Alkali-Resistant Domestication of and Its Growth Kinetics in Sodium Carboxymethyl Cellulose Solid-Free Drilling Fluids.

Microbially induced calcium carbonate precipitation (MICP) provides a novel approach for use in addressing the instabilities of borehole walls comprising broken formations, but the highly alkaline environments of drilling fluids are unfavorable for microbial growth. Therefore, this study investigated the alkali-resistant domestication of commonly used in MICP. Using gradient domestication, was domesticated under different pH conditions (pH 8.

Non-invasive load monitoring based on deep learning to identify unknown loads.

With the rapid development of smart grids, society has become increasingly urgent to solve the problems of low energy utilization efficiency and high energy consumption. In this context, load identification has become a key element in formulating scientific and effective energy consumption plans and reducing unnecessary energy waste. However, traditional load identification methods mainly focus on known electrical equipment, and accurate identification of unknown electrical equipment still faces significant challenges.

A NILM load identification method based on structured V-I mapping.

With the increasing number and types of global power loads and the development and popularization of smart grid technology, a large number of researches on load-level non-intrusive load monitoring technology have emerged. However, the unique power characteristics of the load make NILM face the difficult problem of low robustness of feature extraction and low accuracy of classification and identification in the recognition stage. This paper proposes a structured V-I mapping method to address the inherent limitations of traditional V-I trajectory mapping methods from a new perspective.

Merge Loss Calculation Method for Highly Imbalanced Data Multiclass Classification.

In real classification scenarios, the number distribution of modeling samples is usually out of proportion. Most of the existing classification methods still face challenges in comprehensive model performance for imbalanced data. In this article, a novel theoretical framework is proposed that establishes a proportion coefficient independent of the number distribution of modeling samples and a general merge loss calculation method independent of class distribution.

Fast environmental sound classification based on resource adaptive convolutional neural network.

Recently, with the construction of smart city, the research on environmental sound classification (ESC) has attracted the attention of academia and industry. The development of convolutional neural network (CNN) makes the accuracy of ESC reach a higher level, but the accuracy improvement brought by CNN is often accompanied by the deepening of network layers, which leads to the rapid growth of parameters and floating-point operations (FLOPs). Therefore, it is difficult to transplant CNN model to embedded devices, and the classification speed is also difficult to accept.

Environmental sound classification using temporal-frequency attention based convolutional neural network.

Environmental sound classification is one of the important issues in the audio recognition field. Compared with structured sounds such as speech and music, the time-frequency structure of environmental sounds is more complicated. In order to learn time and frequency features from Log-Mel spectrogram more effectively, a temporal-frequency attention based convolutional neural network model (TFCNN) is proposed in this paper.

Simultaneous Prediction of Soil Properties Using Multi_CNN Model.

Soil nutrient prediction based on near-infrared spectroscopy has become the main research direction for rapid acquisition of soil information. The development of deep learning has greatly improved the prediction accuracy of traditional modeling methods. In view of the low efficiency and low accuracy of current soil prediction models, this paper proposes a soil multi-attribute intelligent prediction method based on convolutional neural networks, by constructing a dual-stream convolutional neural network model Multi_CNN that combines one-dimensional convolution and two-dimensional convolution, the intelligent prediction of soil multi-attribute is realized.

Export trade, embodied carbon emissions, and environmental pollution: An empirical analysis of China's high- and new-technology industries.

China's export trade has been expanding steadily in recent years, significantly increasing resource consumption and environmental pollution. High- and new-technology industries are essential for achieving sustainable economic development and improving environmental quality. This study employs a multi-regional input-output model to estimate the economic benefits and environmental costs of export trade in high- and new-technology industries.