Simple Baseline for Weather Forecasting Using Spatiotemporal Context Aggregation Network

Traditional weather forecasting relies on domain expertise and computationally intensive numerical simulation systems. Recently, with the development of a data-driven approach, weather forecasting based on deep learning has been receiving attention. Deep learning-based weather forecasting has made stunning progress, from various backbone studies using CNN, RNN, and Transformer to training strategies using weather observations datasets with auxiliary inputs. All of this progress has contributed to the field of weather forecasting; however, many elements and complex structures of deep learning models prevent us from reaching physical interpretations. This paper proposes a SImple baseline with a spatiotemporal context Aggregation Network (SIANet) that achieved state-of-the-art in 4 parts of 5 benchmarks of W4C22. This simple but efficient structure uses only satellite images and CNNs in an end-to-end fashion without using a multi-model ensemble or fine-tuning. This simplicity of SIANet can be used as a solid baseline that can be easily applied in weather forecasting using deep learning.Comment: 1st place solution for stage1 and Core Transfer in the Weather4Cast competition on NeurIPS 2

Domain Generalization Strategy to Train Classifiers Robust to Spatial-Temporal Shift

Deep learning-based weather prediction models have advanced significantly in recent years. However, data-driven models based on deep learning are difficult to apply to real-world applications because they are vulnerable to spatial-temporal shifts. A weather prediction task is especially susceptible to spatial-temporal shifts when the model is overfitted to locality and seasonality. In this paper, we propose a training strategy to make the weather prediction model robust to spatial-temporal shifts. We first analyze the effect of hyperparameters and augmentations of the existing training strategy on the spatial-temporal shift robustness of the model. Next, we propose an optimal combination of hyperparameters and augmentation based on the analysis results and a test-time augmentation. We performed all experiments on the W4C22 Transfer dataset and achieved the 1st performance.Comment: Core Transfer Track 1st place solution in Weather4Cast competition at NeuIPS2

An Advanced Operational Approach for Tropical Cyclone Center Estimation Using Geostationary-Satellite-Based Water Vapor and Infrared Channels

Tropical cyclones (TCs) are destructive natural disasters. Accurate prediction and monitoring are important to mitigate the effects of natural disasters. Although remarkable efforts have been made to understand TCs, operational monitoring information still depends on the experience and knowledge of forecasters. In this study, a fully automated geostationary-satellite-based TC center estimation approach is proposed. The proposed approach consists of two improved methods: the setting of regions of interest (ROI) using a score matrix (SCM) and a TC center determination method using an enhanced logarithmic spiral band (LSB) and SCM. The former enables prescreening of the regions that may be misidentified as TC centers during the ROI setting step, and the latter contributes to the determination of an accurate TC center, considering the size and length of the TC rainband in relation to its intensity. Two schemes, schemes A and B, were examined depending on whether the forecasting data or real-time observations were used to determine the initial guess of the TC centers. For each scheme, two models were evaluated to discern whether SCM was combined with LSB for TC center determination. The results were investigated based on TC intensity and phase to determine the impact of TC structural characteristics on TC center determination. While both proposed models improved the detection performance over the existing approach, the best-performing model (i.e., LSB combined with SCM) achieved skill scores (SSs) of +17.4% and +20.8% for the two schemes. In particular, the model resulted in a significant improvement for strong TCs (categories 4 and 5), with SSs of +47.8% and +72.8% and +41.2% and +72.3% for schemes A and B, respectively. The research findings provide an improved understanding of the intensity- and phase-wise spatial characteristics of TCs, which contributes to objective TC center estimation

Efficient and Stable Blue- and Red-Emitting Perovskite Nanocrystals through Defect Engineering: PbX2 Purification

Current efforts to reduce the density of structural defects such as surface passivation, doping, and modified synthetic protocols have allowed us to grow high-quality perovskite nanocrystals (PNCs). However, the role of the purity of the precursors involved during the PNC synthesis to hinder the emergence of defects has not been widely explored. In this work, we analyzed the use of different crystallization processes of PbX2 (X = Cl– or I–) to purify the chemicals and produce highly luminescent and stable CsPbCl3–xBrx and CsPbI3 PNCs. The use of a hydrothermal (Hyd) process to improve the quality of the as-prepared PbCl2 provides blue-emitting PNCs with efficient ligand surface passivation, a maximum photoluminescence quantum yield (PLQY) of ∼ 88%, and improved photocatalytic activity to oxidize benzyl alcohol, yielding 40%. Then, the hot recrystallization of PbI2 prior to Hyd treatment led to the formation of red-emissive PNCs with a PLQY of up to 100%, long-term stability around 4 months under ambient air, and a relative humidity of 50–60%. Thus, CsPbI3 light-emitting diodes were fabricated to provide a maximum external quantum efficiency of up to 13.6%. We claim that the improvement of the PbX2 crystallinity offers a suitable stoichiometry in the PNC structure, reducing nonradiative carrier traps and so maximizing the radiative recombination dynamics. This contribution gives an insight into how the manipulation of the PbX2 precursor is a profitable and potential alternative to synthesize PNCs with improved photophysical features by making use of defect engineering

