Search

Despite their excellent performance and versatility, the efficient integration of small lasers with other optical devices has long been hindered by their broad emission divergence. In this study, we introduce a novel approach for emission engineering in microdisk lasers, significantly enhancing their vertical emission output by directly integrating specially designed reflective metalenses, referred to as “meta-micromirrors”. A 5 μm-diameter microdisk laser is precisely positioned at an 8 μm focal distance on a 30 × 30 μm2 meta-micromirror featuring a numerical aperture (NA) of 0.95, accomplished through micro-transfer printing techniques. Our experiments demonstrated a notable increase in the emission efficiency within an NA of 0.65. Specifically, we observed a 2.68-fold increase in the average emission from ten microdisk lasers. This integration not only enhances the emission efficiency of small lasers but also holds considerable implications for micro- and nano-photonic integrations. The results of this integration open up new possibilities in various fields, including photonic integrated circuits, bio-sensing technologies, and the development of quantum light sources.

In this paper, we develop an advanced architecture and come up with a comprehensive design methodology of genetically optimized hybrid fuzzy neural networks (gHFNNs). The construction of gHFNN exploits fundamental technologies of computational intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms (GAs). The architecture of the gHFNN results from a highly synergistic usage of the genetic optimization-driven hybrid system being generated by combining fuzzy neural networks (FNNs) with polynomial neural networks (PNN). FNN contributes to the formation of the premise part of the overall network structure of the gHFNN. The consequence part of the gHFNN is designed using PNNs. The optimization of the FNN is realized with the aid of a standard backpropagation learning algorithm and genetic optimization. As the consequence part of the gHFNN, the development of the PNN dwells on two general optimization mechanisms: the structural optimization is realized via GAs whereas in the case of the parametric optimization we proceed with a standard least square method-based learning (optimization). Through the consecutive process of such structural and parametric optimization, an optimized PNN becomes generated in a dynamic fashion. To evaluate the performance of the gHFNNs, we experimented with a number of representative numerical examples. A comparative analysis demonstrates that the proposed gHFNNs are neurofuzzy systems with higher accuracy as well as more superb predictive capability than other models available in the literature. Index Terms--Computational intelligence (CI), design procedure, fuzzy neural networks (FNNs), genetically optimized hybrid fuzzy neural networks (gHFNNs), genetically optimized polynomial neural networks (gPNNs), group method of data handling (GMDH).

In this study, we introduce and investigate a class of neural architectures of Polynomial Neural Networks (PNNs), discuss a comprehensive design methodology and carry out a series of numeric experiments. Two kinds of PNN architectures, namely a basic PNN and a modified PNN architecture are discussed. Each of them comes with two types such as the generic and the advanced type. The essence of the design procedure dwells on the Group Method of Data Handling. PNN is a flexible neural architecture whose structure is developed through learning. In particular, the number of layers of the PNN is not fixed in advance but becomes dynamically meaning that the network grows over the training period. In this sense, PNN is a self-organizing network. A comparative analysis shows that the proposed PNN are models with higher accuracy than other fuzzy models. Keywords: Polynomial neural networks (PNNs); The generic and the advanced type of PNNs; Group method of data handling (GMDH); Self-organizing network; Design procedure

Experimental software datasets describing software projects in terms of their complexity and development time have been a subject of intensive modeling. A number of various modeling methodologies and modeling designs have been proposed including such development frameworks as neural networks, fuzzy and neurofuzzy models. In this study, we introduce a concept of self-organizing neurofuzzy networks (SONFN), a hybrid modeling architecture combining neurofuzzy networks (NFN) and polynomial neural networks (PNN). For these networks we develop a comprehensive design methodology. The construction of SONFNs takes advantage of the we!l-established technologies of computational intelligence (CI), namely fuzzy sets, neural networks and genetic algorithms. The architecture of the SONFN results from a synergistic usage of NFNs and PNNs. NFN contributes to the formation of the premise part of the rule-based structure of the SONFN. The consequence part of the SONFN is designed using PNNs. We discuss two types of SONFN architectures whose taxonomy is based on the NFN scheme being applied to the premise part of SONFN. We introduce a comprehensive learning algorithm. It is shown that this network exhibits a dynamic structure as the number of its layers as well as the number of nodes in each layer of the SONFN are not predetermined (as this is the case in a popular topology of a multilayer perceptron). The experimental results include a well-known NASA dataset concerning software cost estimation. Index Terms--Computational intelligence, genetic algorithms, linear fuzzy inference, neurofuzzy networks, polynomial neural networks, self-organizing neurofuzzy networks.

