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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]

Objective: Survival and reconstruction in osteosarcoma is quite challenging. The study aimed to investigate the prognosis in patients treated with neoadjuvant chemotherapy and determine the clinical outcomes of expandable endoprosthesis reconstruction in children. Methods: From January 2009 to December 2014, we retrospectively analyzed 29 skeletally immature children (mean age, 10.5 years; range, 6–15 years) with osteosarcoma around the knee. Of the 29 patients who underwent neoadjuvant chemotherapy and limb salvage surgery, an expandable prosthesis was implanted for reconstruction. No patients were missed during follow‐up. The evaluation index involved follow‐up time, complication, functional results, and lengthening procedures. The survivorship and recurrence were assessed by GraphPad Software, and the function was evaluated by the Musculoskeletal Tumor Society (MSTS) scoring system. Results: A mean follow‐up time was 8.9 years (range, 6–12 years), and the overall 5‐year survival was 89.1% based on Kaplan–Meier analysis. Three patients suffered a relapse and one underwent amputation. Lung metastasis developed in one patient. At 6 months after the operation, patients had a mean MSTS score of 27 points (range, 24–29). Two patients underwent revision surgery, one for implant infection and one for aseptic loosening. Prognosis is correlated with alkaline phosphatase change after treatment. Conclusions: Chemotherapy scheme and limb salvage can achieve high survival rates. This expandable prosthesis was associated with good function and low complication rates. The character of expandability could be a method to overcome discrepancies in the growth period. [ABSTRACT FROM AUTHOR]

Groundwater constitutes a critical component in providing fresh water for various human endeavors. Nevertheless, its susceptibility to contamination by pollutants represents a significant challenge. A comprehensive understanding of the dynamics of solute transport in groundwater and soils is essential for predicting the spatial and temporal distribution of these contaminants. Presently, conventional models such as the mobile–immobile (MIM) model and the rate-limited sorption (RLS) model are widely employed to describe the non-Fickian behavior of solute transport. In this research, we present a novel approach to solute transport that is founded on the temporally relaxed theory of Fick's Law. Our methodology introduces two relaxation times to account for solute particle collisions and attachment, leading to the derivation of a new advection–dispersion equation. Our findings indicate that the relaxation times possess similar properties to the transport parameters in the MIM and RLS models, and our solution can be applied to accurately predict transport parameters from soil column experiments. Additionally, we discovered that the relaxation times are proportional to the magnitude of Peclet number. This innovative approach provides a deeper insight into solute transport and its impact on groundwater contamination. [Display omitted] • The study presents a new perspective on the two-stage phenomenon of solute transport. • The study introduces two relaxation times and derives a new advection–dispersion equation. • The study uses the solution to estimate transport parameters from existing soil column experiments. [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]

High-strength steel reinforcement offers promising application prospects in buildings with large spans and loads, as it can reduce steel congestion and simplify construction process. To investigate the mechanical behavior of flexural members reinforced with high-strength steel and measures to control crack width, in this study, static loading tests were conducted on 17 full-scale reinforced concrete (RC) beams with HRB635 high-strength steel reinforcement, six of which were reinforced with steel fiber. The failure mode, flexural strength, ductility, crack width, and deflection of the specimens were investigated, and the effects of different key parameters, including the reinforcement ratio and diameter, concrete strength, and steel fiber volume fraction were also analyzed. Test results indicated that the tensile strength of HRB635 reinforcement could be fully utilized, while the ductility ratios of high-strength steel reinforced concrete (HSS-RC) beams were less than those of beams with normal strength reinforcement. The maximum crack widths of most specimens without steel fibers exceeded the threshold defined in GB 50010-2010, indicating that the design of HSS-RC beams may be controlled by the requirements of serviceability limit state. Furthermore, the crack widths predicting methods defined in GB 50010-2010 and EN 1992-1-1 are more accurate for HSS-RC beams, while the deflection predicting methods defined in GB 50010-2010 and ACI 318-19 both underestimated the deflection of HSS-RC beams. This study could provide a theoretical basis for the engineering application of HRB635 high-strength steel reinforcement. [ABSTRACT FROM AUTHOR]

