Efficiency Droop in III-nitride LEDs

To dominate the illumination market, applications of high-power, group III-nitride light-emitting diodes (LEDs) with lower cost and higher efficiency at high injection current density must prevail. In this chapter, three possible origins of efficiency droop (including electron leakage, poor hole injection, and delocalization of carriers) in III-nitride LEDs are systematically summarized. To seek a more comprehensive understanding of the efficiency droop, experimental results based on commercialized LEDs are obtained to explain the physical mechanisms. Proposals for droop mitigation, such as (1) improving hole injection, and (2) increasing effective optical volume or reducing carrier density in the active region, are introduced. Finally, a simple expression for the effects of V-shaped pits on the droop is demonstrated

RCRN: Real-world Character Image Restoration Network via Skeleton Extraction

Constructing high-quality character image datasets is challenging because real-world images are often affected by image degradation. There are limitations when applying current image restoration methods to such real-world character images, since (i) the categories of noise in character images are different from those in general images; (ii) real-world character images usually contain more complex image degradation, e.g., mixed noise at different noise levels. To address these problems, we propose a real-world character restoration network (RCRN) to effectively restore degraded character images, where character skeleton information and scale-ensemble feature extraction are utilized to obtain better restoration performance. The proposed method consists of a skeleton extractor (SENet) and a character image restorer (CiRNet). SENet aims to preserve the structural consistency of the character and normalize complex noise. Then, CiRNet reconstructs clean images from degraded character images and their skeletons. Due to the lack of benchmarks for real-world character image restoration, we constructed a dataset containing 1,606 character images with real-world degradation to evaluate the validity of the proposed method. The experimental results demonstrate that RCRN outperforms state-of-the-art methods quantitatively and qualitatively.Comment: Accepted to ACM MM 202

MRP2Rec: Exploring Multiple-Step Relation Path Semantics for Knowledge Graph-Based Recommendations

Knowledge graphs (KGs) have been proven to be effective for improving the performance of recommender systems. KGs can store rich side information and relieve the data sparsity problem. There are many linked attributes between entity pairs (e.g., items and users) in KGs, which can be called multiple-step relation paths. Existing methods do not sufficiently exploit the information encoded in KGs. In this paper, we propose MRP2Rec to explore various semantic relations in multiple-step relation paths to improve recommendation performance. The knowledge representation learning approach is used in our method to learn and represent multiple-step relation paths, and they are further utilized to generate prediction lists by inner products in top-K recommendations. Experiments on two real-world datasets demonstrate that our model achieves higher performance compared with many state-of-the-art baselines

CharFormer: A Glyph Fusion based Attentive Framework for High-precision Character Image Denoising

Degraded images commonly exist in the general sources of character images, leading to unsatisfactory character recognition results. Existing methods have dedicated efforts to restoring degraded character images. However, the denoising results obtained by these methods do not appear to improve character recognition performance. This is mainly because current methods only focus on pixel-level information and ignore critical features of a character, such as its glyph, resulting in character-glyph damage during the denoising process. In this paper, we introduce a novel generic framework based on glyph fusion and attention mechanisms, i.e., CharFormer, for precisely recovering character images without changing their inherent glyphs. Unlike existing frameworks, CharFormer introduces a parallel target task for capturing additional information and injecting it into the image denoising backbone, which will maintain the consistency of character glyphs during character image denoising. Moreover, we utilize attention-based networks for global-local feature interaction, which will help to deal with blind denoising and enhance denoising performance. We compare CharFormer with state-of-the-art methods on multiple datasets. The experimental results show the superiority of CharFormer quantitatively and qualitatively.Comment: Accepted by ACM MM 202

Linking Stoichiometric Homeostasis of Microorganisms with Soil Phosphorus Dynamics in Wetlands Subjected to Microcosm Warming

Soil biogeochemical processes and the ecological stability of wetland ecosystems under global warming scenarios have gained increasing attention worldwide. Changes in the capacity of microorganisms to maintain stoichiometric homeostasis, or relatively stable internal concentrations of elements, may serve as an indicator of alterations to soil biogeochemical processes and their associated ecological feedbacks. In this study, an outdoor computerized microcosm was set up to simulate a warmed (+5°C) climate scenario, using novel, minute-scale temperature manipulation technology. The principle of stoichiometric homeostasis was adopted to illustrate phosphorus (P) biogeochemical cycling coupled with carbon (C) dynamics within the soil-microorganism complex. We hypothesized that enhancing the flux of P from soil to water under warming scenarios is tightly coupled with a decrease in homeostatic regulation ability in wetland ecosystems. Results indicate that experimental warming impaired the ability of stoichiometric homeostasis (H) to regulate biogeochemical processes, enhancing the ecological role of wetland soil as an ecological source for both P and C. The potential P flux from soil to water ranged from 0.11 to 34.51 mg m−2 d−1 in the control and 0.07 to 61.26 mg m−2 d−1 in the warmed treatment. The synergistic function of C-P acquisition is an important mechanism underlying C∶P stoichiometric balance for soil microorganisms under warming. For both treatment groups, strongly significant (p<0.001) relationships fitting a negative allometric power model with a fractional exponent were found between n-HC∶P (the specialized homeostatic regulation ability as a ratio of soil highly labile organic carbon to dissolved reactive phosphorus in porewater) and potential P flux. Although many factors may affect soil P dynamics, the n-HC∶P term fundamentally reflects the stoichiometric balance or interactions between the energy landscape (i.e., C) and flow of resources (e.g., N and P), and can be a useful ecological tool for assessing potential P flux in ecosystems

