Research on neural network prediction method for upgrading scale of natural gas reserves

With the gradual decline of natural gas production, reserve upgrading has become one of the important issues in natural gas exploration and development. However, the traditional reserve upgrade forecasting method is often based on experience and rules, which is subjective and unreliable. Therefore, a prediction method based on neural network is proposed in this paper to improve the accuracy and reliability of reserve upgrade prediction. In order to achieve this goal, by collecting the relevant data of natural gas exploration and development in Sichuan Basin, including geological parameters, production parameters and other indicators, and processing and analyzing the data, the relevant characteristics of reserves increase are extracted. Then, a neural network model based on multi-layer perceptron (MLP) is constructed and trained and optimized using backpropagation algorithm. The results show that the prediction accuracy of the constructed neural network model can reach more than 90% and can effectively predict the reserve upgrading. Experiments show that the model has high accuracy and reliability, and is significantly better than the traditional prediction methods. The method has good stability and reliability, and is suitable for a wider range of natural gas fields

Environmental Sound Classification Algorithm Based on Region Joint Signal Analysis Feature and Boosting Ensemble Learning

Environmental sound classification is an important branch of acoustic signal processing. In this work, a set of sound classification features based on audio signal perception and statistical analysis are proposed to describe the signal from multiple aspects of the time and frequency domain. Energy features, spectral entropy features, zero crossing rate (ZCR), and mel-frequency cepstral coefficient (MFCC) are combined to form joint signal analysis (JSA) features to improve the signal expression of the features. Then, based on the JSA, a novel region joint signal analysis feature (RJSA) for environment sound classification is also proposed. It can reduce feature extraction computation and improve feature stability, robustness, and classification accuracy. Finally, a sound classification framework based on the boosting ensemble learning method is provided to improve the classification accuracy and model generalization. The experimental results show that compared with the highest classification accuracy of the baseline algorithm, the environmental sound classification algorithm based on our proposed RJSA features and ensemble learning methods improves the classification accuracy, and the accuracy of the LightGBM-based sound classification algorithm improves by 14.6%

Environmental Sound Classification Algorithm Based on Region Joint Signal Analysis Feature and Boosting Ensemble Learning

Environmental sound classification is an important branch of acoustic signal processing. In this work, a set of sound classification features based on audio signal perception and statistical analysis are proposed to describe the signal from multiple aspects of the time and frequency domain. Energy features, spectral entropy features, zero crossing rate (ZCR), and mel-frequency cepstral coefficient (MFCC) are combined to form joint signal analysis (JSA) features to improve the signal expression of the features. Then, based on the JSA, a novel region joint signal analysis feature (RJSA) for environment sound classification is also proposed. It can reduce feature extraction computation and improve feature stability, robustness, and classification accuracy. Finally, a sound classification framework based on the boosting ensemble learning method is provided to improve the classification accuracy and model generalization. The experimental results show that compared with the highest classification accuracy of the baseline algorithm, the environmental sound classification algorithm based on our proposed RJSA features and ensemble learning methods improves the classification accuracy, and the accuracy of the LightGBM-based sound classification algorithm improves by 14.6%

Effects of ferric derisomaltose on postoperative anaemia in adult spinal deformity surgery: a study protocol for a randomised controlled trial

Introduction Postoperative anaemia is prevalent in adult spinal deformity (ASD) surgery in association with unfavourable outcomes. Ferric derisomaltose, a novel iron supplement, offers a promising solution in rapidly treating postoperative anaemia. However, the clinical evidence of its effect on patients receiving spinal surgery remains inadequate. This randomised controlled trial aims to evaluate the safety and efficacy of ferric derisomaltose on postoperative anaemia in ASD patients.Methods and analysis This single-centre, phase 4, randomised controlled trial will be conducted at Department of Orthopaedics at Peking Union Medical College Hospital and aims to recruit adult patients who received ASD surgery with postoperative anaemia. Eligible participants will be randomly assigned to receive ferric derisomaltose infusion or oral ferrous succinate. The primary outcome is the change in haemoglobin concentrations from postoperative days 1–14. Secondary outcomes include changes in iron parameters, reticulocyte parameters, postoperative complications, allogeneic red blood cell infusion rates, length of hospital stay, functional assessment and quality-of-life evaluation.Ethics and dissemination This study has been approved by the Research Ethics Committee of Peking Union Medical College Hospital and registered at ClinicalTrials.gov. Informed consent will be obtained from all participants prior to enrolment and the study will be conducted in accordance with the principles of the Declaration of Helsinki. The results of this study are expected to be disseminated through peer-reviewed journals and academic conferences.Trial registration number NCT05714007

Unraveling the phylogenomic diversity of Methanomassiliicoccales and implications for mitigating ruminant methane emissions

Abstract Background Methanomassiliicoccales are a recently identified order of methanogens that are diverse across global environments particularly the gastrointestinal tracts of animals; however, their metabolic capacities are defined via a limited number of cultured strains. Results Here, we profile and analyze 243 Methanomassiliicoccales genomes assembled from cultured representatives and uncultured metagenomes recovered from various biomes, including the gastrointestinal tracts of different animal species. Our analyses reveal the presence of numerous undefined genera and genetic variability in metabolic capabilities within Methanomassiliicoccales lineages, which is essential for adaptation to their ecological niches. In particular, gastrointestinal tract Methanomassiliicoccales demonstrate the presence of co-diversified members with their hosts over evolutionary timescales and likely originated in the natural environment. We highlight the presence of diverse clades of vitamin transporter BtuC proteins that distinguish Methanomassiliicoccales from other archaeal orders and likely provide a competitive advantage in efficiently handling B12. Furthermore, genome-centric metatranscriptomic analysis of ruminants with varying methane yields reveal elevated expression of select Methanomassiliicoccales genera in low methane animals and suggest that B12 exchanges could enable them to occupy ecological niches that possibly alter the direction of H2 utilization. Conclusions We provide a comprehensive and updated account of divergent Methanomassiliicoccales lineages, drawing from numerous uncultured genomes obtained from various habitats. We also highlight their unique metabolic capabilities involving B12, which could serve as promising targets for mitigating ruminant methane emissions by altering H2 flow