QIENet: Quantitative irradiance estimation network using recurrent neural network based on satellite remote sensing data

Global horizontal irradiance (GHI) plays a vital role in estimating solar energy resources, which are used to generate sustainable green energy. In order to estimate GHI with high spatial resolution, a quantitative irradiance estimation network, named QIENet, is proposed. Specifically, the temporal and spatial characteristics of remote sensing data of the satellite Himawari-8 are extracted and fused by recurrent neural network (RNN) and convolution operation, respectively. Not only remote sensing data, but also GHI-related time information (hour, day, and month) and geographical information (altitude, longitude, and latitude), are used as the inputs of QIENet. The satellite spectral channels B07 and B11 - B15 and time are recommended as model inputs for QIENet according to the spatial distributions of annual solar energy. Meanwhile, QIENet is able to capture the impact of various clouds on hourly GHI estimates. More importantly, QIENet does not overestimate ground observations and can also reduce RMSE by 27.51%/18.00%, increase R2 by 20.17%/9.42%, and increase r by 8.69%/3.54% compared with ERA5/NSRDB. Furthermore, QIENet is capable of providing a high-fidelity hourly GHI database with spatial resolution 0.02{\deg} * 0.02{\deg}(approximately 2km * 2km) for many applied energy fields

Detection method of wind speed anomaly fluctuation based on SSA−LSTM

Aiming at the problem of high leakage rate and false alarm rate of traditional statistical methods for abnormal fluctuation in sensor monitoring data caused by dampers opening and closing, a SSA-LSTM wind speed abnormal fluctuation detection method based on the combination of Singular Spectrum Analysis (SSA) and Long and Short-Term Memory Neural Network (LSTM) was proposed by mining the data features in the time-series data in the wind speed sensors. Firstly, SSA was used to pre-process the wind speed sensor monitoring data, and the wind speed data was decomposed into trend component, periodic component and noise component. The data noise generated by turbulent pulsation was removed via reorganizing the trend component and noise component. The LSTM parameters was then optimized, and the optimized LSTM model was used to predict the pre-processed data and obtain the reconstructed wind speed. Finally, the anomaly fraction of the monitored wind speed and reconstructed wind speed was calculated by using the logarithmic probability density function. Anomaly detection for monitoring wind speed was performed by calculating the threshold set value of training set data samples. The experimental results shown that, the removing effect for the data noise generated by turbulence pulsation via SSA was better. Removing the noise component without affecting the data fluctuation was helpful in improving the wind speed reconstruction effect and the anomaly detection accuracy. LSTM can correctly reconstruct the small amplitude wave due to turbulence pulsation without anomalous fluctuation and fits well with the actual data. The reconstruction of abnormal fluctuation segment based on historical fluctuation trend when there was abnormal fluctuation can effectively improve the accuracy of anomaly detection. Through comparative analysis, the reconstruction effect of proposed method in this paper was better than ARIMA, BP and CNN models, with an anomaly detection accuracy of 99.2% and an F1-Score of 0.97, which verified the reliability of the proposed method. The method proposed in the paper has important application value in detecting the abnormal fluctuation of wind speed caused by the opening and closing of dampers

Characterization of ultra-deeply buried middle Triassic Leikoupo marine carbonate petroleum system (!) in the Western Sichuan depression, China

Ultra-deeply buried (>5000 m) marine carbonate reservoirs have gradually become important exploration targets. This research focuses on providing an understanding of the basic elements of the ultra-deeply buried Middle Triassic Leikoupo marine carbonate petroleum system within the Western Sichuan Depression, China. Comprehensive analyses of organic geochemistry, natural gas, and C–H–He–Ne–Ar isotope compositions suggest that the reservoir is charged with compound gases from four source rock units including the Permian Longtan, Middle Triassic Leikoupo, Late Triassic Maantang and Xiaotangzi formations. Approximately a 50-m thick outcrop and 100-m length of drilling cores were examined in detail, and 108 samples were collected from six different exploration wells in order to conduct petrographic and petrophysical analyses. Thin-section and scanning electron microscope (SEM) observations, helium porosity and permeability measurements, mercury injection capillary pressure (MICP) analysis, and wire-line logging (5,500–6,900 m) indicate that the reservoir lithologies include argillaceous algal limestones, dolograinstones, crystalline dolostones, and microbially-derived stromatolitic and thrombolitic dolostones. Reservoir properties exhibit extreme heterogeneity due to different paleogeographic environmental controls and mutual interactions between constructive (e.g., epigenetic paleo-karstification, burial dissolution, structural movement, pressure-solution and dolomitization) and destructive (e.g., physical/chemical compaction, cementation, infilling, recrystallization, and replacement) diagenetic processes. An unconformity-related epigenetic karstification zone was identified in the uppermost fourth member of the Leikoupo Formation, which has developed secondary solution-enhanced pores, vugs, and holes that resulted in higher porosity (1.8–14.2%) and permeability (0.2–7.7 mD). The homogeneity and tightness of the reservoir increases with depth below the unconformity, and it is characterized by primary intergranular and intracrystalline pores, solution pores, fractures, stylolites, and micropores with a lower helium porosity (0.6–4.1%) and permeability (0.003–125.2 mD). Regional seals consist of the Late Triassic Xujiahe Formation, comprised of ~300 m of mudstones that are overlain by ~5,000-m thick of Jurassic to Quaternary continental argillaceous overburden rocks. Effective traps are dominated by a combination of structural-stratigraphic types. Paleo- reservoir crude oil cracking, wet-gases, and dry-gases from three successive hydrocarbon generation processes supplied the sufficient hydrocarbon resources. The homogenization temperatures of the hydrocarbon-associated aqueous fluid inclusions range from 98–130 °C and 130–171 °C, which suggests hydrocarbon charging occurred between 220–170 Ma and 130–90 Ma, respectively. One-dimensional basin evolution models combined with structural geologic and seismic profiles across wells PZ1-XQS1-CK1-XCS1-TS1 show that hydrocarbon migration and entrapment mainly occurred via the unconformity and interconnected fault-fracture networks with migration and charging driven by formation overpressure, abnormal fluid flow pressure, and buoyancy forces during the Indosinian and Yanshanian orogenies, with experiencing additional transformation occurring during the Himalayan orogeny. The predicted estimated reserves reached ~300 × 109 m3. The results provide excellent scientific implications for similar sedimentary basin studies, it is believed that abundant analogous deeply buried marine carbonate hydrocarbon resources yet to be discovered in China and elsewhere worldwide in the near future

