Particulate matter (PM) pollution demands air filters that combine high efficiency with low pressure drop. Here, we report a self-powered electrostatic filter based on an electrospun cationic microfiber web of Chimassorb 944 (C-fiber). The C-fiber functions as a triboelectric nanogenerator (TENG), generating a surface charge density of 85.8 μC/m2 when paired with polytetrafluoroethylene (PTFE), which creates a strong electrostatic field for capturing sub-micron particles, including the most penetrating particle size (MPPS). As a result, the triboelectrically charged C-fiber filter maintains >80% filtration efficiency at a high wind speed of 60 cm/s, far exceeding uncharged mechanical filters (<20%) while retaining low air resistance. Kelvin probe force microscopy (KPFM) visualizes the surface-potential change after particle capture, and the gradual decay of TENG output provides a built-in indicator of dust loading. This strategy offers a promising platform for next-generation smart air purification systems.

This study experimentally evaluated the filter lifespan extension achieved by a retractable, built-in air purification system equipped with a self-cleaning rotating filter. Conventional fixed-type air purifiers commonly experience a rapid increase in pressure drop and non-uniform airflow distribution as dust accumulates on the filter surface, leading to degradation in long-term purification performance. To overcome these limitations, the proposed system incorporates a retractable filter module and a rotational dust-removal mechanism designed to maintain stable airflow and reduce particulate loading during extended operation. Experiments were carried out in a controlled 30 m3 residential-scale test chamber to compare filtration performance, pressure-drop characteristics, and particle removal efficiency between the self-cleaning retractable system and a conventional fixed-depth configuration. The results indicate that the rotating self-cleaning mode reduced pressure drop by up to 18.5% and improved PM2.5 removal efficiency by approximately 12.7% relative to fixed operation. Increasing the filter protrusion depth further enhanced airflow uniformity and expanded the clean-air coverage area, thereby delaying the onset of filter saturation. Overall, the findings demonstrate that the proposed retractable, built-in air purification system effectively suppresses pressure-drop rise, maintains purification efficiency, and extends usable filter life. These results confirm the system’s practical applicability for residential indoor-air-quality management and lay the foundation for future optimization and commercialization.

Freight-rate forecasting in the VLCC TD3C market remains challenged by abrupt regime shifts, pronounced volatility, and heterogeneity in real-time signals from oil prices, seaborne trade, vessel operations, and macroeconomic factors; these directly impact freight planning and chartering. This study presents a daily multivariate dataset with 4,267 samples covering 2014-02-01 to 2025-10-08, integrating crude benchmarks, fuel spreads, refinery margins, port congestion, inventory levels by region, plus detailed AIS-derived VLCC activity, speed, and operation states, scaled and split 80/10/10 for training, validation, and testing. The proposed framework combines a PyTorch Transformer—optimized using Optuna for d_model=128, 9 layers, 8 heads, a 14-day input window, and 5-day output—with Monte Carlo Dropout for uncertainty quantification. Diagnosis uses differential entropy and coefficient-of-variation to verify convergence with 90 separate runs, while a Kalman filter (Q=0.001, R=0.01) smooths the forecast trajectory and enhances temporal reliability. Experimental results show baseline Transformer achieves average MAE 5,259.4, MAPE 13.10%, and R²=0.74 across 1-5 day horizons, with volatility quality metrics declining at longer leads. Applying the Kalman filter reduces errors to MAE 4,326.1, MAPE 10.6%, and raises R² to 0.83; timing and extremity components of volatility quality scores are strengthened, providing a more robust basis for operational decisions. Monte Carlo backtesting for 82 Korean VLCCs over 598 trades finds the Kalman-smoothed strategy earns $108.5M (88.9% win rate, Sharpe ratio 0.83), substantially outperforming raw Transformer ($32.9M, 60.5%, 0.24) and random selection (near zero, 49.3%, 0.005). These results highlight the clear economic value added by calibrating uncertainty and post-processing forecasts, transforming predictive reliability into real-world freight portfolio improvement in the tanker market.

국내에는 현재 50,435척의 5톤 미만 어선이 존재하나, 국내외 법규상 이러한 선박에서 발생하는 선저폐수를 관리하기 위한 제 도적·기술적 장치가 마련되어 있지 않다. 이로 인해 무단으로 배출되는 선저폐수는 심각한 환경오염을 유발하고, 이에 따른 경제적 손실 도 발생한다. 이에 본 연구는 소형선박용 유수 분리 장치의 내부 유동 분포를 개선하기 위해 Filter case cover 형상을 개발하였다. 입구각 0°, 30°, 45°의 세 가지 형상을 대상으로 Computational Fluid Dynamics 해석을 수행하였으며, 45° 형상에서 균일한 속도 분포와 안정적 선회 유동이 형성되어 Filter 전체 면적을 효율적으로 활용하는 것을 확인하였다. 이후 상용 Filter 6종에 대해 단일 성능평가를 수행한 결과, 양 전하막 처리된 활성탄이 유성분 흡착에 효과적임을 확인하였다. 이를 기반으로 기계적 여과 필터와 활성탄 필터로 구성된 3단계 여과 시 스템을 제안하였으며, 30ppm 유수 혼합액을 이용한 실험에서 배출수의 유분 농도를 0ppm에 근접하게 낮추고, 장시간 운전 중에도 안정적 인 유량과 처리 특성을 유지하였다. 본 연구는 형상 및 필터 구성을 최적화하여 설계 효율이 향상된 소형선박용 유수 분리 시스템의 기초 기술을 제시하며, 국내 연안의 환경오염을 방지하고 지속 가능한 생태계를 조성하는데 기여할 수 있을 것으로 기대된다.

