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"Zhang, Jingyuan"
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A study on synergistic development of urban digitalization and greening under the diffusion of low-carbon pilot policies
Low-carbon pilot policies, as comprehensive environmental strategies, provide a valuable perspective for examining the integrated development of urban digitalization and environmental sustainability. However, from the perspective of an integrated “pilot-diffusion” approach, the varying effects of pilot policies on the synergistic development of urban digitalization and greening, under different diffusion modes, are still not well clear. This study uses panel data from 278 Chinese cities between 2005 and 2022 and employs a double machine learning model to effectively control for high-dimensional confounders, reducing biases commonly associated with traditional methodologies. The results indicate that low-carbon city pilot policies effectively promote the synergistic development of urban digitalization and greening, with hierarchical diffusion demonstrating stronger policy effects compared to horizontal diffusion. Additionally, the upgrading of industrial structures and green technological innovation were found to regulate the effects of the policies, and the promotional effect of low-carbon pilot policies on the urban digitalization and greening development development of cities exhibits heterogeneity across different regions and city types. Through the analysis of policy diffusion patterns, this paper clarifies how different diffusion mechanisms influence policy outcomes, providing targeted policy recommendations and theoretical insights to support the coordinated progress of urban digitalization and greening. To enhance future low-carbon pilot policies, this paper emphasizes the need to adapt policy designs to specific diffusion modes and regional contexts, encouraging targeted and differentiated strategies to foster sustainable urban development.
Journal Article
Causal role of immune cells in psoriasis: a Mendelian randomization analysis
A growing body of evidence has shown that immune cells are linked to psoriasis. It is, however, still unclear if these associations reflect a relationship of cause and effect.
We employed a two-sample Mendelian randomization (MR)-based study to elucidate the probable causative connection between immune cells and psoriasis.
Summary information for psoriasis (Ncase = 5,427, Ncontrol = 479,171) was obtained from the European Bioinformatics Institute. Summarized statistical information on 731 immune cell features, including morphological parameters (MP; n = 32), relative cell number (n = 192), median fluorescence intensity (MFI) of surface antigens (n = 389), and absolute cell number (n = 118), was obtained from the genome-wide association studies (GWAS) catalog. The research consisted of forward MR analysis, in which immune cell traits were used as the exposure factor, and psoriasis was the outcome, as well as reverse MR analysis, in which psoriasis was used as the exposure factor, and immune cell traits were the outcome. We ran numerous sensitivity analyses to ascertain the study results for robustness, heterogeneity, and potential multiple-biological effects.
This research determined a probable causative connection between immune cells and psoriasis. In particular, we identified 36 distinct types of immune cells that are potentially causally linked to psoriasis.
Our findings indicate strong causal correlations between 36 immunological phenotypes and psoriasis, thus, directing future clinical trials.
Journal Article
Inverse-designed diamond photonics
Diamond hosts optically active color centers with great promise in quantum computation, networking, and sensing. Realization of such applications is contingent upon the integration of color centers into photonic circuits. However, current diamond quantum optics experiments are restricted to single devices and few quantum emitters because fabrication constraints limit device functionalities, thus precluding color center integrated photonic circuits. In this work, we utilize inverse design methods to overcome constraints of cutting-edge diamond nanofabrication methods and fabricate compact and robust diamond devices with unique specifications. Our design method leverages advanced optimization techniques to search the full parameter space for fabricable device designs. We experimentally demonstrate inverse-designed photonic free-space interfaces as well as their scalable integration with two vastly different devices: classical photonic crystal cavities and inverse-designed waveguide-splitters. The multi-device integration capability and performance of our inverse-designed diamond platform represents a critical advancement toward integrated diamond quantum optical circuits.
Current diamond quantum optics experiments are restricted to single devices and few quantum emitters due to fabrication constraints. Here, the authors utilize inverse design to overcome constraints of diamond nanofabrication methods and fabricate compact and robust diamond devices with unique specifications.
Journal Article
5D Nonlinear Dynamic Evolutionary System in Real Estate Market
In this paper, we propose a new predator-prey nonlinear dynamic evolutionary model of real estate enterprises considering the large, medium, and small real estate enterprises for three different prey teams. A 5D predator-prey nonlinear dynamic evolutionary system in the real estate market is established, where the large, medium, and small real estate enterprises correspond to three differential equations, provincial and local officials, and the central government correspond to the other two differential equations. Nonlinear dynamic analysis on a 5D predator-prey evolutionary system in the real estate market, containing the analysis of equilibrium points and stabilities, is made. The corresponding discrete system is simulated, and the simulation results about Lyapunov spectrum, bifurcation diagram, sequence diagram, and phase diagram are given. Compared with the work of Yang et al. in which all real estate enterprises corresponded to one differential equation, in our proposed model, the large, medium, and small real estate enterprises correspond to three differential equations which is more accordant with the specific circumstance of real estate companies.