To investigate the phytochemical constituents of Hibiscus hamabo, a chromatographic method was used. A new phenolic glucoside, woodorien A (1), along with 10 known compounds, rutin (2), isoquercetin (3), nicotiflorin (4), astragalin (5), trans‐tiliroside (6), N‐trans‐feruloyltyramine (7), N‐trans‐caffeoyltyramine (8), woodorien (9), quercetin 3′‐galactopyranoside (10), and cis‐tiliroside (11), was isolated. The structure of 1 was identified as ethyl 3‐O‐β‐d‐glucopyranosyl‐4‐hydroxybenzoate. Compounds 8, 9, and 11 were isolated from genus Hibiscus, and 2–7 and 10 were isolated from H. hamabo for the first time. Their chemical structures were determined through the interpretation of spectroscopic data. The properties of the UVA photoaging induction assay of all isolated compounds were measured. Compounds 3, 8, and 10 exhibited recovery from photoaging induced by UVA in CCD‐986sk. These results suggest that H. hamabo is a potential source of natural antiphotoaging ingredients and can be used in cosmetic applications. [ABSTRACT FROM AUTHOR]

Purpose: Changes in facial appearance are affected by various intrinsic and extrinsic factors, which vary from person to person. Therefore, each person needs to determine their skin condition accurately to care for their skin accordingly. Recently, genetic identification by skin-related phenotypes has become possible using genome-wide association studies (GWAS) and machine-learning algorithms. However, because most GWAS have focused on populations with American or European skin pigmentation, large-scale GWAS are needed for Asian populations. This study aimed to evaluate the correlation of facial phenotypes with candidate single-nucleotide polymorphisms (SNPs) to predict phenotype from genotype using machine learning. Materials and Methods: A total of 749 Korean women aged 30– 50 years were enrolled in this study and evaluated for five facial phenotypes (melanin, gloss, hydration, wrinkle, and elasticity). To find highly related SNPs with each phenotype, GWAS analysis was used. In addition, phenotype prediction was performed using three machine-learning algorithms (linear, ridge, and linear support vector regressions) using five-fold cross-validation. Results: Using GWAS analysis, we found 46 novel highly associated SNPs (p < 1× 10− 05): 3, 20, 12, 6, and 5 SNPs for melanin, gloss, hydration, wrinkle, and elasticity, respectively. On comparing the performance of each model based on phenotypes using five-fold cross-validation, the ridge regression model showed the highest accuracy (r2 = 0.6422– 0.7266) in all skin traits. Therefore, the optimal solution for personal skin diagnosis using GWAS was with the ridge regression model. Conclusion: The proposed facial phenotype prediction model in this study provided the optimal solution for accurately predicting the skin condition of an individual by identifying genotype information of target characteristics and machine-learning methods. This model has potential utility for the development of customized cosmetics. [ABSTRACT FROM AUTHOR]

Isoacteoside, a dihydroxypheynylethyl glycoside, is a major bioactive component of Abeliophyllum distichum (White Forsythia) which is a deciduous shrub native to the south and central areas of Korea. The present study is designed to evaluate the anti-inflammatory activities and underlying mechanisms of isoacteoside in human mast cell line, HMC-1 cells. We isolated isoacteoside from A. distichum. The anti-inflammatory effect of isoacteoside was investigated in HMC-1 cells by studying the following markers: phorbol 12-myristate 13-acetate and calcium ionophore A23187 (PMACI)-induced interleukin (IL)-1β, IL-6, IL-8, and tumor necrosis factor alpha (TNF-α) secretion and mRNA expression by ELISA and RT-PCR, respectively. In addition, mechanism related to anti-inflammatory was investigated by Western blotting. Isoacteoside significantly suppressed the production and mRNA expression of proinflammatory cytokines including IL-1β, IL-6, IL-8 and TNF-α in PMACI-stimulated HMC-1 cells without cytotoxicity. It was found that anti-inflammatory effects of isoacteoside are mediated by action on caspase-1, mitogen-activated protein kinases (c-Jun N-terminal kinase, p38, extracellular signal-regulated protein kinase) and nuclear factor-kappa B pathways. Taken together, the present findings provide new insights that isoacteoside may be a promising anti-inflammatory agent for inflammatory disorders. [ABSTRACT FROM AUTHOR]

