PHOTONIC ENGINEERING OF ABSORPTION AND EMISSION IN PHOTOVOLTAICS

As modern society advances, the demand for clean and renewable energy resources becomes more and more important. The sun is by far the most abundant source of renewable energy and is indirectly responsible for many other energy resources on earth (e.g. sunlight enables photosynthesis, biofuels, wind, and even carbon-based fuels). A solar cell directly converts the energy of solar illumination into electricity through the photovoltaic effect and is expected to play a crucial role in the future total power generation globally. Our work has focused on photonic approaches to improving the conversion efficiency of solar cells. Toward this goal, we present results describing the use of quantum dot emission to redirect light within a solar cell, as well as the modification of absorption and emission of light from a solar cell using nanostructures and thin films to increase the efficiency to approach (or possibly surpass) the currently understood efficiency limits for traditional devices. The Shockley-Queisser (SQ) limit describes the maximum solar power conversion efficiency achievable for a p-n junction composed of a particular material and is the standard by which new photovoltaic technologies are compared. This limit is based on the principle of detailed balance, which equates the photon flux into a device to the particle flux (photons or electrons) out of that device. Based on this theory, we describe how the efficiency of a photovoltaic cell is altered in the presence of new anti-reflection coatings, nanotexturing (e.g. plasmonic nanoparticle, nanowire), and more advanced photonic structures (e.g. photonic crystals) that are capable of modifying the absorption and emission of photons. Nanostructured solar cells represent a novel class of photovoltaic devices. By careful selection of materials, as well as particle shapes and positions, the device performance can be improved by increasing the optical path length for scattered light, improving the modal distribution of the light within the absorber, and increasing light concentration (or angle restriction). For example, nanowires can yield microscale concentration effects to improve device performance; however, it has been unclear whether or not they can exceed the Shockley-Queisser limit. We show that single-junction nanostructured solar cells have a theoretical maximum efficiency of ∼ 42% under AM 1.5 solar illumination. While this exceeds the efficiency of a non-concentrating planar device, it does not exceed the Shockley-Queisser limit for a planar device with optical concentration. For practical devices, we include the effect of diffuse illumination and find that with the modest optical concentration available from nanostructures (× 1,000), an efficiency of 35.5% is achievable even with 25% diffusive solar radiation. Finally, we discuss how photon emission modification offers an approach for low bandgap materials to achieve higher efficiencies. By incorporating specifically designed photonic structures that restrict the absorption and emission of above bandgap photons, the bandgap of materials can be effectively tuned. Similarly, restriction of the emission angle leads to increased optical concentration. For realistic devices, we consider how both of these effects are affected by non-ideal materials and photonic structures. We find that the photonic crystal bandgap required to achieve maximum efficiency depends critically on the reflectivity of the photonic crystal. We experimentally demonstrated that the semiconductor bandgap of a material need not be an intrinsic property of that material but can be changed through photonic structuring of the surrounding layers. GaAs has a natural bandgap of 1.43 eV; however, we show that optical reflectors can be used to induce photon-recycling effects, which result in a bandgap shift of 0.13 eV. When a p-n junction is created within the GaAs, we find that its electrical properties are also shifted resulting in a 1.71 mV improvement in the open-circuit voltage of the device under 0.6 suns equivalent illumination. These results show that both the optical and electrical properties of a semiconductor can be modified purely by photonic manipulation, which enables a fundamentally new method for designing semiconductor structures and devices. We anticipate that our result will enable a range of optoelectronic devices

Work and Nonwork Support for Employee Development

We develop a model that enhances our understanding of how different supports for development motivate employees to pursue self-development. After collecting data from 418 employees with different backgrounds and using structural equation modeling and bootstrap analysis, we found that work support and nonwork support for development made unique contributions to employee self-development. The relationship between non-work support for development and employee self- development is mediated by self-efficacy and career motivation. The relationship between work support for development and employee self-development is mediated by career motivation. This research expands the support for development from work environment to a broader social environment and clarifies how both work and nonwork supportive environment is positively related to employee self- development. Finally, we discussed practical implications for personnel selection and career development in organizations

Reactivating aberrantly hypermethylated p15 gene in leukemic T cells by a phenylhexyl isothiocyanate mediated inter-active mechanism on DNA and chromatin

