Detailed quantitative description of fluvial reservoirs: A case study of L6-3 Layer of Sandgroup 6 in the second member of Shahejie Formation, Shengtuo Oilfield, China

 The steady development of the oil field is facing severe challenges due to the problems of small-layer division, unclear genesis period and unclear river channel distribution in the 4-6 sand formation in the second district of Shengtuo Oilfield. Based on the processing and optimization of logging data, this paper firstly divided the isochronous strata and established the high-resolution isochronous stratigraphic framework. Using the geo-statistics method in the stratigraphic framework, the sand bodies in each small layer were divided according to the principle of equal time of fluvial facies. On this basis, the distribution pattern of the sand bodies in each stage was simulated by the magnetic random walk model. The magnetic random walk model has obtained robust simulation results, which is consistent with the anatomy of reservoir architectures by experienced geologists. The results also show that the number of channels in each small-layer is different, while the overall distribution of NE direction is reflected. At present, the model can well simulate the position of the main channel line, but it cannot reflect the variation of the river width. The method of quantitative fine description based on logging data has great potential application in fluvial reservoir, especially the magnetic random walk model that can reveal the distribution of sand body in every stage. At the same time, the model can also reflect certain randomness and facilitate the uncertainty analysis of geological factors.Cited as: Li, J., Yan, K., Ren, H., Sun, Z. Detailed quantitative description of fluvial reservoirs: A case study of L6-3 Layer of Sandgroup 6 in the second member of Shahejie Formation, Shengtuo Oilfifield, China. Advances in Geo-Energy Research, 2020, 4(1): 43-53, doi: 10.26804/ager.2020.01.0

Large Trajectory Models are Scalable Motion Predictors and Planners

Motion prediction and planning are vital tasks in autonomous driving, and recent efforts have shifted to machine learning-based approaches. The challenges include understanding diverse road topologies, reasoning traffic dynamics over a long time horizon, interpreting heterogeneous behaviors, and generating policies in a large continuous state space. Inspired by the success of large language models in addressing similar complexities through model scaling, we introduce a scalable trajectory model called State Transformer (STR). STR reformulates the motion prediction and motion planning problems by arranging observations, states, and actions into one unified sequence modeling task. With a simple model design, STR consistently outperforms baseline approaches in both problems. Remarkably, experimental results reveal that large trajectory models (LTMs), such as STR, adhere to the scaling laws by presenting outstanding adaptability and learning efficiency. Qualitative results further demonstrate that LTMs are capable of making plausible predictions in scenarios that diverge significantly from the training data distribution. LTMs also learn to make complex reasonings for long-term planning, without explicit loss designs or costly high-level annotations

Identification and classification of the genomes of novel microviruses in poultry slaughterhouse

Microviridae is a family of phages with circular ssDNA genomes and they are widely found in various environments and organisms. In this study, virome techniques were employed to explore potential members of Microviridae in a poultry slaughterhouse, leading to the identification of 98 novel and complete microvirus genomes. Using a similarity clustering network classification approach, these viruses were found to belong to at least 6 new subfamilies within Microviridae and 3 higher-level taxonomic units. Genome size, GC content and genome structure of these new taxa showed evident regularities, validating the rationality of our classification method. Our method can divide microviruses into about 45 additional detailed clusters, which may serve as a new standard for classifying Microviridae members. Furthermore, by addressing the scarcity of host information for microviruses, the current study significantly broadened their host range and discovered over 20 possible new hosts, including important pathogenic bacteria such as Helicobacter pylori and Vibrio cholerae, as well as different taxa demonstrated different host specificities. The findings of this study effectively expand the diversity of the Microviridae family, providing new insights for their classification and identification. Additionally, it offers a novel perspective for monitoring and controlling pathogenic microorganisms in poultry slaughterhouse environments

Investigation of Local Weighting Filtering on Randomization Technique Estimates in a Data Assimilation System

Mainstream numerical weather prediction (NWP) centers usually estimate the standard deviations of background error by using a randomization technique to calibrate specific parameters of the background error covariance model in variational data assimilation (VAR) systems. However, the sampling size of the randomization technique is typically several orders of magnitude smaller than that of model state variables, and using finite-sized estimates as a proxy for the truth can lead to sampling noise, which may contaminate the estimation of the standard deviation. The sampling noise is firstly investigated in an atmospheric model to show that the sampling noise has a symmetrical structure oscillating around the truth on a small scale. To alleviate the sampling noise, a heterogeneous local weighting filtering is proposed based on distance-weighted correlation and similarity-weighted correlation. Local weighting filtering is easy to implement in the VAR operational systems and has a low computational cost in the post-processing of reducing the sampling noise. The validity and performance of local weighting filtering method are examined in a realistic model framework to show that the proposed filtering is able to eliminate most of the sampling noise dramatically, the details of the filtered results are more visible, and the accuracy of the filtered results is almost the same as that estimated from the larger sample. The signal-to-noise ratio of the optimal filtered field is improved by nearly 20%. A comparison with the widely used spectral filtering approach in the operational system is considered, showing that the proposed filtering method is more efficient to implement in the filtering procedure and exhibits very good performance in terms of preserving the local anisotropic features of the estimates. These attractive results show the potential efficiency of the local weighting filtering method for solving the noise issue in the randomization technique