Journal Article
Revealing multiple classes of stable quantum emitters in hexagonal boron nitride with correlated optical and electron microscopy
Defects in hexagonal boron nitride (hBN) exhibit high-brightness, room-temperature quantum emission, but their large spectral variability and unknown local structure challenge their technological utility. Here, we directly correlate hBN quantum emission with local strain using a combination of photoluminescence (PL), cathodoluminescence (CL) and nanobeam electron diffraction. Across 40 emitters, we observe zero phonon lines (ZPLs) in PL and CL ranging from 540 to 720 nm. CL mapping reveals that multiple defects and distinct defect species located within an optically diffraction-limited region can each contribute to the observed PL spectra. Local strain maps indicate that strain is not required to activate the emitters and is not solely responsible for the observed ZPL spectral range. Instead, at least four distinct defect classes are responsible for the observed emission range, and all four classes are stable upon both optical and electron illumination. Our results provide a foundation for future atomic-scale optical characterization of colour centres.
Defects in hexagonal boron nitride exhibit room-temperature quantum emission, but their unknown structural origin challenges their technological utility. A combination of optical and electron microscopy helps to distinguish at least four classes of defects and correlate them with local strain.
Journal Article
Highly efficient light-emitting diodes via self-assembled InP quantum dots
Heavy-metal-free quantum dot light-emitting diodes (QLEDs) face commercialization challenges due to low efficiency and poor stability. Spin-coated quantum dot films often create charge leakage areas, limiting device performance. Here, we develop an evaporative-driven self-assembly strategy that enables the preparation of uniform and dense InP-based quantum dot films. During device operation, these films effectively suppress performance degradation caused by charge leakage. QLEDs with uniform and dense InP-based quantum dot films achieve high external quantum efficiency (26.6%) and luminance (1.4 × 10
5
cd m
−2
), along with considerable stability (extrapolated
T
50
lifetime of 4026 hours at 1000 cd m
−2
). For a 2 × 3 cm
2
InP-based device, the peak external quantum efficiency reaches 21.1%. By combining high-performance QLEDs with lithography technology, we fabricate miniaturized QLEDs with a minimum pixel size of 3 μm, achieving a resolution as high as 5080 pixels per inch and a peak external quantum efficiency of 22.6%.
Li et al. report evaporative-driven self-assembly of dense and uniform InP quantum dot films for red LEDs, enabling a peak efficiency of 26.6%. Three-ended closed micropillar templates are employed to anchor the liquid, manipulate three-phase contact line, and assemble the quantum dot films.
Journal Article
Dynamic hydrogen-bonding enables high-performance and mechanically robust organic solar cells processed with non-halogenated solvent
Developing active-layer systems with both high performance and mechanical robustness is a crucial step towards achieving future commercialization of flexible and stretchable organic solar cells (OSCs). Herein, we design and synthesize a series of acceptors BTA-C6, BTA-E3, BTA-E6, and BTA-E9, featuring the side chains of hexyl, and 3, 6, and 9 carbon-chain with ethyl ester end groups respectively. Benefiting from suitable phase separation and vertical phase distribution, the PM6:BTA-E3-based OSCs processed by
o
-xylene exhibit lower energy loss and improved charge transport characteristic and achieve a power conversion efficiency of 19.92% (certified 19.57%), which stands as the highest recorded value in binary OSCs processed by green solvents. Moreover, due to the additional hydrogen-bonding provided by ethyl ester side chain, the PM6:BTA-E3-based active-layer systems achieve enhanced stretchability and thermal stability. Our work reveals the significance of dynamic hydrogen-bonding in improving the photovoltaic performance, mechanical robustness, and morphological stability of OSCs.
Developing high-performance and mechanically robust active-layer systems is crucial to commercializing flexible organic solar cells. Here, authors design small molecule acceptors with ethyl ester side chains and achieve certified efficiency of over 19% for mechanically robust devices.
Journal Article
Deep learning-based multi-model approach on electron microscopy image of renal biopsy classification
Background
Electron microscopy is important in the diagnosis of renal disease. For immune-mediated renal disease diagnosis, whether the electron-dense granule is present in the electron microscope image is of vital importance. Deep learning methods perform well at feature extraction and assessment of histologic images. However, few studies on deep learning methods for electron microscopy images of renal biopsy have been published. This study aimed to develop a deep learning-based multi-model to automatically detect whether the electron-dense granule is present in the TEM image of renal biopsy, and then help diagnose immune-mediated renal disease.
Methods
Three deep learning models are trained to classify whether the electron-dense granule is present using 910 electron microscopy images of renal biopsies. We proposed two novel methods to improve the model accuracy. One model uses the pre-trained ResNet convolutional layers for feature extraction with transfer learning which was firstly improved with skip architecture, then uses Support Vector Machine as the classifier. We developed a multi-model to combine the traditional ResNet model with the improved one to further improve the accuracy.
Results
Deep learning-based multi-model has the highest model accuracy, and the average accuracy is about 88%. The improved ReseNet + SVM model performance is much better than the traditional ResNet model. The average accuracy of the improved ResNet + SVM model is 83%, while the traditional ResNet model accuracy is only 58%.
Conclusions
This study presents the first models for electron microscopy image classification of Renal Biopsy. Identifying whether the electron-dense granule is present plays an important role in the diagnosis of immune complex nephropathy. This study made it possible for Artificial Intelligence models assist to analyze complex electron microscopy images for disease diagnosis.
Journal Article