Health benefits of vehicle electrification through air pollution in Shanghai, China.

Vehicle electrification has been recognized for its potential to reduce emissions of air pollutants and greenhouse gases in China. Several studies have estimated how national-level policies of electric vehicle (EV) adoption might bring very large environmental and public health benefits from improved air quality to China. However, large-scale adoption is very costly, some regions derive more benefits from large-scale EV adoption than others, and the benefits of replacing internal combustion engines in specific cities are less known. Therefore, it is important for policymakers to design incentives based on regional characteristics - especially for megacities like Shanghai - which typically suffer from worse air quality and where a larger population is exposed to emissions from vehicles. Over the past five years, Shanghai has offered substantial personal subsidies for passenger EVs to accelerate its electrification efforts. Still, it remains uncertain whether EV benefits justify the strength of incentives. The purpose of our study is to evaluate the health and climate benefits of replacing light-duty gasoline vehicles (ICEVs) with battery EVs in the city of Shanghai. We assess health impacts due to ICEV emissions of primary fine particulate matter, NOx, and volatile organic compounds, and to powerplant emissions of NOx and SO2 due to EV charging. We incorporate climate benefits from reduced greenhouse gas emissions based on existing research. We find that the benefit of replacing the average ICEV with an EV in Shanghai is US$6400 (2400-14,700), with health impacts of EVs about 20 times lower than the average ICEV. Larger benefits ensue if older ICEVs are replaced, but replacing newer China ICEVs also achieves positive health benefits. As Shanghai plans to stop providing personal subsidies for EV purchases in 2024, our results show that EVs achieve public health and climate benefits and can help inform policymaking strategies in Shanghai and other megacities.

Polarization-independent plasmon-induced transparency and slow light effects in a fully continuous symmetric cross-shaped monolayer graphene structure.

A fully continuous geometric center symmetric cross-shaped graphene structure is proposed. Each cross-shaped graphene unit cell is composed of a central graphene region and four completely symmetric graphene chips, where each graphene chip acts as both bright and dark modes simultaneously, while the central graphene region always acts as the bright mode. Through destructive interference, the structure can realize the single plasmon-induced transparency (PIT) phenomenon, where the optical responses are independent of the polarization direction of the linearly polarized light due to the symmetry of the structure. Combining numerical simulations with coupled mode theory (CMT) calculations, the modulation of the Fermi energy of graphene to the optical spectra is investigated. The results show that the spectra are blue shifted as the Fermi energy increases, and the absorption of the two absorption peaks is basically equal (48.7%) when the Fermi energy increases to 0.667 eV. Theoretical calculations show that the slow light performance of the designed structure enhances with the increase of Fermi energy, where the maximum group index is high up to 424.73. Furthermore, it is worth noting that the electrode can be made very small due to its fully continuous structure. This work provides guidance in terms of terahertz modulators, tunable absorbers, and slow light devices.

Cannabidiol Produces Distinct U-Shaped Dose-Response Effects on Cocaine-Induced Conditioned Place Preference and Associated Recruitment of Prelimbic Neurons in Male Rats.

BACKGROUND: Cannabidiol (CBD) has received attention for the treatment of substance use disorders. In preclinical models of relapse, CBD attenuates drug seeking across several drugs of abuse, including cocaine. However, in these models CBD has not been consistently effective. This inconsistency in CBD effects may be related to presently insufficient information on the full spectrum of CBD dose effects on drug-related behaviors. METHODS: We address this issue by establishing a full dose-response profile of CBD's actions using expression of cocaine-induced conditioned place preference as a model for drug-motivated behavior in male rats and by concurrently identifying dose-dependent effects of CBD on underlying neuronal activation and distinct neuronal phenotypes showing dose-dependent activation changes. Additionally, we established CBD levels in plasma and brain samples. RESULTS: CBD produced linear increases in CBD brain/plasma concentrations but suppressed conditioned place preference in a distinct U-shaped manner. In parallel with its behavioral effects, CBD produced U-shaped suppressant effects on neuronal activation in the prelimbic but not infralimbic cortex or nucleus accumbens core and shell. RNAscope in situ hybridization identified suppression of glutamatergic and GABAergic (gamma-aminobutyric acidergic) signaling in the prelimbic cortex as a possible cellular mechanism for the attenuation of cocaine-induced conditioned place preference by CBD. CONCLUSIONS: The findings extend previous evidence on the potential of CBD in preventing drug-motivated behavior. However, CBD's dose-response profile may have important dosing implications for future clinical applications and may contribute to the understanding of discrepant CBD effects on drug seeking reported in the literature.