The development of mineral prospectivity mapping (MPM), which aims to outline and prioritize mineral exploration targets, has been spurred by advances in data-driven machine learning algorithms. Supervised data-driven MPM is a typical few-shot task, suffering from a scarcity of labeled data, the over-fitting of models and an uncertainty of predictions. The main objective of this contribution is to propose a robust framework of few-shot learning (FSL), combining data augmentation and transfer learning to enable the generation of prospectivity models with excellent predictive efficiency and low uncertainty. The mineral systems approach was used to transfer a conceptual mineral system into mappable exploration criteria. Synthetic minority over-sampling technique (SMOTE) was employed to augment and balance the labeled dataset, allowing for model pre-training with the large synthetic training dataset of a source domain. The knowledge derived from pre-trained models was then transferred to the target domain by fine-tuning, and the prospectivity model was generated in light of over-fitting and uncertainty assessments. The proposed FSL framework was applied to tungsten prospectivity mapping in southern Jiangxi Province. The results indicated that the SMOTE-ed balanced dataset boosted the classification accuracy in the training process. The FSL models yielded an arch-shaped prediction point pattern which was favorable for focusing potential targets with high probability and low uncertainty. The FSL models achieved a high predictive performance (test AUC = 0.9172) and the lowest quantitative over-fitting value compared to the models derived from the benchmark algorithms of random forest and support vector machine. Four levels of potential targeting zones, considering both predictive efficiency and uncertainty, were extracted from the resulting FSL prospectivity map. The final high-potential and low-risk exploration targets only cover 4.27% of the area, but capture 41.53% of known tungsten deposits, thus achieving a superior predictive performance. This study highlights the capability of FSL framework to control over-fitting and generate high-confidence exploration targets with low levels of uncertainty. [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]

Detecting pavement cracks from images is a complex computer vision task due to their varying shapes, backgrounds, and sizes. We propose CrackF-Net, an end-to-end convolutional neural network for automatic crack detection in road images. We construct the CrackF-Net network using an encoder–decoder architecture to extract image features in convolutional blocks with residuals and fuse the multiscale convolutional features produced by the decoder. Convolutional blocks with residuals are used to capture the strong semantic features of cracks, and an adaptive filter fusion module is proposed to assist the network make a selection of filter fusion features on the channels. CrackF-Net fuses the multiscale features in decoder to improve crack detection performance. The proposed CrackF-Net is compared to other advanced crack detection methods using three public datasets. The experimental results show that CrackF-Net achieves state-of-the-art performance, which obtains F-measures of 0.866, 0.737, and 0.852 on the three datasets. [ABSTRACT FROM AUTHOR]

• Flexural performance of CWFBs with CHCSs was verified by out-of-plane flexural tests. • The CWFBs with CHCSs had shear lag effects in both the tension and compression zones. • The effect of loading method and slab width on CWFB effective width was analyzed. • The equation to calculate the effective width of the CWFB was deduced theoretically. To investigate the flexural performance and effective flange width of composite wide flat beams (CWFBs) with composite hollow core slabs (CHCSs), four CWFB specimens with double-sided CHCSs were tested and analyzed. The test results indicated that all specimens showed typical flexural damage. The CWFB with CHCSs had shear lag effect in both tension and compression zones. Increasing the width of the CHCSs resulted in a greater effective width, stiffness, and load carrying capacity of CWFB with CHCSs. Compared with the three-point loading method, the effective width of CWFB with CHCSs was larger for the four-point loading method and the uniform loading method. In addition, finite element models (FEMs) of the tested specimens in MSC. Marc software were validated by the test results, and 30 FEMs were further analyzed. Finally, the equations for calculating the effective width were obtained for the CWFB with CHCSs under uniform, four-point and three-point loads. [ABSTRACT FROM AUTHOR]