The Design of Intelligent Sensor Interface Circuit Based on 1451.2

At present, there are many complex and diverse bus interface standards in the field of sensor measurement and control, which leads that different sensors unable to be compatible with different field networks, thus increasing the difficulty of data acquisition and processing. In order to improve the compatibility and the intelligent level of sensors, in this work, a novel intelligent sensor interface model defined by IEEE1451.2 standard is proposed. Finally, the self-recognition, plug and play (PNP) functions are verified on FPGA platform

International Consensus Guidelines for the Definition, Detection, and Interpretation of Autophagy-Dependent Ferroptosis

Macroautophagy/autophagy is a complex degradation process with a dual role in cell death that is influenced by the cell types that are involved and the stressors they are exposed to. Ferroptosis is an iron-dependent oxidative form of cell death characterized by unrestricted lipid peroxidation in the context of heterogeneous and plastic mechanisms. Recent studies have shed light on the involvement of specific types of autophagy (e.g. ferritinophagy, lipophagy, and clockophagy) in initiating or executing ferroptotic cell death through the selective degradation of anti-injury proteins or organelles. Conversely, other forms of selective autophagy (e.g. reticulophagy and lysophagy) enhance the cellular defense against ferroptotic damage. Dysregulated autophagy-dependent ferroptosis has implications for a diverse range of pathological conditions. This review aims to present an updated definition of autophagy-dependent ferroptosis, discuss influential substrates and receptors, outline experimental methods, and propose guidelines for interpreting the results

miR-26a inhibits atherosclerosis progression by targeting TRPC3

Abstract Background Atherosclerosis, a chronic multi-factorial vascular disease, has become a predominant cause of a variety of cardiovascular disorders. miR-26a was previously reported to be involved in atherosclerosis progression. However, the underlying mechanism of miR-26a in atherosclerosis remains to be further explained. Methods High-fat diet (HFD)-fed apolipoprotein E (apoE)−/− mice and oxidized low-density lipoprotein (ox-LDL)-stimulated human aortic endothelial cells (HAECs) were established as in vivo and in vitro models of atherosclerosis. RT-qPCR and western blot analysis were performed to measure the expression of miR-26a and transient receptor potential canonical 3 (TRPC3), respectively. Binding between miR-26a and TRPC3 was predicted with bioinformatics software and verified using a dual luciferase reporter assay. The effects of miR-26a on the lipid accumulation, atherosclerotic lesion, and inflammatory response in HFD-fed apoE−/− mice were investigated by a colorimetric enzymatic assay system, hematoxylin–eosin and oil-Red-O staining, and ELISA, respectively. Additionally, the effects of miR-26a or combined with TRPC3 on cell viability, apoptosis and the nuclear factor-kappa B (NF-κB) pathway in ox-LDL-stimulated HAECs were evaluated by MTT assay, TUNEL assay, and western blot, respectively. Results miR-26a was downregulated in HFD-fed apoE−/− mice and ox-LDL-stimulated HAECs. miR-26a overexpression inhibited the pathogenesis of atherosclerosis by attenuating hyperlipidemia, atherosclerotic lesion and suppressing inflammatory response in HFD-fed apoE−/− mice. Moreover, miR-26a overexpression suppressed inflammatory response and the NF-κB pathway, promoted cell viability and inhibited apoptosis in ox-LDL-stimulated HAECs. Additionally, TRPC3 was demonstrated to be a direct target of miR-26a. Enforced expression of TRPC3 reversed the effects of miR-26a on cell viability, apoptosis, and the NF-κB pathway in ox-LDL-treated HAECs. Conclusions miR-26a alleviated the development of atherosclerosis by regulating TRPC3, providing a potential target for atherosclerosis treatment

Splitting and Combining as a Gamification Method in Engaging Structured Knowledge Learning

The understanding of the structure of knowledge is an essential step of education. Although teachers offer the information foundation and relationship among knowledge points, there are still few methods to encourage students to explore the structure of knowledge by themselves outside of classes. This paper explores the gamification method and the knowledge structure of computer science. We assess the gamification method of “splitting and combining” (SC) to encourage students to finish the process of learning structured knowledge in the university. The results show that this method works well in promoting learning enjoyment and that splitting demonstrates better performance than combining. We can consider the SC method when recommending a gamification method to engage students in structural learning assistance in future smart university education