Surface relief grating near-eye display waveguide design

A near-eye display device (NED) is a visual optical system that places a miniature display in front of the human eye to provide an immersive viewing experience. NEDs have been playing an irreplaceable role in both early military flight applications and today's civil and entertainment applications. In this paper, we propose an easy-to-machine design of a near-eye display based on surface relief grating waveguides, taking into account the experience of previous designs of near-eye displays, the superior performance of the design, and the accuracy level of existing grating processing. The design is designed to meet the requirements of large field of view and large outgoing pupil extension as much as possible. The design is insensitive to the incident angle and achieves a full-field field-of-view angle of 40{\deg}, an angular uniformity error of 20% for diffraction efficiency, and an average diffraction efficiency of 80% for the full field of view. Based on the design, the overall simulation of the optical path of the NED device is completed, and the illumination uniformity of the outgoing pupil expansion of the device is analyzed through simulation

Surface relief grating near-eye display waveguide design

A near-eye display device (NED) is a visual optical system that places a miniature display in front of the human eye to provide an immersive viewing experience. NEDs have been playing an irreplaceable role in both early military flight applications and today's civil and entertainment applications. In this paper, we propose an easy-to-machine design of a near-eye display based on surface relief grating waveguides, taking into account the experience of previous designs of near-eye displays, the superior performance of the design, and the accuracy level of existing grating processing. The design is designed to meet the requirements of large field of view and large outgoing pupil extension as much as possible. The design is insensitive to the incident angle and achieves a full-field field-of-view angle of 40°, an angular uniformity error of 20% for diffraction efficiency, and an average diffraction efficiency of 80% for the full field of view. Based on the design, the overall simulation of the optical path of the NED device is completed, and the illumination uniformity of the outgoing pupil expansion of the device is analyzed through simulation

Joint and Individual Component Regression

Abstract–Multi-group data, which include the same set of variables on separate groups of samples, are commonly seen in practice. Such data structure consists of data from multiple groups and can be challenging to analyze due to data heterogeneity. We propose a novel Joint and Individual Component Regression (JICO) model to analyze multi-group data. Our proposed model decomposes the response into shared and group-specific components, which are driven by low-rank approximations of joint and individual structures from the predictors respectively. The joint structure has the same regression coefficients across multiple groups, whereas individual structures have group-specific regression coefficients. We formulate this framework under the representation of latent components and propose an iterative algorithm to solve for the joint and individual scores. We utilize the Continuum Regression (CR) to estimate the latent scores, which provides a unified framework that covers the Ordinary Least Squares (OLS), the Partial Least Squares (PLS), and the Principal Component Regression (PCR) as its special cases. We show that JICO attains a good balance between global and group-specific models and remains flexible due to the usage of CR. We conduct simulation studies and analysis of an Alzheimer’s disease dataset to further demonstrate the effectiveness of JICO. R package of JICO is available online at https://CRAN.R-project.org/package=JICO.</p

Asymmetric Total Syntheses of <i>ent</i>-Stachybotrin C and Its Congener

The asymmetric total syntheses of ent-stachybotrin C and its congener have been accomplished through a convergent approach in the longest linear sequence of 12 steps from commercially available materials, respectively. Noteworthy transformation of the synthesis involved a cascade Knoevenagel condensation/Hantzsch ester reduction/epoxide ring-opening/transetherification to construct the core pyran ring with two adjacent stereocenters

Data_Sheet_1_A deep learning framework for identifying Alzheimer's disease using fMRI-based brain network.ZIP

BackgroundThe convolutional neural network (CNN) is a mainstream deep learning (DL) algorithm, and it has gained great fame in solving problems from clinical examination and diagnosis, such as Alzheimer's disease (AD). AD is a degenerative disease difficult to clinical diagnosis due to its unclear underlying pathological mechanism. Previous studies have primarily focused on investigating structural abnormalities in the brain's functional networks related to the AD or proposing different deep learning approaches for AD classification.ObjectiveThe aim of this study is to leverage the advantages of combining brain topological features extracted from functional network exploration and deep features extracted by the CNN. We establish a novel fMRI-based classification framework that utilizes Resting-state functional magnetic resonance imaging (rs-fMRI) with the phase synchronization index (PSI) and 2D-CNN to detect abnormal brain functional connectivity in AD.MethodsFirst, PSI was applied to construct the brain network by region of interest (ROI) signals obtained from data preprocessing stage, and eight topological features were extracted. Subsequently, the 2D-CNN was applied to the PSI matrix to explore the local and global patterns of the network connectivity by extracting eight deep features from the 2D-CNN convolutional layer.ResultsFinally, classification analysis was carried out on the combined PSI and 2D-CNN methods to recognize AD by using support vector machine (SVM) with 5-fold cross-validation strategy. It was found that the classification accuracy of combined method achieved 98.869%.ConclusionThese findings show that our framework can adaptively combine the best brain network features to explore network synchronization, functional connections, and characterize brain functional abnormalities, which could effectively detect AD anomalies by the extracted features that may provide new insights into exploring the underlying pathogenesis of AD.</p

