Dried tangerine peel (“Chenpi”), has numerous clinical and nutritional benefits, with its quality being significantly influenced by its storage age, referred to as “Chen Jiu Zhe Liang” in Chinese. ...Concequently, the rapid and accurate identification of Chenpi's age is important for consumers. In this study, Fourier transform infrared spectroscopy (FTIR) was employed to capture spectral images of Chenpi. These FTIR images were then analyzed using a two-dimensional convolutional neural networks (2D-CNN) model, achieving a discrimination accuracy of 97.92%. To address the “black box” nature of the 2D-CNN, Gradient-weighted Class Activation Mapping Plus Plus (Grad-CAM++) was utilized to highlight the important regions contributing to the model's performance. Additionally, six other machine learning models were developped using features identified by the 2D-CNN to validate their effectiveness. The results demonstrated that the combination of FTIR spectral images and 2D-CNN provides a highly effective method for accurately determining the age of Chenpi.
•Chenpi, known as dried tangerine peel, benefits from its quality influenced by storage age.•FTIR and 2D-CNN model determined Chenpi's age with a discrimination accuracy of 97.92%.•Grad-CAM++ highlighted key regions in 2D-CNN model, addressing its “black box” nature.•The effectiveness of 2D-CNN for Chenpi's age discrimination was further validated.
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Artificial neural networks (ANNs), a subset of Quantitative Structure-Property Relationship (QSPR) methods, offer a promising avenue for addressing challenges in materials science. In ...particular, ANNs can learn intricated patterns within the experimental data, enabling them to predict properties and recognize complex relationships with remarkable accuracy. However, the opacity of ANNs, normally acting as black boxes, raises concerns about their reliability and interpretability. To enhance their transparency and to uncover the underlying relationships between chemical features and material properties, we propose a novel approach that employs Gradient-weighted Class Activation Mapping (Grad-CAM) applied to Convolutional Neural Networks (CNNs). By analyzing these attention maps, we identify the crucial chemical features influencing the prediction of a polymer property, specifically the glass transition temperature (Tg). Our methodology is validated using a dataset of atactic acrylates, allowing us to not only predict Tg values for a control group of polymers but also to quantitatively assess the impact of individual monomer structural elements on these predictions. This work proposes a step towards transparent models in materials science, contributing to a deeper understanding of the intricate relationship between chemical structures and material properties.
In this paper, in order to penalize for lack of divergence-free solution, we propose a sparse grad-div stabilized algorithm for the incompressible magnetohydrodynamics equations, which just adds a ...minimally intrusive module that implements grad-div stabilization with a sparse block structure matrix. Unconditional stability and error estimates of the proposed algorithm are provided and numerical tests are carried out. Compared to other grad-div stabilizations, the sparse grad-div stabilized algorithm is more efficient with some large values of grad-div parameters.
In this paper, we present a new static and time-dependent MagnetoHydroDynamic (MHD) equilibrium code, TokaMaker, for axisymmetric configurations of magnetized plasmas, based on the well-known ...Grad-Shafranov equation. This code utilizes finite element methods on an unstructured triangular grid to enable capturing accurate machine geometry and simple mesh generation from engineering-like descriptions of present and future devices. The new code is designed for ease of use without sacrificing capability and speed through a combination of Python, Fortran, and C/C++ components. A detailed description of the numerical methods of the code, including a novel formulation of the boundary conditions for free-boundary equilibria, and validation of the implementation of those methods using both analytic test cases and cross-code validation is shown. Results show expected convergence across tested polynomial degree for analytic and cross-code test cases.
•A developed CNN model proposed to predict the ETC of gradient porous ceramics.•Integrating self-attention mechanism into our model leads to a higher accuracy.•Grad-CAM applied to visually explain ...the optimization mechanism of our model.•Gradient QSGS developed to generate porous structure with gradient porosity.
Accurate and stable prediction of the effective thermal conductivity (ETC) of porous ceramic materials is of great significance for their application in areas such as optimizing the design of thermal barrier coatings and improving energy conversion efficiency. Porous ceramic materials exhibit complex porous structures with gradient porosity distributions that pose a great challenge for effective medium theory (EMT) and conventional convolutional neural networks (CNN) in attempting to accurately and stably predict the ETC of porous media. In this study, a CNN model that integrates self-attention and a multiscale feature-fusion mechanism is proposed to predict the ETC of porous media with greater accuracy and stability. The integration of the self-attention and multiscale feature-fusion mechanisms enhances the CNN's ability to learn long-range dependencies and preserve detailed information. The optimization of the CNN's accuracy and stability is visually illustrated using gradient-weighted class activation mapping (Grad-CAM) for ETC. Additionally, by employing the proposed gradient quartet structure generation set (QSGS), a gradient porous ceramic media dataset comprising 10 000 images was built to train the CNN model. Finally, the prediction results and relative error distribution of the ETC were compared across different models. Our model demonstrated improvements in statistical metrics, including a 33.7 % decrease in mean error, 25.2 % decrease in median error, and 59.6 % decrease in maximum error. The decreases in these metrics and Grad-CAM for the ETC strongly demonstrates that the model proposed in this work greatly improved the accuracy and stability of predicting the ETC of ceramic materials with gradient porosity distributions.
