Impact of Sea Surface Temperature and Surface Air Temperature on Maximizing Typhoon Rainfall: Focusing on Typhoon Maemi in Korea

In this study, the effects of surface air temperature (SAT) and sea surface temperature (SST) changes on typhoon rainfall maximization are analysed. Based on the numerically reproduced Typhoon Maemi, this study tried to maximize the typhoon-induced rainfall by increasing the amount of saturated water vapour in the atmosphere and the amount of water vapour entering the typhoon. Using the Weather Research and Forecasting (WRF) model, which is one of the regional climate models (RCMs), the rainfall simulated by WRF while increasing the SAT and SST to various sizes at initial conditions and boundary conditions of the model was analysed. As a result of the simulated typhoon rainfall, the spatial distribution of total rainfall depth on the land due to the increase combination of SAT and SST showed a wide variety without showing a certain pattern. This is attributed to the geographical location of the Korean peninsula, which is a peninsula between the continent and the ocean. In other words, under certain conditions, typhoons may drop most of the rainfall on the southern sea of the peninsula before landing on the peninsula. For instance, the 6-hour duration maximum precipitation (MP) in Busan Metropolitan City was 472.1 mm when the SST increased by 2.0°C. However, when the SST increased by 4.0°C, the MP was found to be 395.3 mm, despite the further increase in SST. This indicates that the MP at a particular area and the increase in temperature do not have a linear relationship. Therefore, in order to maximize typhoon rainfall, it is necessary to repeat the numerical experiment on various conditions and search for the combination of SAT and SST increase which is most suitable for the target typhoon

Characterization of urinary cotinine in non-smoking residents in smoke-free homes in the Korean National Environmental Health Survey (KoNEHS)

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.Abstract Background The objectives of this study were to determine urinary cotinine concentrations in non-smoking residents of smoke-free homes and to establish the relationship of urinary cotinine with housing type and other socio-demographic and secondhand smoke (SHS) exposure factors. Methods We used data from the Korean National Environmental Health Survey I (2009–2011). The study included 814 non-smoking adult residents living in apartments, attached, and detached housing. Residents who lived with smokers were excluded. Urinary cotinine concentration was used as a biomarker for SHS exposure. The factors associated with urinary cotinine levels in non-smoking residents were determined using multivariate regression analysis. Results Urinary cotinine was detected in 88 % of the 814 non-smoking residents of smoke-free homes. The urinary cotinine concentrations of residents living in attached [1.18 ng/mg creatinine (Cr)] and detached housing (1.23 ng/mg Cr) were significantly higher than those of residents who lived in apartments (0.69 ng/mg Cr). Urinary cotinine concentrations were significantly higher in residents who were men, those with a household income ≤1000 USD/month, those who were former smokers with >1 year and ≤1 year of not smoking, and those who experienced SHS odor every day. In the multivariate regression analysis, housing type, sex, former smoking status, and frequency of experiencing SHS odor were associated with urinary cotinine concentrations (R 2 = 0.14). Conclusions The majority of non-smoking residents of smoke-free homes had detectable urinary cotinine. Housing type, sex, former smoking status, and frequency of experiencing SHS odor were predictors for urinary cotinine concentrations in the study participants

Factors associated with secondhand smoke incursion into the homes of non-smoking residents in a multi-unit housing complex: a cross-sectional study in Seoul, Korea

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.Abstract Background In a multi-unit housing (MUH) complex, secondhand smoke (SHS) can pass from one living space to another. The aim of this study was to determine the prevalence of SHS incursion, and to establish the relationship between SHS incursion and socio-demographic and built environmental factors in MUH in Korea. Methods A population-based sample of 2600 residents (aged ≥19 years) living in MUH from across the city of Seoul, Korea, was obtained through a web-based selection panel. The residents completed a questionnaire detailing socio-demographic factors, smoking status, frequency of SHS incursion, and built environmental factors. The presence of a personal smoke-free home rule was determined by residents declaring that no one smoked inside the home. Results Of the 2600 participants, non-smoking residents who lived in homes with a personal smoke-free rule were selected for further analysis (n = 1784). In the previous 12 months, 74.7% of residents had experienced SHS incursion ≥1 times. A multivariate ordinal logistic regression analysis indicated that residents who spent more time at home, lived with children, supported the implementation of smoke-free regulations in MUH, lived in small homes, lived in homes with natural ventilation provided by opening a front door or the windows and front door, and lived in homes with more frequent natural ventilation were more likely to report SHS incursion into their homes. Conclusions The majority of the non-smoking residents experienced SHS incursion, even with a personal smoke-free rule in their homes. A smoke-free policy in MUH is needed to protect residents from SHS exposure when they are at home

