Art Therapy as a Tool for Korean American Families: A Literature Review

This literature review aims to offer a comprehensive overview of attributes of Korean culture that make significant impacts on the family dynamic in the Korean immigrant households and to learn different types of art therapy that can help them. This literature review identifies specifically the struggles both first-generation Korean immigrant parents and second-generation Korean-American adolescents experience in order to understand where their conflicts come from. Later, different approaches of art therapy for the conflicts Korean immigrant households face are discussed. Data are collected from existing literature and videos by terms including art therapy for immigrants, family art therapy, Korean immigrant family, and art therapy for Korean, and they are critically analyzed through a cultural lens. The findings revealed that the major factor of the conflict between Korean parents and their second-generation adolescent children is the distinct relation-oriented Korean culture and that different models and approaches can be applied to Korean immigrant families considering the unique culture

Synthesis of Graphene-Based Hybrid Nanomaterials and Their Applications

Department of Chemical EngineeringThis thesis describes approaches to develop new types of graphene-based nanomaterials and their practical applications in various fields. Diverse functionalities can be achieved by the chemical modification of graphene oxide (GO), which makes it easy to host and grow functional nanomaterials on the surface of graphene. Furthermore, GO-based nanomaterials can be hybridized with inorganic nanomaterials using a layer-by-layer assembly (LbL) method, which is a versatile technique to fabricate multilayer thin films. Based on these integrated nanostructures, we have explored their potential uses as additives in a non-aqueous suspension, carbocatalysts for biomass reforming, and electrocatalysts for oxygen reduction, hydrogen evolution, and biomass reforming reaction. This thesis is divided into four parts: (1) The synthesis of GO, (2) Chemical modifications of GO for non-aqueous suspensions, (3) Graphene-based carbocatalysts for biomass reforming, and (4) a Graphene-based electrocatalyst. The first part introduces the general experimental method for GO and its mass production. Based on the conventional synthetic process of GO, we successfully developed a synthetic process for its mass production, resulting in large amounts as well as a high-quality product. The second part suggests various approaches for the chemical functionalization of GO with various organic molecules. Diverse functional groups of GO are easily modifiable to different structures and functionalities, thus its derivative can offer extraordinary possibilities for various utilizations. In particular, to use graphene derivatives in real applications, its dispersion stability is an important issue. Herein, we developed diverse GO-derivatives with chemical modification to improve its dispersion stability in non-aqueous suspensions. The third part represents a GO-based carbocatalyst for biomass reforming. GO and its derivatives are promising metal-free heterogeneous catalysts due to their high surface areas and rich chemical properties. We developed a bifunctional boron-doped sulfonated graphene oxide by chemical modification and demonstrated its excellent catalytic conversion of biomass into valuable chemicals. In the last part, various forms of graphene derivatives were fabricated and utilized as electrocatalysts for oxygen evolution reactions and electrochemical biomass reforming. A series of nitrogen-doped reduced graphene nanosheets were synthesized and exhibiting varying degrees and configurations of nitrogen depending on the type of precursors used. Furthermore, three-dimensional hybrid electrocatalytic electrodes were assembled by the LbL method toward simultaneous biomass conversion and hydrogen evolution reactions. In this research, we highlight how the nanostructure of electrocatalysts can be precisely controlled and how it affects their electrocatalytic performance.clos

A Novel CCA Attack for NTRU+ KEM

The KpqC competition has begun in 2022, that aims to standardize Post-Quantum Cryptography (PQC) in the Republic of Korea. Among the 16 submissions of the KpqC competition, the lattice-based schemes exhibit the most promising and balanced features in performance. In this paper, we propose an effective classical CCA attack to recover the transmitted session key for NTRU+, one of the lattice-based Key Encapsulation Mechanisms (KEM) proposed in the KpqC competition, for the first time. With the proposed attacks, we show that all the suggested parameters of NTRU+ do not satisfy the claimed security. We also suggest a way to modify the NTRU+ scheme to defend our attack