Clutter Suppression Method for Off-Grid Effects Mitigation in Airborne Passive Radars with Contaminated Reference Signals.

For an airborne passive radar with contaminated reference signals, the clutter caused by multipath (MP) signals involved in the reference channel (MP clutter) corrupts the covariance estimation in space-time adaptive processing (STAP). In order to overcome the severe STAP performance degradation caused by impure reference signals and off-grid effects, a novel MP clutter suppression method based on local search is proposed for airborne passive radar. In the proposed method, the global dictionary is constructed based on the sparse measurement model of MP clutter, and the global atoms that are most relevant to the residual are selected. Then, the local dictionary is designed iteratively, and local searches are performed to match real MP clutter points. Finally, the off-grid effects are mitigated, and the MP clutter is suppressed from all matched atoms. A range of simulations is conducted in order to demonstrate the effectiveness of the proposed method.

Plasmon-Enhanced Ultraviolet Photoluminescence from the Graphene/GaN Nanofilm.

The response of graphene surface plasmon (SP) in ultraviolet (UV) wavelength region and its functional applications on the short wavelength of graphene/semiconductorare both fascinating research areas. Herein, a hybrid structure of graphene/GaN nanofilm was designed and fabricated to investigate the photoluminescence (PL) performance and the coupling dynamics. It is demonstrated that the resonant coupling between graphene SPs and GaN exciton emission is responsible for the substantially enhanced PL from the structure of graphene/GaN nanofilm. The underlying mechanism of the improved PL performance was proposed based on theoretical simulation and experimental characterization. The results are helpful to design new types of optic and photoelectronic devices based on SP coupling in graphene/semiconductor hybrid structures.

Fast analysis method for stochastic optical reconstruction microscopy using multiple measurement vector model sparse Bayesian learning.

Compressed sensing (CS) can be used in fluorescence microscopy to improve the temporal resolution of stochastic optical reconstruction microscopy (STORM). Currently, most algorithms used in CS-STORM belong to the single measurement vector (SMV) model, where each super-resolution image is recovered individually from a raw frame, thereby prolonging the computational time. Here, we apply the multiple measurement vector (MMV) model CS algorithm to STORM, wherein all raw images are converted into a matrix and recovered by solving the simultaneous sparse recovery problem. We use the MMV model-based sparse Bayesian learning (SBL) algorithm to reconstitute the raw images of STORM, then compare its imaging resolution and run time with the SMV model CS algorithms. The simulated and experimentally recovered super-resolution images prove that the resolution of MMV model SBL (M-SBL) is comparable with the SMV model algorithm, while the run time is far less and decreases from several hours to several minutes. The high resolution and shorter reconstitution time make M-SBL a promising real-time image reconstruction method for CS-STORM.

Super-resolution fluorescence blinking imaging using modified Fourier ptychography.

In this paper, we propose a new super-resolution imaging technique based on fluorescence blinking (SRFB). Contrary to structured illumination microscopy (SIM), SRFB considers the time-varying fluorescence distribution under a suitable density as the varying illuminated speckle pattern, and therefore, external speckle patterns or diffusers are not required. With several images recorded at different times, a super-resolution image can be obtained through an iterative algorithm modified from Fourier ptychography. Recorded image sequences in a microscopy imaging experiment based on photo switching or fluorescence blinking effects, such as STORM and SOFI, can be handled with SRFB and used to recover a super-resolution image. The simulation and experimental results confirm that the SRFB scheme can surpass the diffraction limit by a factor greater than two.

3D localization of high particle density images using sparse recovery.

If particles are too close in space, their images may be overlapped when they are observed with microscopes because of diffraction limitation, which makes them difficult to be distinguished or localized. This limitation also affects the efficiency of localization of those single-particle-localization microcopies, such as stochastic optical reconstruction microscopy (STORM) and (fluorescence) photoactivated localization microscopy [(F)PALM]. In this work, we developed a 3D sparse recovery (3D-SR) method, with the aim of localizing particles with high density in three dimensions, which cannot be resolved using original STROM or (F)PALM. A cylindrical lens was introduced to a traditional wide-field microscope in order to form the 3D point spread function for 3D-SR. The performance of the 3D-SR method was evaluated using simulation. Simulated results demonstrated that, even for particle densities as high as 4 µm-2 on a transversal projection, particles could still be localized with high accuracy. The standard deviations were found to be 25.59 nm along the transverse direction and 50.42 nm along the axial direction. Compared with the existing 3D localization methods used in high particle density cases, such as 3D-DAOSTORM, 3D-SR allows a higher activated fluorophore density per frame.