Preparation and properties of ZnO-based nanostructured films with light trapping eff ects

In this paper, ZnO-based nanostructured films were prepared by hydrothermal method on ZnO seed layers obtained by sol-gel method and AZO transparent conductive glass. X RD, SEM, sheet resistance test, transmittance and haze spectra were used to characterize the structure, morphology, optoelectronic properties and light trapping abilities of the films. The effects of seed layer concentration and hydrothermal growth temperature on the characteristics of ZnO-based nanostructured fi lms were studied, and the prepared fi lms had light trapping eff ects and high total transmittance in the visible light region

DiffEditor: Boosting Accuracy and Flexibility on Diffusion-based Image Editing

Large-scale Text-to-Image (T2I) diffusion models have revolutionized image generation over the last few years. Although owning diverse and high-quality generation capabilities, translating these abilities to fine-grained image editing remains challenging. In this paper, we propose DiffEditor to rectify two weaknesses in existing diffusion-based image editing: (1) in complex scenarios, editing results often lack editing accuracy and exhibit unexpected artifacts; (2) lack of flexibility to harmonize editing operations, e.g., imagine new content. In our solution, we introduce image prompts in fine-grained image editing, cooperating with the text prompt to better describe the editing content. To increase the flexibility while maintaining content consistency, we locally combine stochastic differential equation (SDE) into the ordinary differential equation (ODE) sampling. In addition, we incorporate regional score-based gradient guidance and a time travel strategy into the diffusion sampling, further improving the editing quality. Extensive experiments demonstrate that our method can efficiently achieve state-of-the-art performance on various fine-grained image editing tasks, including editing within a single image (e.g., object moving, resizing, and content dragging) and across images (e.g., appearance replacing and object pasting). Our source code is released at https://github.com/MC-E/DragonDiffusion

DragonDiffusion: Enabling Drag-style Manipulation on Diffusion Models

Despite the ability of existing large-scale text-to-image (T2I) models to generate high-quality images from detailed textual descriptions, they often lack the ability to precisely edit the generated or real images. In this paper, we propose a novel image editing method, DragonDiffusion, enabling Drag-style manipulation on Diffusion models. Specifically, we construct classifier guidance based on the strong correspondence of intermediate features in the diffusion model. It can transform the editing signals into gradients via feature correspondence loss to modify the intermediate representation of the diffusion model. Based on this guidance strategy, we also build a multi-scale guidance to consider both semantic and geometric alignment. Moreover, a cross-branch self-attention is added to maintain the consistency between the original image and the editing result. Our method, through an efficient design, achieves various editing modes for the generated or real images, such as object moving, object resizing, object appearance replacement, and content dragging. It is worth noting that all editing and content preservation signals come from the image itself, and the model does not require fine-tuning or additional modules. Our source code will be available at https://github.com/MC-E/DragonDiffusion

The Revelation of Foreign Carbon Emission Policies for Developing China Low-Carbon Economy

In present, developing the low-carbon economy is common recognized by the world. In order to achieve the aim of low-carbon economy, to reduce the carbon dioxide emissions is one of the most important measures. Nowadays, because of the large population, high energy intensity and the unreasonable energy structure based on fossil fuel, our country has faced an enormous pressure during the reducing carbon emissions process. Based on the current international applying carbon emission reduction proposals, we discussed the developed countries carbon emission reduction policies in detailed which includes the United States, Britain and Japan and etc. and in the meantime. Based on the above overseas developed countries experiences, the paper analyses  our country’s present situation of carbon emission reduction, finds the existing problems and put forward relevant recommendations. In brief, the research will provide some reference to build a low-carbon economy society of our country.Keywords:  low-carbon economy; CO2 emission; home and abroad carbon emission reduction policy; questions and measures Résumé:  A l'heure actuelle, le développement de l'économie à bas-carbone est généralement reconnu par le monde. Afin d'atteindre l'objectif de l'économie à bas-carbone, la réduction des émissions de dioxyde de carbone est l'une des mesures les plus importantes. De nos jours, en raison d'une population nombreuse, d'une intensité énergétique élevée et d'une structure énergétique déraisonnable basée sur les combustibles fossiles, notre pays a fait face à une pression énorme au cours du processus de réduction des émissions de carbone. Sur la base des propositions internationales de la réduction de l'émission de carbone actuellement appliquées, nous avons étudié les politiques de réduction des émissions de carboneles des pays développés en détail, qui comprend les États-Unis, la Grande-Bretagne, le Japon etc, et en même temps,  basé sur les expériences de ces pays développés ci-dessus, le document analyse la situation actuelle de notre pays en matière de la réduction des émissions de carbone, trouve des problèmes existants et propose des recommandations pertinentes. En bref, cette recherche fournira des références à la construction d'une société de l'économie bas-carbone dans notre pays.Mots-Clés:  économie à bas-carbone; émissions de CO2; politiques de la réduction de l'émission de carbone domestiques et étrangères; problèmes et mesure

The role of EGFR mutation as a prognostic factor in survival after diagnosis of brain metastasis in non-small cell lung cancer: A systematic review and meta-analysis

Abstract Background The brain is a common site for metastasis in non-small-cell lung cancer (NSCLC). This study was designed to evaluate the relationship between the mutational of the epidermal growth factor receptor (EGFR) and overall survival (OS) in NSCLC patients with brain metastases. Methods Searches were performed in PubMed, EmBase, and the Cochrane Library to identify studies evaluating the association of EGFR mutation with OS in NSCLC patients through September 2017. Results 4373 NSCLC patients with brain metastases in 18 studies were involved. Mutated EGFR associated with significantly improved OS compared with wild type. Subgroup analyses suggested that this relationship persisted in studies conducted in Eastern, with retrospective design, with sample size ≥500, mean age of patients ≥65.0 years, percentage male < 50.0%, percentage of patients receiving tyrosine kinase inhibitor ≥30.0%. Finally, although significant publication bias was observed using the Egger test, the results were not changed after adjustment using the trim and fill method. Conclusions This meta-analysis suggests that EGFR mutation is an important predictive factor linked to improved OS for NSCLC patients with brain metastases. It can serve as a useful index in the prognostic assessment of NSCLC patients with brain metastases

Bi-level Physics-Informed Neural Networks for PDE Constrained Optimization using Broyden's Hypergradients

Deep learning based approaches like Physics-informed neural networks (PINNs) and DeepONets have shown promise on solving PDE constrained optimization (PDECO) problems. However, existing methods are insufficient to handle those PDE constraints that have a complicated or nonlinear dependency on optimization targets. In this paper, we present a novel bi-level optimization framework to resolve the challenge by decoupling the optimization of the targets and constraints. For the inner loop optimization, we adopt PINNs to solve the PDE constraints only. For the outer loop, we design a novel method by using Broyden's method based on the Implicit Function Theorem (IFT), which is efficient and accurate for approximating hypergradients. We further present theoretical explanations and error analysis of the hypergradients computation. Extensive experiments on multiple large-scale and nonlinear PDE constrained optimization problems demonstrate that our method achieves state-of-the-art results compared with strong baselines