantum Tomography: From Markovianity to Non-Markovianity

The engineering of quantum computers requires the reliable characterization of qubits, quantum operations, and even the entire hardware. Quantum tomography is an indispensable framework in quantum characterization, verification, and validation (QCVV), which has been widely accepted by researchers. According to the tomographic target, quantum tomography can be categorized into quantum state tomography (QST), quantum process tomography (QPT), gate set tomography (GST), process tensor tomography (PTT), and instrument set tomography (IST). Standard quantum tomography toolkits generally consist of basic linear inverse methods and statistical maximum likelihood estimation (MLE)-based methods. Furthermore, the performance of standard methods, including effectiveness and efficiency, has been further developed by exploiting Bayesian estimation, neural networks, matrix completion techniques, etc. In this review, we introduce the fundamental quantum tomography techniques, including QST, QPT, GST, PTT, and IST. We first introduce the details of basic linear inverse methods. Then, the framework of MLE methods with constraints is summarized. Finally, we briefly introduce recent further research in developing the performance of tomography, utilizing some symmetry properties of the target. This review provides a primary getting-start in developing quantum tomography, which promotes quantum computer development

Basal Forebrain-Dorsal Hippocampus Cholinergic Circuit Regulates Olfactory Associative Learning

The basal forebrain, an anatomically heterogeneous brain area containing multiple distinct subregions and neuronal populations, innervates many brain regions including the hippocampus (HIP), a key brain region responsible for learning and memory. Although recent studies have revealed that basal forebrain cholinergic neurons (BFCNs) are involved in olfactory associative learning and memory, the potential neural circuit is not clearly dissected yet. Here, using an anterograde monosynaptic tracing strategy, we revealed that BFCNs in different subregions projected to many brain areas, but with significant differentiations. Our rabies virus retrograde tracing results found that the dorsal HIP (dHIP) received heavy projections from the cholinergic neurons in the nucleus of the horizontal limb of the diagonal band (HDB), magnocellular preoptic nucleus (MCPO), and substantia innominate (SI) brain regions, which are known as the HMS complex (HMSc). Functionally, fiber photometry showed that cholinergic neurons in the HMSc were significantly activated in odor-cued go/no-go discrimination tasks. Moreover, specific depletion of the HMSc cholinergic neurons innervating the dHIP significantly decreased the performance accuracies in odor-cued go/no-go discrimination tasks. Taken together, these studies provided detailed information about the projections of different BFCN subpopulations and revealed that the HMSc-dHIP cholinergic circuit plays a crucial role in regulating olfactory associative learning