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Fire incidents in energy storage stations are frequent, posing significant firefighting safety risks. To simulate the fire characteristics and inhibition performances by fine water mist for lithium-ion battery packs in an energy-storage cabin, the PyroSim software is used to build a 1:1 experimental geometry model of a containerized lithium-ion energy storage cabin. The simulation reveals five stages and their characteristic parameter variations during a fire incident: initial temperature rise, flame generation, flame spread, stable combustion, and flame extinguishment. By adjusting various parameters of the fine water mist, the design of the fine water mist firefighting system for containerized energy storage units is optimized. The simulation results indicate that the optimal inhibition effect for the energy storage cabin's fine water mist firefighting system is achieved when the spray intensity is ≥24 l/min, the fog cone angle is 76°, nozzle velocity is 10 m/s, and the optimal particle size of the fine water mist is 50 μm. The research findings not only provide a rational method and theoretical guidance for the numerical simulation of thermal runaway in lithium batteries but also offer theoretical data support for the safety design and protection of future energy storage cabins. [ABSTRACT FROM AUTHOR]

A new rectangular concrete-filled hot-rolled-steel tubular flat column (RCSTFC) constructed from hot-rolled steel and internal steel plates was proposed in this study. The influence of the internal steel plate thickness (T I), angle steel thickness (T a), and cell number on the axial compression performance of RCSTFCs was investigated. The test results showed that RCSTFCs exhibit overall instability and local buckling under axial compression. Under D / B = 2.0, 2.67 and 3.0, the changes in T I , T a and cell number did not significantly influence the failure mode, strength index or axial deformation ductility of RCSTFCs with the same steel ratio. Furthermore, the area of the effectively confined zone and the stress-strain relationship of the confined concrete in the RCSTFCs were deduced, and a new calculation method for the axial load-carrying capacity of RCSTFCs was proposed. Compared to the design methods in GB 50936, CECS 159, AS 5100.6, and AISC 360, the proposed method produced relatively accurate results in the prediction of the axial load-carrying capacity of RCSTFCs. • RCSTFC configuration was studied by axial compression test. • Confined area and stress-strain relationship of concrete in RCSTFC were deduced. • New calculation method of RCSTFC axial load-carrying capacity was proposed. [ABSTRACT FROM AUTHOR]

AI-driven mineral prospectivity mapping (MPM) is a valid and increasingly accepted tool for delineating the targets of mineral exploration, but it suffers from noisy and unrepresentative input features. In this study, a set of fractal and multifractal methods, including box-counting calculation, concentration–area fractal modeling, and multifractal analyses, were employed to excavate the underlying nonlinear mineralization-related information from geological features. Based on these methods, multiple feature selection criteria, namely prediction–area plot, K-means clustering, information gain, chi-square, and the Pearson correlation coefficient, were jointly applied to rank the relative importance of ore-related features and their fractal representations, so as to choose the optimal input feature dataset readily used for training predictive AI models. The results indicate that fault density, the multifractal spectrum width (∆α) of the Yanshanian intrusions, information dimension (D1) of magnetic anomalies, correlation dimension (D2) of iron-oxide alteration, and the D2 of argillic alteration serve as the most effective predictor features representative of the corresponding ore-controlling elements. The comparative results of the model assessment suggest that all the AI models trained by the fractal datasets outperform their counterparts trained by raw datasets, demonstrating a significant improvement in the predictive capability of fractal-trained AI models in terms of both classification accuracy and predictive efficiency. A Shapley additive explanation was employed to trace the contributions of these features and to explain the modeling results, which imply that fractal representations provide more discriminative and definitive feature values that enhance the cognitive capability of AI models trained by these data, thereby improving their predictive performance, especially for those indirect predictor features that show subtle correlations with mineralization in the raw dataset. In addition, fractal-trained models can benefit practical mineral exploration by outputting low-risk exploration targets that achieve higher capturing efficiency and by providing new mineralization clues extracted from remote sensing data. This study demonstrates that the fractal representations of geological features filtered by multi-criteria feature selection can provide a feasible and promising means of improving the predictive capability of AI-driven MPM. [ABSTRACT FROM AUTHOR]

[Display omitted] • Utilizing Wavelet Transform for Multi-Scale Feature Extraction from SERS Data with Minimal Information Loss. • Utilizing Deep Learning for Enhanced Classification by Harnessing its Strong Nonlinear Processing Capability. • Enhancing Neural Network Performance through Data Augmentation Methods for Mitigating Overfitting Risks. • Comparative Analysis of Six Selected Wavelet Basis Functions for Processing SERS Data. The early detection of liver cancer greatly improves survival rates and allows for less invasive treatment options. As a non-invasive optical detection technique, Surface-Enhanced Raman Spectroscopy (SERS) has shown significant potential in early cancer detection, providing multiple advantages over conventional methods. The majority of existing cancer detection methods utilize multivariate statistical analysis to categorize SERS data. However, these methods are plagued by issues such as information loss during dimensionality reduction and inadequate ability to handle nonlinear relationships within the data. To overcome these problems, we first use wavelet transform with its multi-scale analysis capability to extract multi-scale features from SERS data while minimizing information loss compared to traditional methods. Moreover, deep learning is employed for classification, leveraging its strong nonlinear processing capability to enhance accuracy. In addition, the chosen neural network incorporates a data augmentation method, thereby enriching our training dataset and mitigating the risk of overfitting. Moreover, we acknowledge the significance of selecting the appropriate wavelet basis functions in SERS data processing, prompting us to choose six specific ones for comparison. We employ SERS data from serum samples obtained from both liver cancer patients and healthy volunteers to train and test our classification model, enabling us to assess its performance. Our experimental results demonstrate that our method achieved outstanding and healthy volunteers to train and test our classification model, enabling us to assess its performance. Our experimental results demonstrate that our method achieved outstanding performance, surpassing the majority of multivariate statistical analysis and traditional machine learning classification methods, with an accuracy of 99.38 %, a sensitivity of 99.8 %, and a specificity of 97.0 %. These results indicate that the combination of SERS, wavelet transform, and deep learning has the potential to function as a non-invasive tool for the rapid detection of liver cancer. [ABSTRACT FROM AUTHOR]