Spin and Valley Polarized Multiple Fermi Surfaces of {\alpha}-RuCl3_3/Bilayer Graphene Heterostructure

We report the transport properties of ${\alpha}$-RuCl$_3$/bilayer graphene heterostructures, where carrier doping is induced by a work function difference, resulting in distinct electron and hole populations in ${\alpha}$-RuCl3 and bilayer graphene, respectively. Through a comprehensive analysis of multi-channel transport signatures, including Hall measurements and quantum oscillation, we unveil significant band modifications within the system. In particular, we observe the emergence of spin and valley polarized multiple hole-type Fermi pockets, originating from the spin-selective band hybridization between ${\alpha}$-RuCl$_3$ and bilayer graphene, breaking the spin degree of freedom. Unlike ${\alpha}$-RuCl$_3$ /monolayer graphene system, the presence of different hybridization strengths between ${\alpha}$-RuCl$_3$ and the top and bottom graphene layers leads to an asymmetric behavior of the two layers, confirmed by effective mass experiments, resulting in the manifestation of valley-polarized Fermi pockets. These compelling findings establish ${\alpha}$-RuCl$_3$ proximitized to bilayer graphene as an outstanding platform for engineering its unique low-energy band structure.Comment: accepted to AP

FlexRound: Learnable Rounding based on Element-wise Division for Post-Training Quantization

Post-training quantization (PTQ) has been gaining popularity for the deployment of deep neural networks on resource-limited devices since unlike quantization-aware training, neither a full training dataset nor end-to-end training is required at all. As PTQ schemes based on reconstructing each layer or block output turn out to be effective to enhance quantized model performance, recent works have developed algorithms to devise and learn a new weight-rounding scheme so as to better reconstruct each layer or block output. In this work, we propose a simple yet effective new weight-rounding mechanism for PTQ, coined FlexRound, based on element-wise division instead of typical element-wise addition such that FlexRound enables jointly learning a common quantization grid size as well as a different scale for each pre-trained weight. Thanks to the reciprocal rule of derivatives induced by element-wise division, FlexRound is inherently able to exploit pre-trained weights when updating their corresponding scales, and thus, flexibly quantize pre-trained weights depending on their magnitudes. We empirically validate the efficacy of FlexRound on a wide range of models and tasks. To the best of our knowledge, our work is the first to carry out comprehensive experiments on not only image classification and natural language understanding but also natural language generation, assuming a per-tensor uniform PTQ setting. Moreover, we demonstrate, for the first time, that large language models can be efficiently quantized, with only a negligible impact on performance compared to half-precision baselines, achieved by reconstructing the output in a block-by-block manner.Comment: Accepted to ICML 202

A Novel Approach to Synthesize Helix Wave Hollow Fiber Membranes for Separation Applications

Helix wave hollow fiber membranes are promising candidate to mitigate fouling and polarization effects in membrane operations. Current study describes a novel but simple approach to synthesize hollow fiber membranes with helix wave configuration. Poly(ether sulfone) (PES) based helix-waved hollow fiber membranes have been fabricated by dry-wet phase inversion process by using asymmetric coagulation conditions. Frequencies of the wave cycle have been observed approximately 20 and the wave length 7.1-7.6mm under the specifically required operating conditions defined by dope solution extrudate rate of 1g/min through 4cm of air-gap heights with 8.6m/min of winding speeds. On the other hand, simple hollow fibers are formed when the elongation force exerted by the winder is much higher than the surface tension of the external coagulant. The process can be useful for making polymer fibers for other applications as well

Inverse Design of Terahertz Nanoresonators through Physics-Informed Machine Learning