Traditional Herbal Formula Banhasasim-tang

Banhasasim-tang (BHSST) is a Korean traditional herbal formula comprising eight medicinal herbs. The aim of the present study was to investigate the anti-inflammatory effect of BHSST using macrophage and keratinocyte cell lines. First, we evaluated the effects of BHSST on inflammatory mediator and cytokine production in lipopolysaccharide- (LPS-) stimulated RAW 264.7 macrophages. BHSST markedly inhibited the production of nitric oxide (NO), prostaglandin E2 (PGE2), and interleukin- (IL-) 6. BHSST significantly suppressed the protein expression of toll-like receptor 4 (TLR4) and phosphorylated nuclear factor-kappa B (NF-κB) p65 in RAW 264.7 cells. Second, we examined whether BHSST influences the production of chemokines and STAT1 phosphorylation in tumor necrosis factor-α/interferon-γ TI-stimulated HaCaT keratinocytes. BHSST significantly suppressed the production of RANTES/CCL5, TARC/CCL17, MDC/CCL22, and IL-8 in TI-stimulated HaCaT cells. BHSST also suppressed TI-induced phosphorylation of STAT1 in HaCaT cells. These results suggest that BHSST may be useful as an anti-inflammatory agent, especially for inflammatory skin diseases

LONP1 and ClpP cooperatively regulate mitochondrial proteostasis for cancer cell survival

Mitochondrial proteases are key components in mitochondrial stress responses that maintain proteostasis and mitochondrial integrity in harsh environmental conditions, which leads to the acquisition of aggressive phenotypes, including chemoresistance and metastasis. However, the molecular mechanisms and exact role of mitochondrial proteases in cancer remain largely unexplored. Here, we identified functional crosstalk between LONP1 and ClpP, which are two mitochondrial matrix proteases that cooperate to attenuate proteotoxic stress and protect mitochondrial functions for cancer cell survival. LONP1 and ClpP genes closely localized on chromosome 19 and were co-expressed at high levels in most human cancers. Depletion of both genes synergistically attenuated cancer cell growth and induced cell death due to impaired mitochondrial functions and increased oxidative stress. Using mitochondrial matrix proteomic analysis with an engineered peroxidase (APEX)-mediated proximity biotinylation method, we identified the specific target substrates of these proteases, which were crucial components of mitochondrial functions, including oxidative phosphorylation, the TCA cycle, and amino acid and lipid metabolism. Furthermore, we found that LONP1 and ClpP shared many substrates, including serine hydroxymethyltransferase 2 (SHMT2). Inhibition of both LONP1 and ClpP additively increased the amount of unfolded SHMT2 protein and enhanced sensitivity to SHMT2 inhibitor, resulting in significantly reduced cell growth and increased cell death under metabolic stress. Additionally, prostate cancer patients with higher LONP1 and ClpP expression exhibited poorer survival. These results suggest that interventions targeting the mitochondrial proteostasis network via LONP1 and ClpP could be potential therapeutic strategies for cancer

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Department of Urban and Environmental Engineering (Environmental Science and Engineering)clos

Searching for New Human Behavior Model in the Climate Change Age: Analyzing the Impact of Risk Perception and Government Factors on Intention–Action Consistency in Particulate Matter Mitigation

This study aims to analyze factors influencing citizens’ intentions to take protective action against particulate matter (PM) and their actual actions in response to PM. There were few research on the role of government factors and the issue of intention–action inconsistency in the context of PM mitigation action. Therefore, this study set not only variables in the risk perception paradigm but also ones in government factors as independent variables, while intention and action in response to PM were set as dependent variables. This study’s analysis was based on survey data collected from Korean people. For representativeness of the samples, this study adopted the quota sampling method, considering region, gender, and age. Five hundred respondents finished the survey. To verify the hypotheses, this study used regression and binomial logistic analysis. Analysis showed that (1) negative emotions, trust, knowledge, government competency, policy satisfaction, and policy awareness had significant effects on intention and action in response to PM, and (2) perceived benefits only affected intention, whereas government accountability only affected action. Logistic analysis showed that there were groups in which intentions and actions did not match. Negative emotions and government competence induce intention–action consistency, whereas the perceived benefits and trust in government tend to encourage inconsistency. Knowledge is a variable that induces both consistency and inconsistency in the intention–action relationship. The determinant structures of independent variables affecting the likelihood of belonging to the four groups differed

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Precipitation is one of the main factors that affect water and energy cycles, and its estimation plays a very important role in securing water resources and timely responding to water disasters. Satellitebased quantitative precipitation estimation (QPE) has the advantage of covering large areas at high spatiotemporal resolution. In this study, machine learning-based rainfall intensity models were developed using Himawari-8 Advanced Himawari Imager (AHI) water vapor channel (6.7 ??m), infrared channel (10.8 ??m), and weather radar Column Max (CMAX) composite data based on random forest (RF). The target variables were weather radar reflectivity (dBZ) and rainfall intensity (mm/hr) converted by the ZR relationship. The results showed that the model which learned CMAX reflectivity produced the Critical Success Index (CSI) of 0.34 and the Mean-Absolute-Error (MAE) of 4.82 mm/hr. When compared to the GeoKompsat-2 and Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN)-Cloud Classification System (CCS) rainfall intensity products, the accuracies improved by 21.73% and 10.81% for CSI, and 31.33% and 23.49% for MAE, respectively. The spatial distribution of the estimated rainfall intensity was much more similar to the radar data than the existing products