SHP2 Regulates the Osteogenic Fate of Growth Plate Hypertrophic Chondrocytes

Transdifferentiation of hypertrophic chondrocytes into bone-forming osteoblasts has been reported, yet the underlying molecular mechanism remains incompletely understood. SHP2 is an ubiquitously expressed cytoplasmic protein tyrosine phosphatase. SHP2 loss-of-function mutations in chondroid cells are linked to metachondromatosis in humans and mice, suggesting a crucial role for SHP2 in the skeleton. However, the specific role of SHP2 in skeletal cells has not been elucidated. To approach this question, we ablated SHP2 in collagen 2α1(Col2α1)-Cre- and collagen 10α1(Col10α1)-Cre-expressing cells, predominantly proliferating and hypertrophic chondrocytes, using “Cre-loxP”-mediated gene excision. Mice lacking SHP2 in Col2α1-Cre-expressing cells die at mid-gestation. Postnatal SHP2 ablation in the same cell population caused dwarfism, chondrodysplasia and exostoses. In contrast, mice in which SHP2 was ablated in the Col10α1-Cre-expressing cells appeared normal but were osteopenic. Further mechanistic studies revealed that SHP2 exerted its influence partly by regulating the abundance of SOX9 in chondrocytes. Elevated and sustained SOX9 in SHP2-deficient hypertrophic chondrocytes impaired their differentiation to osteoblasts and impaired endochondral ossification. Our study uncovered an important role of SHP2 in bone development and cartilage homeostasis by influencing the osteogenic differentiation of hypertrophic chondrocytes and provided insight into the pathogenesis and potential treatment of skeletal diseases, such as osteopenia and osteoporosis

Multilevel defects in the hematopoietic niche in essential thrombocythemia

The role of the bone marrow niche in essential thrombocythemia (ET) remains unclear. Here, we observed multilevel defects in the hematopoietic niche of patients with JAK2V617F-positive ET, including functional deficiency in mesenchymal stromal cells (MSC), immune imbalance, and sympathetic-nerve damage. Mesenchymal stromal cells from patients with JAK2V617F-positive essential thrombocythemia had a transformed transcriptome. In parallel, they showed enhanced proliferation, decreased apoptosis and senescence, attenuated ability to differentiate into adipocytes and osteocytes, and insufficient support for normal hematopoiesis. Additionally, they were inefficient in suppressing immune responses. For instance, they poorly inhibited proliferation and activation of CD4-positive T cells and the secretion of the inflammatory factor soluble CD40-ligand. They also poorly induced formation of mostly immunosuppressive T-helper 2 cells (Th2) and the secretion of the anti-inflammatory factor interleukin-4 (IL-4). Furthermore, we identified WDR4 as a potent protein with low expression and which was correlated with increased proliferation, reduced senescence and differentiation, and insufficient support for normal hematopoiesis in MSC from patients with JAK2V617F-positive ET. We also observed that loss of WDR4 in MSC cells downregulated the interleukin-6 (IL-6) level through the ERK–GSK3β–CREB signaling based on our in vitro studies. Altogether, our results show that multilevel changes occur in the bone marrow niche of patients with JAK2V617F-positive ET, and low expression of WDR4 in MSC may be critical for inducing hematopoietic related changes

QIENet: Quantitative irradiance estimation network using recurrent neural network based on satellite remote sensing data

Global horizontal irradiance (GHI) plays a vital role in estimating solar energy resources, which are used to generate sustainable green energy. In order to estimate GHI with high spatial resolution, a quantitative irradiance estimation network, named QIENet, is proposed. Specifically, the temporal and spatial characteristics of remote sensing data of the satellite Himawari-8 are extracted and fused by recurrent neural network (RNN) and convolution operation, respectively. Not only remote sensing data, but also GHI-related time information (hour, day, and month) and geographical information (altitude, longitude, and latitude), are used as the inputs of QIENet. The satellite spectral channels B07 and B11–B15 and time are recommended as model inputs for QIENet according to the spatial distributions of annual solar energy. Meanwhile, QIENet is able to capture the impact of various clouds on hourly GHI estimates. More importantly, QIENet does not overestimate ground observations and can also reduce RMSE by 27.51%/18.00%, increase R2 by 20.17%/9.42%, and increase r by 8.69%/3.54% compared with ERA5/NSRDB. Furthermore, QIENet is capable of providing a high-fidelity hourly GHI database with spatial resolution 0.02°×0.02° (approximately 2km×2km) for many applied energy fields