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•Multimodal imaging was used to simultaneously extract morphological, compositional and structural information of tissues.•Rapid and accurate diagnosis of breast cancer was achieved by multimodal ...micro-imaging with deep learning.•Gradient-weighted Class Activation Mapping (Grad-CAM) was used to reveal the reliability of model performance.
Accurate and rapid diagnosis of breast cancer is particularly important since it becomes the primary malignant tumor threatening women's health. Although pathological diagnosis with H&E staining section is the gold standard in tumor diagnosis, it is hard to meet quick intraoperative inspection because of its complex procedures and hysteresis. This study adopts multimodal microscopic imaging technology (bright-field imaging, auto-fluorescence imaging and orthogonal polarization imaging) combined with deep learning to achieve rapid intelligent diagnosis of breast cancer by getting the rich information of tissue morphology, content and structure of collagen in tissue slices. By using both fusion classification models of multimodal images at pixel level and decision level, the AUC score and accuracy is 0.9366 and 89.01% for the pixel fusion classification, as well as 0.9421 and 87.53% for the decision fusion classification, respectively. The results suggest that the multimodal microscopic imaging technique proposed in this study has unique advantages in accuracy, speed and feasibility in clinic for breast cancer diagnosis, and has strong clinical potential and application prospects when combined with deep learning.
Nanomechanical resonators can detect various small physical quantities with high sensitivity using changes in resonant properties. However, viscous damping in liquids significantly reduces the ...measurement sensitivity. This study proposes convolutional neural network (CNN) vibration spectrum analysis to evaluate the highly sensitive vibration states of nanomechanical resonators, which are useful for in-liquid measurements. This research was carried out through the measurement of acetone concentration. First, we compared the concentration classification ability between the proposed and conventional methods and determined that the proposed method of analyzing vibration spectral changes using the CNN model can provide higher measurement sensitivity than the conventional measurement method of observing resonance properties changes and comparing the values for each measurement condition. This result shows that CNN-based spectral analysis is effective for the vibration spectra of in-liquid measurements. Next, gradient-weighted class activation mapping (Grad-CAM) was applied to verify which frequency bands are important for concentration classification in CNN model decision-making. The vibration states in these frequency bands were analyzed in terms of oscillation modes. This analysis revealed significant oscillation modes of the nanomechanical resonator in the liquid environment. Notably, in addition to the resonance states utilized in the conventional method, several other oscillation modes were found to be significant for measurements. This finding suggests that these oscillation modes may be highly sensitive for measurements in liquid environments. Among these oscillation modes, the mode with very small amplitude is highly promising for achieving unprecedented levels of sensitivity in sensing technologies.
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•Highly sensitive vibrations were evaluated for nanomechanical resonators in liquids.•Vibration analysis by CNN was effective for in-liquid measurement of the resonator.•Grad-CAM was effective in identifying useful frequencies for in-liquid measurements.•Very small amplitude oscillation modes showed sensitivity to physical quantities.
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•Defect types of pine nut was identified using hyperspectral imaging at two spectral ranges.•1D CNN models using spectra as inputs obtained promising performances.•3D CNN models using ...hyperspectral images as input obtained equivalent performances.•1D CNN and 3D CNN models were visualized and interpreted by Grad-CAM++.
Pine nuts, as a highly nutritious and medicinally valuable food, are susceptible to various defects during their cultivation, harvesting, and transportation, which can reduce their quality. Therefore, rapid and accurate identification of pine nut defect types is of utmost importance to ensure the overall quality of the pine nuts. In this study, hyperspectral imaging (HSI) systems covering two spectral ranges (400–1000 nm and 900–1700 nm) were employed to capture hyperspectral images of healthy pine nuts and pine nuts with six types of defects. One-dimensional (1D) and three-dimensional (3D) Convolutional Neural Network (CNN) models with multi-head attention mechanisms were constructed using 1D spectra and 3D hyperspectral images, respectively. To validate the effectiveness of the proposed models, Support Vector Classifier (SVC) models were built using 1D spectra and used as a comparison. Overall, the proposed CNN models outperform traditional machine learning methods in two spectral ranges (400–1000 nm and 900–1700 nm). 1D CNN model in the near-infrared spectral range (900–1700 nm) achieved an accuracy of 90.23 % on the training set and 81.32 % on the validation set. Additionally, the Generalized Gradient-Weighted Class Activation Mapping (Grad-CAM++) visualization method was applied to conduct visual analysis on the 1D CNN and 3D CNN models, enabling the identification of important wavelength ranges and pixel regions in the models, thereby enhancing the interpretability of the decision-making process of the models. Overall, the results of this study demonstrated the feasibility of using a combination of hyperspectral imaging and convolutional neural networks for pine nut defects classification, and the visual analysis of the models provided new insights and understanding for pine nut defects identification.