The research progress of next generation risk assessment in cosmetic ingredients and the implications for traditional Chinese medicine risk assessment

Introduction: The advancement and advocacy of the 3R principle and safety evaluation and risk assessment technology has given rise to an innovative method on a global scale, which known as the Next Generation Risk Assessment (NGRA). This progressive approach adopts a tiered assessment strategy based on prepositive hypothesis and exposure analysis. Unlike traditional risk assessment methods that rely on toxicological endpoint animal tests, NGRA lays emphasis on evaluating the exposure process by outlining the mechanisms and significant key events leading to adverse outcomes. The NGRA process entails prioritizing the robust high-throughput screening and prediction tools to enhance the accuracy, efficiency, and relevance of the assessment. Result: This review provides an overview of the principles, framework, and tools of NGRA as recommended by the International Cosmetics Regulatory Cooperation Organization (ICCR). Furthermore, the review encompasses published case studies of cosmetics ingredients and elucidates the potential applications and technical challenges associated with NGRA while considering the extant safety evaluation requirements for cosmetics ingredients in China. Moreover, the review synthesizes a selection of case studies of cosmetics ingredients and hope to proffer implications for the assessment of risk in traditional Chinese medicine. Conclusion: It was hoped to help researchers to investigate novel techniques of safety risk assessment and propose research concepts for both cosmetics and traditional Chinese medicine

The Characteristic of Fe as a β-Ti Stabilizer in Ti Alloys

It is well known that adding elements, especially β-Ti stabilizers, are holding a significant effect on titanium alloy strength due to the solution and precipitate strengthening mechanisms. In order to reveal the Fe strengthening mechanism in titanium, this study investigate the effect of Fe on the stability of β-Ti and the phase transition between α, β and ω phase with first-principle calculations. According to our study, Fe is a strong β-Ti phase stabilizer could owe to the 3d orbital into eg and t2g states which results in strong hybridization between Fe-d orbital and Ti-d orbital. The phase transition from ω to β or from α to β becomes easier for Fe-doped Ti compared to pure titanium. Based on our results, it is found that one added Fe atom can lead the phase transition (ω → β) of at least nine titanium atoms, which further proves that Fe has a strong stabilizing effect on β-Ti phase. This result provides a solid guide for the future design of high-strength titanium with the addition of Fe

A New Method for Forest Canopy Hemispherical Photography Segmentation Based on Deep Learning

Research Highlights: This paper proposes a new method for hemispherical forest canopy image segmentation. The method is based on a deep learning methodology and provides a robust and fully automatic technique for the segmentation of forest canopy hemispherical photography (CHP) and gap fraction (GF) calculation. Background and Objectives: CHP is widely used to estimate structural forest variables. The GF is the most important parameter for calculating the leaf area index (LAI), and its calculation requires the binary segmentation result of the CHP. Materials and Methods: Our method consists of three modules, namely, northing correction, valid region extraction, and hemispherical image segmentation. In these steps, a core procedure is hemispherical canopy image segmentation based on the U-Net convolutional neural network. Our method is compared with traditional threshold methods (e.g., the Otsu and Ridler methods), a fuzzy clustering method (FCM), commercial professional software (WinSCANOPY), and the Habitat-Net network method. Results: The experimental results show that the method presented here achieves a Dice similarity coefficient (DSC) of 89.20% and an accuracy of 98.73%. Conclusions: The method presented here outperforms the Habitat-Net and WinSCANOPY methods, along with the FCM, and it is significantly better than the Otsu and Ridler threshold methods. The method takes the original canopy hemisphere image first and then automatically executes the three modules in sequence, and finally outputs the binary segmentation map. The method presented here is a pipelined, end-to-end method

CSIP-Net: Convolutional Satellite Image Prediction Network for Meteorological Satellite Infrared Observation Imaging

Geosynchronous satellite observation images have the advantages of a wide observation range and high temporal resolution, which are critical for understanding atmospheric motion and change patterns. The realization of geosynchronous satellite observation image prediction will provide significant support for short-term forecasting, including precipitation forecasting. Here, this paper proposes a deep learning method for predicting satellite observation images that can perform the task of predicting satellite observation sequences. In the study of predicting the observed images for Band 9 of the FY-4A satellite, the average mean square error of the network’s 2-h prediction is 4.77 Kelvin. The network’s predictive performance is the best among multiple deep learning models. We also used the model to predict Bands 10–14 of the FY-4A satellite and combined the multi-band prediction results. To test the application potential of the network prediction performance, we ran a precipitation area detection task on the multi-band prediction results. After 2 h of prediction, the detection results from satellite infrared images still achieved an accuracy of 0.855