<p>Abstract</p> <p>Background</p> <p>We have previously demonstrated that phenylhexyl isothiocyanate (PHI), a synthetic isothiocyanate, inhibits histone deacetylases and remodels chromatins to induce growth arrest in HL-60 myeloid leukemia cells in a concentration-dependent manner.</p> <p>Methods</p> <p>To investigate the effect of PHI, a novel histone deacetylases inhibitor (HDACi), on demethylation and activation of transcription of <it>p15 </it>in acute lymphoid leukemia cell line Molt-4, and to further decipher the potential mechanism of demethylation, DNA sequencing and modified methylation specific PCR (MSP) were used to screen <it>p15</it>-M and <it>p15</it>-U mRNA after Molt-4 cells were treated with PHI, 5-Aza and TSA. DNA methyltransferase 1 (DNMT1), 3A (DNMT3A), 3B (DNMT3B) and <it>p15 </it>mRNA were measured by RT-PCR. P15 protein, acetylated histone H3 and histone H4 were detected by Western Blot.</p> <p>Results</p> <p>The gene <it>p15 </it>in Molt-4 cells was hypermethylated and inactive. Hypermethylation of gene <it>p15 </it>was attenuated and <it>p15 </it>gene was activated de novo after 5 days exposure to PHI in a concentration-dependent manner. DNMT1 and DNMT3B were inhibited by PHI (P < 0.05). Alteration of DNMT3A was not significant at those concentrations. Acetylated histone H3 and histone H4 were accumulated markedly after exposure to PHI.</p> <p>Conclusion</p> <p>PHI could induce both DNA demethylation and acetylated H3 and H4 accumulation in Molt-4 cells. Hypermethylation of gene <it>p15 </it>was reversed and <it>p15 </it>transcription could be reactivated de novo by PHI.</p

MANGO: A Mask Attention Guided One-Stage Scene Text Spotter

Recently end-to-end scene text spotting has become a popular research topic due to its advantages of global optimization and high maintainability in real applications. Most methods attempt to develop various region of interest (RoI) operations to concatenate the detection part and the sequence recognition part into a two-stage text spotting framework. However, in such framework, the recognition part is highly sensitive to the detected results (e.g.), the compactness of text contours). To address this problem, in this paper, we propose a novel Mask AttentioN Guided One-stage text spotting framework named MANGO, in which character sequences can be directly recognized without RoI operation. Concretely, a position-aware mask attention module is developed to generate attention weights on each text instance and its characters. It allows different text instances in an image to be allocated on different feature map channels which are further grouped as a batch of instance features. Finally, a lightweight sequence decoder is applied to generate the character sequences. It is worth noting that MANGO inherently adapts to arbitrary-shaped text spotting and can be trained end-to-end with only coarse position information (e.g.), rectangular bounding box) and text annotations. Experimental results show that the proposed method achieves competitive and even new state-of-the-art performance on both regular and irregular text spotting benchmarks, i.e., ICDAR 2013, ICDAR 2015, Total-Text, and SCUT-CTW1500.Comment: Accepted to AAAI2021. Code is available at https://davar-lab.github.io/publication.html or https://github.com/hikopensource/DAVAR-Lab-OC

E2-AEN: End-to-End Incremental Learning with Adaptively Expandable Network

Expandable networks have demonstrated their advantages in dealing with catastrophic forgetting problem in incremental learning. Considering that different tasks may need different structures, recent methods design dynamic structures adapted to different tasks via sophisticated skills. Their routine is to search expandable structures first and then train on the new tasks, which, however, breaks tasks into multiple training stages, leading to suboptimal or overmuch computational cost. In this paper, we propose an end-to-end trainable adaptively expandable network named E2-AEN, which dynamically generates lightweight structures for new tasks without any accuracy drop in previous tasks. Specifically, the network contains a serial of powerful feature adapters for augmenting the previously learned representations to new tasks, and avoiding task interference. These adapters are controlled via an adaptive gate-based pruning strategy which decides whether the expanded structures can be pruned, making the network structure dynamically changeable according to the complexity of the new tasks. Moreover, we introduce a novel sparsity-activation regularization to encourage the model to learn discriminative features with limited parameters. E2-AEN reduces cost and can be built upon any feed-forward architectures in an end-to-end manner. Extensive experiments on both classification (i.e., CIFAR and VDD) and detection (i.e., COCO, VOC and ICCV2021 SSLAD challenge) benchmarks demonstrate the effectiveness of the proposed method, which achieves the new remarkable results

TRIE++: Towards End-to-End Information Extraction from Visually Rich Documents

Recently, automatically extracting information from visually rich documents (e.g., tickets and resumes) has become a hot and vital research topic due to its widespread commercial value. Most existing methods divide this task into two subparts: the text reading part for obtaining the plain text from the original document images and the information extraction part for extracting key contents. These methods mainly focus on improving the second, while neglecting that the two parts are highly correlated. This paper proposes a unified end-to-end information extraction framework from visually rich documents, where text reading and information extraction can reinforce each other via a well-designed multi-modal context block. Specifically, the text reading part provides multi-modal features like visual, textual and layout features. The multi-modal context block is developed to fuse the generated multi-modal features and even the prior knowledge from the pre-trained language model for better semantic representation. The information extraction part is responsible for generating key contents with the fused context features. The framework can be trained in an end-to-end trainable manner, achieving global optimization. What is more, we define and group visually rich documents into four categories across two dimensions, the layout and text type. For each document category, we provide or recommend the corresponding benchmarks, experimental settings and strong baselines for remedying the problem that this research area lacks the uniform evaluation standard. Extensive experiments on four kinds of benchmarks (from fixed layout to variable layout, from full-structured text to semi-unstructured text) are reported, demonstrating the proposed method's effectiveness. Data, source code and models are available