Microbial diversity and biogenic methane potential of a thermogenic-gas coal mine

The microbial communities and biogenic methane potential of a gas coal mine were investigated by cultivation-independent and cultivation-dependent approaches. Stable carbon isotopic analysis indicated that in situ methane in the coal mine was dominantly of a thermogenic origin. However, a high level of diversity of bacteria and methanogens that were present in the coal mine was revealed by 454 pyrosequencing, and included various fermentative bacteria in the phyla of Actinobacteria, Bacteroidetes, Firmicutes, and Proteobacteria, and acetotrophic, hydrogenotrophic, and methylotrophic methanogens. Methane was produced in enrichments of mine water samples supplemented with acetate under laboratory conditions. The microbial flora obtained from the enrichments could stimulate methane formation from coal samples. 16S rRNA gene clone library analysis indicated that the microbial community from coal cultivation samples supplemented with the enriched microbial consortium was dominated by the anaerobic fermentative Clostridiales and facultative acetoclastic Methanosarcina. This study suggests that the biogenic methane potential in the thermogenic-gas coal mine could be stimulated by the indigenous microorganisms

Deep learning for field-based automated high-throughput plant phenotyping

The current rapid development trend in Artificial Intelligence (AI) provides a vast selection of high-quality tools to solve complex problems in more efficient ways than before. As a consequence, many fields of science and engineering are starting to explore AI tools, especially Deep Learning (DL) models for visual perception, audio understanding and decision making. This thesis explores the application of DL in plant science and agriculture to overcome the throughput bottleneck inherent in the currently practiced manual phenotyping in fields. Among many field-based phenotyping challenges, we focus on the problems of pod-counting and flower detection in soybean crop production. To accomplish this objective, we leverage the RetinaNet DL model with different backbones to process the raw image data collected by autonomous ground-robotic platforms. The proposed high-throughput phenotyping framework also involves tracking algorithms for robust decision-making using multiple image frames from the video data collected by the robots. In the thesis, we discuss the training data generation, model building and inference processes in detail. High degree of accuracy results presented in this study demonstrate the promise of DL tools for field-based automated high-throughput plant phenotyping. Hence, a framework such as the one presented here can dramatically transform agriculture in terms of scalability, precision and profitability

Short-Term Load Forecasting Model Based on the Fusion of PSRT and QCNN

Short-term load forecasting (STLF) model based on the fusion of Phase Space Reconstruction Theory (PSRT) and Quantum Chaotic Neural Networks (QCNN) was proposed. The quantum computation and chaotic mechanism were integrated into QCNN, which was composed of quantum neurons and chaotic neurons. QCNN has four layers, and they are the input layer, the first hidden layer of quantum hidden nodes, the second hidden layer of chaotic hidden nodes, and the output layer. The theoretical basis of constructing QCNN is Phase Space Reconstruction Theory (PSRT). Through the actual example simulation, the simulation results show that proposed model has good forecasting precision and stability

Analysis and Countermeasures on Product Quality Inspection Management in the Quality Management System of Research in Universities

Based on the operation and practice of the scientific research quality management system in Universities, according to the standards demands on product quality inspection and the characteristics of scientific research product, the questions of scientific research product quality inspection in the quality management system in Universities were summarized. The implementation, supervision and management measures of scientific research product quality inspection were analyzed and put forward, in order to provide the reference of enhancing the operational effectiveness of scientific research quality management system and improving the research management in Universities