We report an enhancement in light emission efficiency of Si nanocrystal (NC) light-emitting diodes (LEDs) by employing 5.5 periods of SiCN/SiC superlattices (SLs). SiCN and SiC layers in SiCN/SiC SLs were designed by considering the optical bandgap to induce the uniform electron sheet parallel to the SL planes. The electrical property of Si NC LED with SiCN/SiC SLs was improved. In addition, light output power and wall-plug efficiency of the Si NC LED with SiCN/SiC SLs were also enhanced by 50% and 40%, respectively. This was attributed to both the formation of two-dimensional electron gas, i.e., uniform electron sheet parallel to the SiCN/SiC SL planes due to the conduction band offset between the SiCN layer and SiC layer, and an enhanced electron transport into the Si NCs due to a lower tunneling barrier height. We show here that the use of the SiCN/SiC SL structure can be very useful in realizing a highly efficient Si NC LED. [ABSTRACT FROM AUTHOR]

We report an enhancement in light emission efficiency form Si nanocrystal (NC) light-emitting diodes (LEDs) via surface plasmons (SPs) by employing Au nanoparticles (NPs). Photoluminescence intensity of Si NCs with Au NPs was enhanced by 2 factors of magnitude due to the strong coupling of Si NCs and SP resonance modes of Au NPs. The electrical characteristics of Si NC LED were significantly improved, which was attributed to an increase in an electron injection into the Si NCs due to the formation of inhomogeneous Schottky barrier at the SiC-indium tin oxide interface. Moreover, light output power from the Si NC LED was enhanced by 50% due to both SP coupling and improved electrical properties. The results presented here can provide a very promising way to significantly enhance the performance of Si NC LED. [ABSTRACT FROM AUTHOR]

In this study, we introduce an advanced architecture of genetically optimized Hybrid Fuzzy Neural Networks (gHFNN) and develop a comprehensive design methodology supporting their construction. A series of numeric experiments is included to illustrate the performance of the networks. The construction of gHFNN exploits fundamental technologies of Computational Intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms (GAs). The architecture of the gHFNNs results from a synergistic usage of the genetic optimization-driven hybrid system generated by combining Fuzzy Neural Networks (FNN) with Polynomial Neural Networks (PNN). In this tandem, a FNN supports the formation of the condition part of the rule-based structure of the gHFNN. The conclusion part of the gHFNN is designed using PNNs. We distinguish between two types of the simplified fuzzy inference rule-based FNN structures showing how this taxonomy depends upon the type of a fuzzy partition of input variables. As to the conclusion part of the gHFNN, the development of the PNN dwells on two general optimization mechanisms: the structural optimization is realized via GAs whereas in case of the parametric optimization we proceed with a standard least square method-based learning. To evaluate the performance of the gHFNN, we experimented with three representative numerical examples. A comparative analysis demonstrates that the proposed gHFNN come with higher accuracy as well as superb predictive capabilities when compared with other neurofuzzy models. [ABSTRACT FROM AUTHOR]

Patrinia villosa (Thunb.) Juss is a traditional herb commonly used in East Asia including Korea, Japan, and China. It has been administered to reduce and treat inflammation in Donguibogam, Korea. The mechanism for its anti-inflammatory effects has already been reported. In this study, we confirmed the efficacy of Patrinia villosa (Thunb.) Juss ethanol extract (Pv-EE) for inducing autophagy and investigate its anti-melanogenic properties. Melanin secretion and content were investigated using cells from the melanoma cell line B16F10. Pv-EE inhibited melanin in melanogenesis induced by α-melanocyte-stimulating hormone (α-MSH). The mechanism of inhibition of Pv-EE was confirmed by suppressing the mRNA of microphthalmia-associated transcription factor (MITF), decreasing the phosphorylation level of CREB, and increasing the phosphorylation of ERK. Finally, it was confirmed that Pv-EE induces autophagy through the autophagy markers LC3B and p62, and that the anti-melanogenic effect of Pv-EE is inhibited by the autophagy inhibitor 3-methyl adenine (3-MA). These results suggest that Pv-EE may be used as a skin protectant due to its anti-melanin properties including autophagy. [ABSTRACT FROM AUTHOR]

Keratoameloblastoma is a rare subtype of ameloblastoma. It tends to have a poor prognosis, and therefore a careful diagnosis based on imaging is important in planning appropriate surgical treatment for this distinctive type of ameloblastoma. A unique feature of keratoameloblastoma is atypical calcification inside the mass, such as a ground-glass appearance, internal calcification, or a mixed radiolucent and radiopaque pattern. Because of its poor prognosis and the likelihood of frequent recurrences, surgical resection is considered the treatment of choice. This study reports a new case of keratoameloblastoma with special emphasis on its radiologic features and reviews previously reported cases of keratoameloblastoma. [ABSTRACT FROM AUTHOR]