Convolutional Neural Network Model Research Articles

Gut metagenome-derived image augmentation and deep learning improve prediction accuracy of metabolic disease classification.

In recent years, statistics and machine learning methods have been widely used to analyze the relationship between human gut microbial metagenome and metabolic diseases, which is of great significance for the functional annotation and development of microbial communities. In this study, we proposed a new and scalable framework for image enhancement and deep learning of gut metagenome, which could be used in the classification of human metabolic diseases. Each data sample in three representative human gut metagenome datasets was transformed into image and enhanced, and put into the machine learning models of logistic regression (LR), support vector machine (SVM), Bayesian network (BN) and random forest (RF), and the deep learning models of multilayer perceptron (MLP) and convolutional neural network (CNN). The accuracy performance of the overall evaluation model for disease prediction was verified by accuracy (A), accuracy (P), recall (R), F1 score (F1), area under ROC curve (AUC) and 10 fold cross-validation. The results showed that the overall performance of MLP model was better than that of CNN, LR, SVM, BN, RF and PopPhy-CNN, and the performance of MLP and CNN models was further improved after data enhancement (random rotation and adding salt-and-pepper noise). The accuracy of MLP model in disease prediction was further improved by 4%-11%, F1 by 1%-6% and AUC by 5%-10%. The above results showed that human gut metagenome image enhancement and deep learning could accurately extract microbial characteristics and effectively predict the host disease phenotype. The source code and datasets used in this study can be publicly accessed in https://github.com/HuaXWu/GM_ML_Classification.git.

Fault Diagnosis of Wind Turbine Blades with Continuous Wavelet Transform based Deep Learning Model Using Vibration Signal

Wind Turbines are the most crucial devices in wind energy conversion systems to increase energy generation efficiency from wind sources. Blade failure in wind turbines is due to the induced vibrations of change in ecological variables. It is necessary to periodically inspect the state of the wind turbine blades to enhance safety by decreasing downtime, lessening the likelihood of unplanned failures requiring extensive repair, and progressively increasing power generation with reduced logistical costs. The primary goal of the proposed research is to adopt a hybrid strategy that combines convolutional neural network models and continuous wavelet transform. The bump wavelet-based continuous wavelet transform with a convolutional neural network model is employed to classify the faulty wind turbine blades based on the extracted vibration signals of turbine blades. This approach distinguishes between different states of blade faults affecting wind turbine blades during operational phases. The research considers blade faults in the horizontal axis wind turbine, including blade bending, erosion, and the connection looseness between the hub and blade. The cross-validation and hold-out methods are used to validate the classification accuracy and are compared with other existing popular methods. The hold-out method has a better classification accuracy of 97.916%.

Learning graph-Fourier spectra of textured surface images for defect localization

In the realm of industrial manufacturing, product inspection remains a significant bottleneck, with only a small fraction of manufactured items undergoing inspection for surface defects. Advances in imaging systems and AI can allow automated full inspection of manufactured surfaces. However, even the most contemporary imaging and machine learning methods perform poorly for detecting defects in images with highly textured backgrounds, that stem from diverse manufacturing processes. This paper introduces an approach based on graph Fourier analysis to automatically identify defective images, as well as crucial graph Fourier coefficients that inform the defects in the images. The approach thereby facilitates precise localization and characterization of defects, amidst highly textured backgrounds. A convolutional neural network model (1D-CNN) was trained with the coefficients of the graph Fourier transform of the images as the input to identify, with classification accuracy of 99.4%, if the image contains a defect. An explainable AI method using SHAP (SHapley Additive exPlanations) was used to further analyze the trained 1D-CNN model to discern important spectral coefficients for each image. This approach sheds light on the crucial contribution of low-frequency graph eigen waveforms to precisely localize surface defects in images, thereby advancing the realization of zero-defect manufacturing.

ANTIPASTI: Interpretable prediction of antibody binding affinity exploiting normal modes and deep learning.

The high binding affinity of antibodies toward their cognate targets is key to eliciting effective immune responses, as well as to the use of antibodies as research and therapeutic tools. Here, we propose ANTIPASTI, a convolutional neural network model that achieves state-of-the-art performance in the prediction of antibody binding affinity using as input a representation of antibody-antigen structures in terms of normal mode correlation maps derived from elastic network models. This representation captures not only structural features but energetic patterns of local and global residue fluctuations. The learnt representations are interpretable: they reveal similarities of binding patterns among antibodies targeting the same antigen type, and can be used to quantify the importance of antibody regions contributing to binding affinity. Our results show the importance of the antigen imprint in the normal mode landscape, and the dominance of cooperative effects and long-range correlations between antibody regions to determine binding affinity.

BrainQCNet: A Deep Learning attention-based model for the automated detection of artifacts in brain structural MRI scans