Covalent functionalization based heteroatom doped graphene nanosheet as a metal-free electrocatalyst for oxygen reduction reaction

Oxygen reduction reaction (ORR) is an important reaction in energy conversion systems such as fuel cells and metal-air batteries. Carbon nanomaterials doped with heteroatoms are highly attractive materials for use as electrocatalysts by virtue of their excellent electrocatalytic activity, high conductivity, and large surface area. This study reports the synthesis of highly efficient electrocatalysts based on heteroatom-doped graphene nanosheets prepared through covalent functionalization using various small organic molecules and a subsequent thermal treatment. A series of nitrogen-doped reduced graphene oxide (NRGOn) nanosheets exhibited varying degrees and configurations of nitrogen atoms within the graphitic framework depending on the type of precursors used. On the basis of the rotating disk electrode (RDE) and rotating ring-disk electrode (RRDE) experiments, NRGO3, with a high degree of pyridinic-N content, displayed the desired one-step, quasi-four-electron transfer pathway during ORR, similar to commercial Pt/C. We also demonstrated the potential of covalent functionalization of sulfur and boron-doped graphene nanosheets.close4

Electrocatalytic arsenite oxidation in bicarbonate solutions combined with CO₂ reduction to formate

Sunlight-driven water-energy nexus technologies are receiving increasing attention. This study presents a hybrid electrochemical system that catalyzes the oxidation of As(III) to As(V) with a nanoparticulate TiO₂ electrocatalyst (Ti/Ir_(1-x)Ta_xO_y/TiO₂; denoted as an n-TEC) while simultaneously converting CO₂ to formate on a Bi electrode in aqueous bicarbonate solutions at circum-neutral pH. Linear sweep voltammograms of n-TEC exhibit a specific As(III) oxidation peak (E_(p,As)), at which the Faradaic efficiency (FE) of As(V) production is ∼100%. However, the application of a potential higher than the peak (E > E_(p,As)) leads to a significant decrease in the FE due to water oxidation. Upon the addition of chloride, the oxidation of water and chloride occur competitively, producing reactive chlorine species responsible for mediating the oxidation of As(III). The Bi electrodes synthesized via the electrodeposition of Bi³⁺ typically show high FEs of >80% for formate production in bicarbonate solution purged with CO₂. The addition of chloride significantly enhances the current while maintaining the FE. The n-TEC catalyst and Bi electrodes are paired in a single device equipped with a membrane, and significant effort is made to achieve the same FEs in both the anodic and cathodic reactions as in their half-reactions. Finally, the optimized n-TEC/Bi pair is coupled with a low-cost, commercially available photovoltaic (PV). Various technical factors that drive the overall reactions with the PV are considered, and maximum FEs of ∼95% are achieved for the production of both As(V) and formate

TimeKit: A Time-series Forecasting-based Upgrade Kit for Collaborative Filtering

Recommender systems are a long-standing research problem in data mining and machine learning. They are incremental in nature, as new user-item interaction logs arrive. In real-world applications, we need to periodically train a collaborative filtering algorithm to extract user/item embedding vectors and therefore, a time-series of embedding vectors can be naturally defined. We present a time-series forecasting-based upgrade kit (TimeKit), which works in the following way: it i) first decides a base collaborative filtering algorithm, ii) extracts user/item embedding vectors with the base algorithm from user-item interaction logs incrementally, e.g., every month, iii) trains our time-series forecasting model with the extracted time-series of embedding vectors, and then iv) forecasts the future embedding vectors and recommend with their dot-product scores owing to a recent breakthrough in processing complicated time-series data, i.e., neural controlled differential equations (NCDEs). Our experiments with four real-world benchmark datasets show that the proposed time-series forecasting-based upgrade kit can significantly enhance existing popular collaborative filtering algorithms.Comment: Accepted at IEEE BigData 202

EXIT: Extrapolation and Interpolation-based Neural Controlled Differential Equations for Time-series Classification and Forecasting

