Browsing by Author "Huang, Chun-Kai"

Now showing 1 - 4 of 4

High-power and single-mode VCSEL arrays with single-polarized outputs by using package-induced tensile strain
(2020-09-01)
Shi, Jin-Wei
;
Khan, Zuhaib
;
Horng, Ray-Hua
;
Yeh, Hsiao-Yun
;
Huang, Chun-Kai
In this work, we demonstrate a novel high-power vertical-cavity surface-emitting laser (VCSEL) array with highly single-mode (SM) and single-polarized output performance without significantly increasing the intra-cavity loss and threshold current (Ith). By combining a low-loss zinc-diffusion aperture with an electroplated copper substrate, we can obtain a highly SM output (side mode suppression ratio >50 dB) with a wry narrow divergence angle (1 / e(2) : similar to 10 degrees) under high output power (3.1 W; 1% duty cycle) and sustain a single polarization state, with a polarization suppression ratio of around 9 dB, under the full range of bias currents. Compared to the reference device without the copper substrate, the demonstrated array can not only switch the output optical spectra from quasi-SM to highly SM but also maintain a close threshold current value (Ith: 0.8 versus 0.7 mA per unit device) and slope efficiency. The enhancement in fundamental mode selectivity of our VCSEL structure can be attributed to the single-polarized lasing mode induced by tensile strain, which is caused by the electroplated copper substrate, as verified by the double-crystal x-ray measurement results. (C) 2020 Optical Society of America
Multifunctional micelles for cancer cell targeting, distribution imaging, and anticancer drug delivery
(2007-09-24)
Huang, Chun-Kai
;
Lo, Chun-Liang
;
Chen, Hung-Hao
;
Hsiue, Ging-Ho
;
應用化學系
Multifunctional micelles for cancer cell targeting, distribution imaging, and anticancer drug delivery were prepared from an environmentally-sensitive graft copolymer, poly(N-isopropyl acrylamide-co-methacryl acid)-g-poly(D,L-lactide) (P(NIPAAm-co-MAAc)-g-PLA), a diblock copolymer, methoxy poly(ethylene glycol)-b-Poly(D,L-lactide) (mPEG-PLA) and two functionalized diblock copolymers, galactosamine-PEG-PLA (Gal-PEG-PLA) and fluorescein isothiocyanate-PEG-PLA (FITC-PEG-PLA). Anticancer drug, free base doxorubicin (Dox) was incorporated into the inner core of multifunctional micelles by dialysis. From the drug release study, a change in pH (from pH 7.4 to 5.0) deformed the structure of the inner core from that of aggregated P(NIPAAm-co-MAAc), causing the release of a significant quantity of doxorubicin (Dox) from multifunctional micelles. Multifunctional micelles target specific tumors by an asialoglycoprotein (HepG2 cells)-Gal (multifunctional micelle) receptor-mediated tumor targeting mechanism. This mechanism then causes intracellular pH changes which induce Dox release from multifunctional micelles and that micelles have strong effects on the viability of HepG2 cells and are abolished by galactose. Confocal laser scanning microscopy (CLSM) reveals a clear distribution of multifunctional micelles. With careful design and sophisticated manipulation, polymeric micelles can be widely used in cancer diagnosis, cancer targeting, and cancer therapy simultaneously.
QA document recommendations for communities of question-answering websites
(2014-02-01)
Liu, Duen-Ren
;
Chen, Yu-Hsuan
;
Huang, Chun-Kai
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
With the rapid development of Internet and Web 2.0 technology, Question & Answering (Q&A) websites have become an essential knowledge-sharing platform. This platform provides knowledge community services where users with common interests or expertise can form a knowledge community. Community members can collect and share QA knowledge (documents) regarding their interests. However, due to the massive amount of QAs created every day, information overload can become a major problem. Consequently, a recommendation mechanism is needed to recommend QA documents for communities of Q&A websites. Existing studies did not investigate the recommendation mechanisms for knowledge collections in communities of Q&A Websites. Traditional recommendation methods use member importance as weight to consolidate individual profiles and generate group profiles, which in turn are used to filter out items of recommendation. However, they do not consider certain factors of the recommended items, such as the reputation of the community members and the complementary relationships between documents. In this work, we propose a novel method to recommend related QA documents for knowledge communities of Q&A websites. The proposed method recommends QA documents by considering factors such as the community members' reputation in collecting and answering QAs, the push scores and collection time of QAs, the complementary relationships between QAs and their relevance to the communities. This research evaluates and compares the proposed methods using an experimental dataset collected from Yahoo! Answers Taiwan website. Experimental results show that the proposed method outperforms other conventional methods, providing a more effective manner to recommend Q&A documents to knowledge communities. (C) 2013 Elsevier B.V. All rights reserved.
Recommending QA Documents for Communities of Question-Answering Websites
(2013)
Liu, Duen-Ren
;
Huang, Chun-Kai
;
Chen, Yu-Hsuan
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
Question & Answering (Q&A) websites have become an essential knowledge-sharing platform. This platform provides knowledge-community services where users with common interests or expertise can form a knowledge community to collect and share QA documents. However, due to the massive amount of QAs, information overload can become a major problem. Consequently, a recommendation mechanism is needed to recommend QAs for communities of Q&A websites. Existing studies did not investigate the recommendation mechanisms for knowledge collections in communities of Q&A Websites. In this work, we propose a novel recommendation method to recommend related QAs for communities of Q&A websites. The proposed method recommends QAs by considering the community members\' reputations, the push scores and collection time of QAs, the complementary relationships between QAs and their relevance to the communities. Experimental results show that the proposed method outperforms other conventional methods, providing a more effective manner to recommend QA documents to knowledge communities.