[Display omitted] • Tenogenic patch can promote the tendon-like morphology, adhesion, proliferation and migration of tendon fibroblasts in vitro. • The tenogenic patch enhanced the collagen fiber regeneration in the acute rotator cuff tear model. • The patch scaffold can strengthen tendon and the regeneration of tendon-to-bone insertion. Rotator cuff tear of shoulder is a common tendon injury. Current therapeutic strategies are unsatisfactory because of poor biomechanical properties of the repaired rotator cuff tendons. To solve this problem, a newly biofabricated tenogenic patch by uniaxial cold-drawing technology was applied for the treatment of acute rotator cuff tear and its therapeutic effects were evaluated in vitro and in vivo. The cytocompatibility results indicated that the engineered patch was capable of promoting the spreading, migration and proliferation of tendon fibroblasts in vitro. Stress–strain curves of the sutured patch indicated improved elasticity followed by the yield phenomena, nonlinear plasticity and obviously delaminated rupture. The tenogenic patch also showed significantly enhanced collagen I deposition and better tendon-to-bone healing in the rat rotator cuff tear models in vivo. Furthermore, implantation with the tenogenic patch also promoted the fibrocartilage formation in Safranin-O/Fast green-staining. The analysis of biomechanical properties showed that the tenogenic patch is capable of improving the ultimate load of the repaired tendon. Therefore, the tenogenic patch may constitute an efficient strategy to enhance tendon-to-bone healing for clinical repair applications of rotator cuff tear. [ABSTRACT FROM AUTHOR]

• This study proposed a SC-CRWBB by introduced BRBs at corner and UPTs at center to CRW. • The seismic performance of SC-CRWBB was studied through the quasi-static tests. • The influence of UPTs initial stress and BRB area on residual drift was analyzed. • The hysteretic curve and the required UPTs initial stress were theoretically deduced. A new self-centering controlled rocking wall (CRW) with buckling-restrained braces (BRBs) at the base (SC-CRWBB) is proposed to improve the energy dissipation and self-centering capacities of CRWs. The SC-CRWBB was established by adding unbonded posttensioned tendons (UPTs) at the center and BRBs at two corners of a CRW. The quasi-static test results demonstrated that the proposed SC-CRWBB achieved a satisfactory seismic performance. Structural damage was concentrated in the BRBs, while a few cracks occurred in the surface between the CRW and mortar layer and between the BRBs and CRW. Then, a finite element model (FEM) of the test specimen was created in ABAQUS software to study the influence of the initial stress of the UPTs and sectional area of the BRB core plate on the residual deformation and lateral carrying capacity of the SC-CRWBB. Finally, an analytical method for the investigation of SC-CRWBBs was validated against the test and FE analysis results. [ABSTRACT FROM AUTHOR]

• A new type of superimposed reinforced concrete shear wall with insulation is proposed by integrating the insulation panel into the precast exterior concrete panel to achieve both bearing and insulation capabilities. • The connection method of the longitudinal reinforcement at the horizontal joints of the superimposed reinforced concrete shear wall is indirect splicing on one side and direct splicing on the other side, and the direct splicing area is moved upwards to avoid the horizontal joints. A new type of superimposed reinforced concrete shear wall with insulation (SRCSWI) is proposed in this paper by integrating the insulation panel into the precast exterior concrete panel to achieve both bearing and insulation capabilities. Four full-sized specimens, including one cast-in-place reinforced concrete shear wall (CRCSW), two SRCSWIs and one superimposed reinforced concrete shear wall (SRCSW) without insulation, were designed for use in pseudo-static tests. The test results showed that the failure modes of the SRCSWIs were similar to those of the CRCSW and SRCSW. For the SRCSWI with a lower reinforcement ratio for the boundary elements, fewer cracks occurred in the wall, while the opening width of the cracks in the horizontal joints was relatively large, which means that the plastic deformation of this specimen was concentrated near the horizontal joints. For the SRCSWIs, the precast exterior concrete panel contributed little to the stiffness and carrying capacity of the walls. For the SRCSWI specimens, SRCSW specimen and CRCSW specimen with the same design axial load ratio, the stiffness, ductility and carrying capacity were basically consistent. For the SRCSWI specimen with a lower axial force, the carrying capacity was lower, but the ductility ratio and energy dissipation coefficient were higher than those of the other specimens. Finally, solid finite element (FE) models of the test specimens were established in ABAQUS and validated with the experimental results. The distribution of concrete damage, steel reinforcement stress distribution and hysteretic curves in the numerical analysis were basically consistent with the test results. [ABSTRACT FROM AUTHOR]