The rapid development of 6G communications using terahertz (THz) electromagnetic waves has created a demand for highly sensitive THz nanoresonators capable of detecting these waves. Among the potential candidates, THz nanogap loop arrays show promising characteristics but require significant computational resources for accurate simulation. This requirement arises because their unit cells are 10 times smaller than millimeter wavelengths, with nanogap regions that are 1,000,000 times smaller. To address this challenge, we propose a rapid inverse design method for terahertz nanoresonators using physics-informed machine learning, specifically employing double deep Q-learning combined with an analytical model of the THz nanogap loop array. Through approximately 200,000 iterations in about 39 hours on a middle-level personal computer (CPU: 3.40 GHz, 6 cores, 12 threads, RAM: 16 GB, GPU: NVIDIA GeForce GTX 1050), our approach successfully identifies the optimal structure, resulting in an experimental electric field enhancement of 32,000 at 0.2 THz, 300% stronger than previous achievements. By leveraging our analytical model-based approach, we significantly reduce the computational resources required, providing a viable alternative to the impractical numerical simulation-based inverse design that was previously impractical

Memory-Efficient Fine-Tuning of Compressed Large Language Models via sub-4-bit Integer Quantization

Large language models (LLMs) face the challenges in fine-tuning and deployment due to their high memory demands and computational costs. While parameter-efficient fine-tuning (PEFT) methods aim to reduce the memory usage of the optimizer state during fine-tuning, the inherent size of pre-trained LLM weights continues to be a pressing concern. Even though quantization techniques are widely proposed to ease memory demands and accelerate LLM inference, most of these techniques are geared towards the deployment phase. To bridge this gap, this paper presents Parameter-Efficient and Quantization-aware Adaptation (PEQA) - a simple yet effective method that combines the advantages of PEFT with quantized LLMs. By updating solely the quantization scales, PEQA can be directly applied to quantized LLMs, ensuring seamless task transitions. Parallel to existing PEFT methods, PEQA significantly reduces the memory overhead associated with the optimizer state. Furthermore, it leverages the advantages of quantization to substantially reduce model sizes. Even after fine-tuning, the quantization structure of a PEQA-tuned LLM remains intact, allowing for accelerated inference on the deployment stage. We employ PEQA-tuning for task-specific adaptation on LLMs with up to 65 billion parameters. To assess the logical reasoning and language comprehension of PEQA-tuned LLMs, we fine-tune low-bit quantized LLMs using a instruction dataset. Our results show that even when LLMs are quantized to below 4-bit precision, their capabilities in language modeling, few-shot in-context learning, and comprehension can be resiliently restored to (or even improved over) their full-precision original performances with PEQA.Comment: Published at NeurIPS 2023. Camera-ready versio

Ga-doped Pt-Ni Octahedral Nanoparticles as a Highly Active and Durable Electrocatalyst for Oxygen Reduction Reaction

Bimetallic PtNi nanoparticles have been considered as a promising electrocatalyst for oxygen reduction reaction (ORR) in polymer electrolyte membrane fuel cells (PEMFCs) owing to their high catalytic activity. However, under typical fuel cell operating conditions, Ni atoms easily dissolve into the electrolyte, resulting in degradation of the catalyst and the membrane-electrode assembly (MEA). Here, we report gallium-doped PtNi octahedral nanoparticles on a carbon support (Ga-PtNi/C). The Ga-PtNi/C shows high ORR activity, marking an 11.7-fold improvement in the mass activity (1.24 A mgPt-1) and a 17.3-fold improvement in the specific activity (2.53 mA cm-2) compare to the commercial Pt/C (0.106 A mgPt-1 and 0.146 mA cm-2). Density functional theory calculations demonstrate that addition of Ga to octahedral PtNi can cause an increase in the oxygen intermediate binding energy, leading to the enhanced catalytic activity toward ORR. In a voltage-cycling test, the Ga-PtNi/C exhibits superior stability to PtNi/C and the commercial Pt/C, maintaining the initial Ni concentration and octahedral shape of the nanoparticles. Single cell using the Ga-PtNi/C exhibits higher initial performance and durability than those using the PtNi/C and the commercial Pt/C. The majority of the Ga-PtNi nanoparticles well maintain the octahedral shape without agglomeration after the single cell durability test (30,000 cycles). This work demonstrates that the octahedral Ga-PtNi/C can be utilized as a highly active and durable ORR catalyst in practical fuel cell applications