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Nickel-cadmium alkaline batteries are the core energy source for auxiliary devices of electric multiple units. Hence, an accurate estimation of their state of charge (SOC) is significantly important for prolonging battery life and improving energy efficiency. Given the limitations of existing SOC estimation methods when dealing with small-sample battery cycling data, this paper proposes an attention mechanism integrated convolutional neural network-gated recurrent unit (CNN-GRU) model for battery SOC estimation, and experimental validation is conducted on the LPH140A model nickel-cadmium batteries used in electric multiple units. The model employs a convolutional neural network (CNN) to extract short-term feature dependencies from long sequences within the battery cycling data. Then, an attention mechanism-integrated gated recurrent unit (GRU) is adopted to capture long spatial distance dependencies of the extracted feature data, resulting in more precise battery SOC estimation. To precisely estimate the SOC of small-sample battery cycling data, this paper transforms the continuous regression model into a classification problem, discretizes the battery SOC ranges, and converts the final prediction result into discrete SOC values. The experimental results show that compared with the CNN-GRU algorithm, the proposed approach improves three key metrics — root mean square error, mean absolute error, and mean relative error by 18.90%, 17.92% and 19.78%, respectively, demonstrating impressive prediction accuracy and stability.

Mapping relationships of multidimensional architectures play an essential role in the autonomous transportation system (ATS), which can help understand the complex relationships between multidimensional architectures. The current mapping relationship discovery for multidimensional architectures in ATS requires significant manual involvement, leading to the underutilization of textual data and the intense subjectivity of results. In order to address the above issues, it is necessary to mine and further utilize the semantic information in the textual data. This study applies the text-matching model to the mapping relationship discovery of multi-dimensional architectures, which can calculate the semantic similarity between texts. On this basis, a method based on the Siamese-BERT-wwm-Bi-LSTM model is proposed, which incorporates Chinese BERT with whole word masking (BERT-wwm), bidirectional long-short term memory (Bi-LSTM), and the Siamese Network. A series of experiments are conducted with different text-matching models. The results show that the precision rate, recall rate, and F1-score exceed 80% for most applied methods, which verifies the feasibility of using the text-matching model for mapping relationship discovery. These results expect to provide a method with good performance that can automatically perform mapping relationship discovery., Author(s): You Liu [1]; Haonan Tuo [1]; Mingfan He [1]; Qiang Fu [1]; Tianjian Yu (corresponding author) [1]; Jinjun Tang (corresponding author) [1] 1. Introduction With the development of emerging [...]

Spatial autocorrelation and skewed distribution are the most frequent issues in crash rate modelling analysis. Previous studies commonly focus on the spatial autocorrelation between adjacent regions or the relationships between crash rate and potentially risky factors across different quantiles of crash rate distribution, but rarely both. To overcome the research gap, this study utilizes the spatial autoregressive quantile (SARQ) model to estimate how contributing factors influence the total and fatal-plus-injury crash rates and how modelling relationships change across the distribution of crash rates considering the effects of spatial autocorrelation. Three types of explanatory variables, i.e., demographic, traffic networks and volumes, and land-use patterns, were considered. Using data collected in New York City from 2017 to 2019, the results show that: (1) the SARQ model outperforms the traditional quantile regression model in prediction and fitting performance; (2) the effects of variables vary with the quantiles, mainly classifying three types: increasing, unchanged, and U-shaped; (3) at the high tail of crash rate distribution, the effects commonly have sudden increases/decrease. The findings are expected to provide strategies for reducing the crash rate and improving road traffic safety. [ABSTRACT FROM AUTHOR]

Breast cancer has been shown to live in the tumor microenvironment, which consists of not only breast cancer cells themselves but also a significant amount of pathophysiologically altered surrounding stroma and cells. Diverse components of the breast cancer microenvironment, such as suppressive immune cells, re-programmed fibroblast cells, altered extracellular matrix (ECM) and certain soluble factors, synergistically impede an effective anti-tumor response and promote breast cancer progression and metastasis. Among these components, stromal cells in the breast cancer microenvironment are characterized by molecular alterations and aberrant signaling pathways, whereas the ECM features biochemical and biomechanical changes. However, triple-negative breast cancer (TNBC), the most aggressive subtype of this disease that lacks effective therapies available for other subtypes, is considered to feature a unique microenvironment distinct from that of other subtypes, especially compared to Luminal A subtype. Because these changes are now considered to significantly impact breast cancer development and progression, these unique alterations may serve as promising prognostic factors of clinical outcome or potential therapeutic targets for the treatment of TNBC. In this review, we focus on the composition of the TNBC microenvironment, concomitant distinct biological alteration, specific interplay between various cell types and TNBC cells, and the prognostic implications of these findings.