A Spherical Hybrid Triboelectric Nanogenerator for Enhanced Water Wave Energy Harvesting

Water waves are a continuously generated renewable source of energy. However, their random motion and low frequency pose significant challenges for harvesting their energy. Herein, we propose a spherical hybrid triboelectric nanogenerator (SH-TENG) that efficiently harvests the energy of low frequency, random water waves. The SH-TENG converts the kinetic energy of the water wave into solid-solid and solid-liquid triboelectric energy simultaneously using a single electrode. The electrical output of the SH-TENG for six degrees of freedom of motion in water was investigated. Further, in order to demonstrate hybrid energy harvesting from multiple energy sources using a single electrode on the SH-TENG, the charging performance of a capacitor was evaluated. The experimental results indicate that SH-TENGs have great potential for use in self-powered environmental monitoring systems that monitor factors such as water temperature, water wave height, and pollution levels in oceans.11Ysciescopu

Emerging Microreaction Systems Based on 3D Printing Techniques and Separation Technologies

The past three decades have seen increasing progress in the integration and process diversification of microfluidic systems for use in chemistry, biochemistry, and analysis. Here we summarize recent achievements in microreaction modules and microseparation units. We look into recent developments of microreaction systems fabricated by various 3D printing techniques for chemical synthetic applications. Moreover, we take a look at the recent achievements of newly developed microseparation technologies with enhanced separation efficiency realized by adopting single or hybrid principles as well as novel device concepts. Emerging technologies of 3D printing have potential to realize a vertically stacking the microchannels and miniaturization of bulky microreaction accessories. When the advanced microreaction systems are integrated with newly developed microseparation technologies, automated synthesis of industrial compounds, such as pharmaceuticals which need multiple types of salification chemistry, will be almost completed. Many opportunities are open to developing innovative microreaction systems with these techniques that can also be highly durable under harsh conditions.113Ysciescopu

Comparative study of computational algorithms for the Lasso with high-dimensional, highly correlated data

Variable selection is important in high-dimensional data analysis. The Lasso regression is useful since it possesses sparsity, soft-decision rule, and computational efficiency. However, since the Lasso penalized likelihood contains a nondifferentiable term, standard optimization tools cannot be applied. Many computation algorithms to optimize this Lasso penalized likelihood function in high-dimensional settings have been proposed. To name a few, coordinate descent (CD) algorithm, majorization-minimization using local quadratic approximation, fast iterative shrinkage thresholding algorithm (FISTA) and alternating direction method of multipliers (ADMM). In this paper, we undertake a comparative study that analyzes relative merits of these algorithms. We are especially concerned with numerical sensitivity to the correlation between the covariates. We conduct a simulation study considering factors that affect the condition number of covariance matrix of the covariates, as well as the level of penalization. We apply the algorithms to cancer biomarker discovery, and compare convergence speed and stability.OAIID:RECH_ACHV_DSTSH_NO:T201719354RECH_ACHV_FG:RR00200001ADJUST_YN:EMP_ID:A079602CITE_RATE:1.904FILENAME:10.1007-s10489-016-0850-7.pdfDEPT_NM:통계학과EMAIL:[email protected]_YN:YFILEURL:https://srnd.snu.ac.kr/eXrepEIR/fws/file/c3cdcc74-11a7-4316-8c57-a7b1737f830c/linkN

Characterizing Clickbaits on Instagram

Clickbaits are routinely utilized by online publishers to attract the attention of people in competitive media markets. Clickbaits are increasingly used in visual-centric social media but remain a largely unexplored problem. Existing defense mechanisms rely on text-based features and are thus inapplicable to visual social media. By exploring the relationships between images and text, we develop a novel approach to characterize clickbaits on visual social media. Focusing on the topic of fashion, we first examined the prevalence of clickbaits on Instagram and surveyed their negative impacts on user experience through a focus group study (N=31). In a largescale analysis, we collected 450,000 Instagram posts and manually labeled 12,659 of these posts to determine what people consider to be clickbaits. By combining three different types of features (e.g., image, text, and meta features), our classifier was able detect clickbaits with an accuracy of 0.863. We performed an extensive feature analysis and showed that content-based features are much more important than meta features (e.g., number of followers) in clickbait classification. Our analysis indicates that approximately 11% of fashion-related Instagram posts are clickbait and that these posts are consistently accompanied by many hashtags, thus demonstrating that clickbait is prevalent in visual social media

Clickbaits Labeling Data on Instagram

Our dataset is composed of information about 7,769 posts on Instagram. The data collection was done over a two-week period in July 2017 using an InstaLooter API. We searched for posts mentioning 62 internationally renowned fashion brand names as hashtag

Numbering-up Module of Metal Microreactor for Scale-up Drug Production

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