Additional file 1 of Study on the characteristics of genetic diversity of different populations of Guizhou endemic plant Rhododendron pudingense based on microsatellite markers

Supplementary Material

A New Type of Quantum Fertilizer (Silicon Quantum Dots) Promotes the Growth and Enhances the Antioxidant Defense System in Rice Seedlings by Reprogramming the Nitrogen and Carbon Metabolism

To promote the growth and yield of crops, it is necessary to develop an effective silicon fertilizer. Herein, a new type of 2 nm silicon quantum dot (SiQD) was developed, and the phenotypic, biochemical, and metabolic responses of rice seedlings treated with SiQDs were investigated. The results indicated that the foliar application of SiQDs could significantly improve the growth of rice seedlings by increasing the uptake of nutrient elements and activating the antioxidative defense system. Furthermore, metabolomics revealed that the supply of SiQDs could significantly up-regulate several antioxidative metabolites (oxalic acid, maleic acid, glycine, lysine, and proline) by reprogramming the nitrogen- and carbon-related biological pathways. The findings provide a new strategy for developing an effective and promising quantum fertilizer in agriculture

Data_Sheet_1_A novel framework of MOPSO-GDM in recognition of Alzheimer's EEG-based functional network.PDF

BackgroundMost patients with Alzheimer's disease (AD) have an insidious onset and frequently atypical clinical symptoms, which are considered a normal consequence of aging, making it difficult to diagnose AD medically. But then again, accurate diagnosis is critical to prevent degeneration and provide early treatment for AD patients.ObjectiveThis study aims to establish a novel EEG-based classification framework with deep learning methods for AD recognition.MethodsFirst, considering the network interactions in different frequency bands (δ, θ, α, β, and γ), multiplex networks are reconstructed by the phase synchronization index (PSI) method, and fourteen topology features are extracted subsequently, forming a high-dimensional feature vector. However, in feature combination, not all features can provide effective information for recognition. Moreover, combining features by manual selection is time-consuming and laborious. Thus, a feature selection optimization algorithm called MOPSO-GDM was proposed by combining multi-objective particle swarm optimization (MOPSO) algorithm with Gaussian differential mutation (GDM) algorithm. In addition to considering the classification error rates of support vector machine, naive bayes, and discriminant analysis classifiers, our algorithm also considers distance measure as an optimization objective.ResultsFinally, this method proposed achieves an excellent classification error rate of 0.0531 (5.31%) with the feature vector size of 8, by a ten-fold cross-validation strategy.ConclusionThese findings show that our framework can adaptively combine the best brain network features to explore network synchronization, functional interactions, and characterize brain functional abnormalities, which can improve the recognition efficiency of diseases. While improving the classification accuracy of application algorithms, we aim to expand our understanding of the brain function of patients with neurological disorders through the analysis of brain networks.</p

Visible-Light-Driven Zinc Oxide Quantum Dots for the Management of Bacterial Fruit Blotch Disease and the Improvement of Melon Seedlings Growth

Bacterial fruit blotch is one of the most destructing diseases of melon producing-regions. Here, zinc oxide quantum dots (ZnO QDs) were synthesized, and their antibacterial activity against Acidovorax citrulli was investigated. The results indicated that the obtained ZnO QDs displayed 5.7-fold higher antibacterial activity than a commercial Zn-based bactericide (zinc thiazole). Interestingly, the antibacterial activity of ZnO QDs irradiated with light was 1.8 times higher than that of the dark-treated group. It was because ZnO QDs could induce the generation of hydroxyl radicals and then up-regulate the expression of oxidative stress-related genes, finally leading to the loss of cell membrane integrity. A pot experiment demonstrated that foliar application of ZnO QDs significantly reduced the bacterial fruit blotch disease incidence (32.0%). Furthermore, the supply of ZnO QDs could improve the growth of infected melon seedlings by activating the antioxidant defense system. This work provides a promising light-activated quantum-bactericide for the management of pathogenic bacterial infections in melon crop protection

Effect of Exposed Facets and Oxidation State of CeO₂ Nanoparticles on CO₂ Adsorption and Desorption

CeO2 nanoparticles exhibit potential as solid adsorbents for carbon dioxide (CO2) capture and storage (CCS), offering precise control over various facets and enhancing their efficiency. This study investigated the adsorption and desorption behaviors of two types of CeO2 nanoparticles: cubic CeO2 with primarily {001} facets and polyhedral CeO2 with mainly {111} facets. The results showed that despite polyhedral CeO2’s lower quantity, it demonstrated successful adsorption–desorption cycles in both oxidized and reduced states. However, reduced CeO2–x exhibited a higher adsorption capacity but displayed irreversible adsorption–desorption cycles. Reversible adsorption occurred through weak bond formation with CO2, while cubic CeO2 with a high oxygen vacancy concentration exhibited irreversible adsorption due to strong bond formation. These insights contribute significantly to understanding CeO2 nanoparticle characteristics and their impact on the CO2 adsorption and desorption processes, aiding in the development of advanced CCS techniques