This paper introduces a simple and reliable photometric calibration method to extract Hα line flux from narrowband images. The equivalent width of the Hα line (EWHα) is derived using two- and simplified three-filter methods. Synthetic photometry of CALSPEC stars demonstrates the dependency of EWHα on the V − R color, described by a skewed Gaussian function within −0.1 < V − R < 0.7. Systematic errors of the two- and three-filter methods are analyzed under 0%–10% R-band flux contamination. Although the three-filter method underestimates EWHα by 10%, it exhibits less scatter compared to the two-filter method. The simplified three-filter method was validated with the Landolt SA 107 field and surpasses the two-filter method in terms of precision and accuracy. Additionally, applying our method to V960 Mon yields EWHα consistent with high-resolution spectroscopic results.

이수식 쉴드 TBM 공법에서 발생하는 부산물인 필터케이크를 유동성 채움재로의 재활용 가능성을 평가하기 위해 다양한 기초 실험을 수행하였다. 필터케이크를 굵은골재 및 잔골재와 혼합하여, 필터케이크의 함량 비율을 증가시키면서 세 가 지 배합(Case 1, Case 2, Case 3)을 구성하였다. 강도 발현을 위한 바인더로는 보통 포틀랜드 시멘트를 사용하였으며, 물- 시멘트비(w/c)를 변화시켜 플로우 시험, 블리딩 시험, 압축강도 시험을 통해 유동성 채움재로서의 가능성을 평가하였다. 시험 결과, 필터케이크 함량이 증가할수록 혼합물의 유동성은 저하되었으며, 이를 보완하기 위해 혼합수의 양을 증가시 키며 적정한 범위의 유동성을 확보하도록 하였으나, 혼합수의 양이 많아질수록 압축강도가 크게 감소하는 경향을 보였 다. 특히, 필터케이크 함량이 가장 높은 Case 3에서는 이러한 현상이 두드러지게 나타났으며, 반면 필터케이크 함량이 적 은 Case 1에서는 상대적으로 높은 강도가 발현되었다. 또한, 필터케이크 함량이 적을수록 혼합물의 유동성은 혼합수량에 민감하게 변화하였다. 블리딩은 필터케이크의 혼합 비율에 영향을 받았으며, 필터케이크 함량이 가장 높은 Case 3에서 블리딩이 가장 적게 발생하였다. 이는 필터케이크의 높은 수분 흡수율이 블리딩 감소에 영향을 미친 것으로 판단된다. 즉, 유동성, 강도, 블리딩 사이의 균형을 맞추기 위한 적절한 배합비 설정을 통해 TBM 공법 부산물인 필터케이크는 유 동성 채움재로 재활용 가능성이 높을 것으로 평가하였다.

Accurate seismic vulnerability assessment requires high quality and large amounts of ground motion data. Ground motion data generated from time series contains not only the seismic waves but also the background noise. Therefore, it is crucial to determine the high-pass cut-off frequency to reduce the background noise. Traditional methods for determining the high-pass filter frequency are based on human inspection, such as comparing the noise and the signal Fourier Amplitude Spectrum (FAS), f2 trend line fitting, and inspection of the displacement curve after filtering. However, these methods are subject to human error and unsuitable for automating the process. This study used a deep learning approach to determine the high-pass filter frequency. We used the Mel-spectrogram for feature extraction and mixup technique to overcome the lack of data. We selected convolutional neural network (CNN) models such as ResNet, DenseNet, and EfficientNet for transfer learning. Additionally, we chose ViT and DeiT for transformer-based models. The results showed that ResNet had the highest performance with R2 (the coefficient of determination) at 0.977 and the lowest mean absolute error (MAE) and RMSE (root mean square error) at 0.006 and 0.074, respectively. When applied to a seismic event and compared to the traditional methods, the determination of the high-pass filter frequency through the deep learning method showed a difference of 0.1 Hz, which demonstrates that it can be used as a replacement for traditional methods. We anticipate that this study will pave the way for automating ground motion processing, which could be applied to the system to handle large amounts of data efficiently.