Improved Flood Insights: Diffusion-Based SAR to EO Image Translation

Driven by rapid climate change, the frequency and intensity of flood events are increasing. Electro-Optical (EO) satellite imagery is commonly utilized for rapid response. However, its utilities in flood situations are hampered by issues such as cloud cover and limitations during nighttime, making accurate assessment of damage challenging. Several alternative flood detection techniques utilizing Synthetic Aperture Radar (SAR) data have been proposed. Despite the advantages of SAR over EO in the aforementioned situations, SAR presents a distinct drawback: human analysts often struggle with data interpretation. To tackle this issue, this paper introduces a novel framework, Diffusion-Based SAR to EO Image Translation (DSE). The DSE framework converts SAR images into EO images, thereby enhancing the interpretability of flood insights for humans. Experimental results on the Sen1Floods11 and SEN12-FLOOD datasets confirm that the DSE framework not only delivers enhanced visual information but also improves performance across all tested flood segmentation baselines.Comment: 10 pages, 6 figure

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

Artemisia iwayomogi

The objective of the present study was to determine whether Artemisia iwayomogi (AI) extract reduces visceral fat accumulation and obesity-related biomarkers in mice fed a high-fat diet (HFD), and if so, whether these effects are exerted by modulation of the expression of genes associated with adipogenesis and inflammation. AI extract supplementation for 11 weeks significantly prevented HFD-induced increments in body weight, visceral adiposity, adipocyte hypertrophy, and plasma levels of lipids and leptin. Additionally, AI extract supplementation resulted in downregulation of adipogenic transcription factors (PPARγ2 and C/EBPα) and their target genes (CD36, aP2, and FAS) in epididymal adipose tissue compared to the HFD alone. The AI extract effectively reversed the HFD-induced elevations in plasma glucose and insulin levels and the homeostasis model assessment of insulin resistance index. Furthermore, the extract significantly decreased gene expression of proinflammatory cytokines (TNFα, MCP1, IL-6, IFNα, and INFβ) in epididymal adipose tissue and reduced plasma levels of TNFα and MCP1 as compared to HFD alone. In conclusion, these results suggest that AI extract may prevent HFD-induced obesity and metabolic disorders, probably by downregulating the expression of genes related to adipogenesis and inflammation in visceral adipose tissue

A Model Combining Forest Environment Images and Online Microclimate Data Instead of On-Site Measurements to Predict Phytoncide Emissions

In the existing phytoncide-prediction process, solar radiation and photosynthetically active radiation (PAR) are difficult microclimate factors to measure on site. We derived a phytoncide-prediction technique that did not require field measurements. Visual indicators extracted from forest images and statistical analysis were used to determine appropriate positioning for forest environment photography to improve the accuracy of the new phytoncide-prediction formula without using field measurements. Indicators were selected from the Automatic Mountain Meteorology Observation System (AMOS) of the Korea Forest Service to replace on-site measured climate data and the phytoncide-prediction equation was derived using them. Based on regression analyses, we found that forest density, leaf area, and light volume above the horizon could replace solar radiation and PAR. In addition, AMOS data obtained at 2 m altitudes yielded suitable variables to replace microclimate data measured on site. The accuracy of the new equation was highest when the surface area in the image accounted for 25% of the total. The new equation was found to have a higher prediction accuracy (71.1%) compared to that of the previous phytoncide-prediction equation (69.1%), which required direct field measurements. Our results allow the public to calculate and predict phytoncide emissions more easily in the future

Application of Technology to Develop a Framework for Predicting Power Output of a PV System Based on a Spatial Interpolation Technique: A Case Study in South Korea

To increase the accuracy of photovoltaic (PV) power prediction, meteorological data measured at a plant’s target location are widely used. If observation data are missing, public data such as automated synoptic observing systems (ASOS) and automatic weather stations (AWS) operated by the government can be effectively utilized. However, if the public weather station is located far from the target location, uncertainty in the prediction is expected to increase owing to the difference in distance. To solve this problem, we propose a power output prediction process based on inverse distance weighting interpolation (IDW), a spatial statistical technique that can estimate the values of unsampled locations. By demonstrating the proposed process, we tried to improve the prediction of photovoltaic power in random locations without data. The forecasting accuracy depends on the power generation forecasting model and proven case, but when forecasting is based on IDW, it is up to 1.4 times more accurate than when using ASOS data. Therefore, if measured data at the target location are not available, it was confirmed that it is more advantageous to use data predicted by IDW as substitute data than public data such as ASOS

Prediction of Natural Volatile Organic Compounds Emitted by Bamboo Groves in Urban Forests

Due to the COVID-19 outbreak, people in countries around the world including the United Kingdom, Denmark, Canada, and South Korea are seeking physiological and psychological healing by visiting forests as stay-at-home orders continue. NVOCs (natural volatile organic compounds), a major healing factor of forests, have several positive effects on human health. This study specifically researched the NVOC characteristics of bamboo groves. This study revealed that α-pinene, 3-carene, and camphene were observed to emit the most, and the largest amount of NVOC emitted was seen during the early morning and late afternoon within bamboo groves. Furthermore, NVOC emission was found to have normal correlations with temperature and humidity, and inverse correlations with solar radiation, PAR (photosynthetically active radiation), and wind speed. A regression analysis conducted to predict the effect of microclimate factors on NVOC emissions resulted in a regression equation with 82.9% explanatory power, finding that PAR, temperature, and humidity had a significant effect on NVOC emission prediction. In conclusion, this study investigated NVOC emission of bamboo groves, examined the relationship between NVOC emissions and microclimate factors, and derived a prediction equation of NVOC emissions to figure out bamboo groves’ forest healing effects. These results are expected to provide a basis for establishing more effective forest healing programs in bamboo groves

Fabrication and Investigation of Acid Functionalized CNT Blended Nanocomposite Hollow Fiber Membrane for High Filtration and Antifouling Performance in Ultrafiltration Process

In this study, we fabricated a nanocomposite polyethersulfone (PES) HF membrane by blending acid functionalized carbon nanotubes (FCNT) to address the issue of reduced membrane life, increased energy consumption, and operating costs due to low permeability and membrane fouling in the ultrafiltration process. Additionally, we investigated the effect of FCNT blending on the membrane in terms of the physicochemical properties of the membrane and the filtration and antifouling performance. The FCNT/PES nanocomposite HF membrane exhibited increased water permeance from 110.1 to 194.3 LMH/bar without sacrificing rejection performance and increased the flux recovery ratio from 89.0 to 95.4%, compared to a pristine PES HF membrane. This study successfully developed a high filtration and antifouling polymer-based HF membrane by blending FCNT. Furthermore, it was validated that blending FCNT into the membrane enhances the filtration and antifouling performance in the ultrafiltration process