A Segmented Cross-Correlation Algorithm for Dynamic North Finding Using Fiber Optic Gyroscopes.

Fiber optic gyroscope (FOG)-based north finding is extensively applied in navigation, positioning, and various fields. In dynamic north finding, an accelerated turntable speed shortens the time required for north finding, resulting in a rapid north-finding response. However, with an increase in turntable speed, the turntable's jitter contributes to signal contamination in the FOG, leading to a deterioration in north-finding accuracy. This paper introduces a divide-and-conquer algorithm, the segmented cross-correlation algorithm, designed to mitigate the impact of turntable speed jitter. A model for north-finding error is established and analyzed, incorporating FOG's self-noise and the turntable's speed jitter. To validate the feasibility of our method, we implemented the algorithm on a FOG. The simulation and experimental results exhibited a strong concordance, affirming the validity of our proposed north-finding error model. The experimental findings indicate that, at a turntable speed of 180°/s, the north-finding bias error within a 360 s duration is 0.052°, representing a 64% improvement over the traditional algorithm. These results indicate the effectiveness of the proposed algorithm in mitigating the impact of unstable turntable speeds, offering a solution for north finding with both prompt response and enhanced accuracy.

Converting coastal silt into subgrade soil with biochar as reinforcing agent, CO2 adsorbent, and carbon sequestrating material.

Large amounts of coastal silt produced annually is urgent to be treated with a feasible strategy. This study converted it into subgrade soil by cement solidification for resource utilization. Biochar was used as exogenous additive for enhancing compressive strength of the product, simultaneously achieving carbon sequestration. Three biochars derived from peanut shells (PSBC), cow dung (CDBC) and sewage sludge (SSBC) at 300 °C, 500 °C and 700 °C pyrolysis, were added into raw materials with 1%, 2% and 5%, respectively. All biochars significantly improved the compressive strength of the subgrade soil by 20-110%. Biochar catalyzed cement hydration reactions to produce more Ca(OH)2, CaCO3 and calcium silicate hydrates (C-S-H gel). The catalytic capacity of different biochars followed the order of SSBC > PSBC > CDBC. Addition of 2% SSBC500 induced the greatest increase in 28 d-strength from only 1.0 MPa-2.1 MPa, which was due to that 500 °C biochar had a suitable specific surface area and porosity. Biochar facilitated CO2 capture (absorption) during the hydration reactions at the initial 48 h with 55-70 mg g-1. The high alkalinity and water holding capacity of biochar contributed to the absorption of CO2; the high content of minerals in SSBC compared to CDBC and PSBC promoted chemical conversion of CO2 to carbonate. Besides, the biochar itself as carbon rich material was encapsulated in the subgrade soil, which can be regarded as a long-term carbon sequestration strategy. Carbon budget analysis demonstrated that converting one ton dry silt into subgrade soil with addition of 2% biochar could increase CO2 sequestration from 11 kg to 36-94 kg. This study proposes a novel strategy of using biochar to strengthen the subgrade soil simultaneously achieve long-term carbon sequestration.

Biochar as additive for improved building performances and heavy metals solidification of sediment-based lightweight concrete.

The sustainable disposal of large volumes of contaminated dredged river sediment has become a challenge for municipal management. In this study, a cutting-edge biochar application method was innovated, which converted the polluted dredged sediment into a low-carbon and environmentally friendly building material through an autoclave-free method. As the amount of biochar addition increased from 0 to 2% (w/w), the compressive strength of the dredged sediment-based lightweight concrete (DS-LC) increased from 3.92 to 4.61 MPa. Accordingly, the thermal conductivity decreased from 0.237 to 0.222 W/(m K), the water absorption decreased by 6%, and the water resistance coefficient increased by 33%. Results of X-ray diffraction (XRD) and thermogravimetric (TG) analysis showed that biochar promoted the hydration reaction and the carbonation process. Scanning electron microscopy (SEM) attached with energy-dispersive X-ray spectroscopy (EDX) showed that biochar addition changed the microstructure of the DS-LCs, which made the pore distribution more uniform and densified. Biochar addition also strengthened the immobilization of heavy metals (Cu, Zn, Cr, and As) by approximately 18-27% and combination of biochar and silica fume could increase the heavy metal immobilization by 28-44%. Compared with the traditional concrete material, the DS-LC with biochar addition could not only reduce the carbon emission but also has potential economic benefit for the treatment and utilization of dredged sediment.

DESA: a novel hybrid decomposing-ensemble and spatiotemporal attention model for PM2.5 forecasting.

Exposure to fine particulate matter can easily lead to health issues. PM2.5 concentrations are associated with various spatiotemporal factors, which makes the prediction of PM2.5 concentrations still a challenging task. One of the reasons that makes the accurate prediction by statistical learning method difficult is severe fluctuations in input data. In addition, the abstraction method of space will also affect the prediction results. To address these important issues, a novel hybrid decomposing-ensemble and spatiotemporal attention (DESA) model is proposed to improve the prediction accuracy by decomposing the mode-mixed time series into single-mode series and automatically assign weights to the spatiotemporal factors. In our proposed framework, raw PM2.5 series are firstly decomposed into simple sub-series via the complete ensemble empirical mode decomposition (CEEMD) method. Then, to keep the results independent of the spatial abstraction method, a data-driven approach called multiscale spatiotemporal attention network is employed to extract spatiotemporal features from the sub-series. Finally, the predictions of each sub-series are processed separately and combined to obtain the final prediction results. The experimental results indicate that the proposed model achieved the better performance with RMSE of 11.15, 17.49, 24.84, and 26.93 for 6-, 12-, 24-, and 36-h forecasting, respectively. The proposed method is expected to be applied in fine prediction of air pollution and controlling programs and therefore provide decision support or useful guidance.

The relationship between air quality and MODIS aerosol optical depth in major cities of the Yangtze River Delta.

The AOD derived from the MODIS deep blue(DB) algorithm and AQI were used to investigate the correlation between AOD and AQI in seven major cities of Yangtze River Delta (YRD) from January to December 2019. The accuracy of MODIS AOD was validated by AERONET. Moreover, the AOD and AQI were studied to explore the annual and seasonal distribution characteristics, and the correlation analysis was carried out using five regression models. It was found: Ⅰ) There was a significant correlation between AOD and AERONET data (R2 ˃ 0.80, RMSE = 0.106, and MAE = 0.089). Ⅱ) The highest AQI was observed in winter (83), followed by spring (76), autumn (74), and summer (72). Ⅲ) The monthly average AOD showed noticeable seasonal variations, which reached the highest in summer (0.91) and the lowest in winter (0.69), followed by spring and autumn. Ⅳ) Among the five models, the cubic model obtained the best results with R2 ˃ 0.55. In the sub-seasonal regression model, the cubic model outperformed other models in spring (R2 ˃ 0.57), summer (R2 ˃ 0.76) and autumn (R2 ˃ 0.38). However, in winter the composite model outperformed others (R2 ˃ 0.68). Ⅴ) Considering annual data, the AOD can predict over 70% of the variations in AQI (0.41＜R2 ＜0.81). These results demonstrate the feasibility of AOD derived from the MODIS DB algorithm in AQI prediction. The method used in this study can be applied as an aid for air pollution control programs in different regions.