Deep learning inspired by differential equations is a recent research trend and has marked the state of the art performance for many machine learning tasks. Among them, time-series modeling with neural controlled differential equations (NCDEs) is considered as a breakthrough. In many cases, NCDE-based models not only provide better accuracy than recurrent neural networks (RNNs) but also make it possible to process irregular time-series. In this work, we enhance NCDEs by redesigning their core part, i.e., generating a continuous path from a discrete time-series input. NCDEs typically use interpolation algorithms to convert discrete time-series samples to continuous paths. However, we propose to i) generate another latent continuous path using an encoder-decoder architecture, which corresponds to the interpolation process of NCDEs, i.e., our neural network-based interpolation vs. the existing explicit interpolation, and ii) exploit the generative characteristic of the decoder, i.e., extrapolation beyond the time domain of original data if needed. Therefore, our NCDE design can use both the interpolated and the extrapolated information for downstream machine learning tasks. In our experiments with 5 real-world datasets and 12 baselines, our extrapolation and interpolation-based NCDEs outperform existing baselines by non-trivial margins.Comment: main 8 page

Tensile property improvement of TWIP-cored three-layer steel sheets fabricated by hot-roll-bonding with low-carbon steel or interstitial-free steel

TWIP-cored three-layer steel sheets were newly fabricated by hot rolling of TWIP steel sheet surrounded by low-carbon (LC) or interstitial-free (IF) steel sheets. TWIP/LC or TWIP/IF interfaces were well bonded without pores or voids, while a few pearlites were thinly formed along the interfaces. The strengths and elongation of the TWIP-cored sheets increased as the volume fraction of TWIP-cored region increased, and were also well matched with the ones calculated by a rule of mixtures based on volume fraction or force fraction. According to digital image correlation and electron back-scatter diffraction analyses, very high strain hardening effect in the initial deformation stage and active twin formation in the interfacial region beneficially affected the overall homogeneous deformation in the TWIP-cored sheets without any yield point phenomenon occurring in the LC sheet and serrations occurring in the TWIP sheet, respectively. These TWIP-cored sheets can cover a wide range of yield strength, tensile strength, and ductility levels, e.g., 320-498 MPa, 545-878 MPa, and 48-54%, respectively, by controlling the volume fraction of TWIP-cored region, and thus present new applications to multi-functional automotive steel sheets requiring excellent properties.1163Ysciescopu

The impact of socioeconomic deprivation on the risk of atrial fibrillation in patients with diabetes mellitus:A nationwide population-based study

OBJECTIVE: To evaluate the relationship between socioeconomic status and the risk of atrial fibrillation (AF) in patients with diabetes mellitus (DM). RESEARCH DESIGN AND METHODS: From the National Health Insurance Service (NHIS) database, we identified 2,429,610 diabetic patients who underwent national health check-ups between 2009 and 2012. Tracing back the subjects for 5 years from the date of health check-up, we determined the subjects’ income and whether they received medical aid (MA) during the past 5 years. Subjects were divided into six groups according to the number of years of receiving (MA groups 0 through 5) and into four groups according to socioeconomic status change during the past 5 years. We estimated the risk of AF for each group using the Cox proportional-hazards model. RESULTS: During a median follow-up of 7.2 ± 1.7 years, 80,257 were newly identified as AF. The MA groups showed a higher risk of AF than the non-MA group with the hazard ratios (HRs) and 95% confidence interval (CI) 1.32 (1.2–1.44), 1.33 (1.22–1.45), 1.23 (1.13–1.34), 1.28 (1.16–1.4), and 1.50 (1.39–1.63) for MA groups 1 through 5, respectively. Dividing subjects according to socioeconomic condition change, those who experienced worsening socioeconomic status (non-MA to MA) showed higher risk compared to the persistent non-MA group (HR 1.54; 95% CI 1.38–1.73). CONCLUSION: Low socioeconomic status was associated with the risk of AF in patients with diabetes. More attention should be directed at alleviating health inequalities, targeting individuals with socioeconomic deprivation to provide timely management for AF