Abstract Analyses of structural MRI (sMRI) data depend on robust upstream data quality control (QC). It is also crucial that researchers seek to retain maximal amounts of data to ensure reproducible, generalizable models and to avoid wasted effort, including that of participants. The time-consuming and difficult task of manual QC evaluation has prompted the development of tools for the automatic assessment of brain sMRI scans. Existing tools have proved particularly valuable in this age of Big Data; as datasets continue to grow, reducing execution time for QC evaluation will be of considerable benefit. The development of Deep Learning (DL) models for artifact detection in structural MRI scans offers a promising avenue toward fast, accurate QC evaluation. In this study, we trained an interpretable Deep Learning model, ProtoPNet, to classify minimally preprocessed 2D slices of scans that had been manually annotated with a refined quality assessment (ABIDE 1; n = 980 scans). To evaluate the best model, we applied it to 2141 ABCD T1-weighted MRI scans for which gold-standard manual QC annotations were available. We obtained excellent accuracy: 82.4% for good quality scans (Pass), 91.4% for medium to low quality scans (Fail). Further validation using 799 T1w MRI scans from ABIDE 2 and 750 T1w MRI scans from ADHD-200 confirmed the reliability of our model. Accuracy was comparable to or exceeded that of existing ML models, with fast processing and prediction time (1 minute per scan, GPU machine, CUDA-compatible). Our attention model also performs better than traditional DL (i.e., convolutional neural network models) in detecting poor quality scans. To facilitate faster and more accurate QC prediction for the neuroimaging community, we have shared the model that returned the most reliable global quality scores as a BIDS-app (https://github.com/garciaml/BrainQCNet).

Fusion model of gray level co-occurrence matrix and convolutional neural network faced for histopathological images.

The image recognition of cancer cells plays an important role in diagnosing and treating cancer. Deep learning is suitable for classifying histopathological images and providing auxiliary technology for cancer diagnosis. The convolutional neural network is employed in the classification of histopathological images; however, the model's accuracy may decrease along with the increase in network layers. Extracting appropriate image features is helpful for image classification. In this paper, different features of histopathological images are represented by extracting features of the gray co-occurrence matrix. These features are recombined into a 16 × 16 × 3 matrix to form an artificial image. The original image and the artificial image are fused by summing the softmax output. The histopathological images are divided into the training set, validation set, and testing set. Each training dataset consists of 1500 images, while the validation dataset and test dataset each consist of 500 images. The results indicate that the effectiveness of our fusion model is demonstrated through significant improvements in accuracy, precision, recall, and F1-score, with an average accuracy reaching 99.31%. This approach not only enhances the classification performance of tissue pathology images but also holds promise for advancing computer-aided diagnosis in cancer pathology.

Optimizing Hybrid Active–Passive Thermal Management of Prismatic Li‐Ion Batteries Using Phase Change Materials and Porous‐Filled Mini‐Channels

ABSTRACTTackling climate change is crucial, and electrifying the vehicular transportation sector is essential to reduce greenhouse gas emissions. Lithium‐ion (Li‐ion) batteries are highly efficient for electric vehicles (EVs) but face challenges such as thermal management, risk of thermal runaway, and high costs of lithium and cobalt. Overcoming these challenges is vital for the widespread adoption of hybrid and EVs. To overcome this drawback, this article proposed a large‐kernel attention graph convolutional network (LKAGCN) with leaf in wind optimization algorithm (LWOA) named as LKAGCN‐LWOA technique, which enhances the thermal management of prismatic Li‐ion batteries by integrating both active and passive cooling techniques. The system incorporates phase change materials (PCMs) with porous‐filled mini‐channels to regulate battery temperature effectively. The LKAGCN analyze thermal properties, battery conditions, and PCM characteristics to predict and optimize the thermal behavior of the battery pack using LWOA. The proposed methods tune the parameters of the hybrid thermal management system, ensuring efficient thermal regulation and improved performance. The proposed method is compared to various existing methods such as convolutional neural network (CNN), Taguchi method, and Finite element model (FEM).

Diagnosis and Screening of Velocardiofacial Syndrome by Evaluating Facial Photographs Using a Deep Learning-Based Algorithm.

Early detection of rare genetic diseases, including velocardiofacial syndrome (VCFS), is essential for patient well-being. However, their rarity and limited clinical experience of physicians make diagnosis challenging. Deep learning algorithms have emerged as promising tools for efficient and accurate diagnosis. This study investigates the use of a deep learning algorithm to develop a face recognition model for diagnosing VCFS. The study employed publicly available labeled face datasets to train the multitask cascaded convolutional neural networks (MTCNN) model. Subsequently, we examined the binary classification performance for diagnosing VCFS using the most efficient face recognition model. A total of 98 VCFS patients (920 facial photographs) and 91 non-VCFS controls (463 facial photographs) were randomly divided into training and test sets. Additionally, we analyzed whether the classification results matched the known facial phenotype of VCFS. The face recognition model demonstrated high accuracy, ranging from 94% to 99%, depending on the training dataset. The accuracy of the binary classification diagnostic model varied from 81% to 88% when evaluating with photographs taken at various angles, but reached 95% evaluating with frontal photographs only. Gradient-weighted class activation mapping heatmap revealed the high importance level of perinasal and periorbital areas, exhibiting consistency with the conventional facial phenotypes of VCFS. This study shows the feasibility and effectiveness of MTCNN-based model for detecting VCFS solely from facial photographs. The high accuracy underscores the potential of deep learning in aiding early diagnosis of rare genetic diseases, facilitating timely interventions for patient care.

Deep learning-based rapid prediction of temperature field and intelligent control of molten pool during directed energy deposition process

In this work, deep learning-based approaches are proposed to provide promising solutions to address the challenges in realizing intelligent manufacturing and digital twins for directed energy deposition (DED) process. Firstly, a rapid and accurate prediction of part temperature is realized by innovatively combining graph neural networks (GNNs) and recurrent neural networks (RNNs). Twenty parts with different structures are selected for demonstration. GPU parallel computing technique is adopted to accelerate the thermal finite element analysis, which is used to quickly construct the simulated graph dataset with sufficient samples. By embedding the memory optimization method into the GNN block, deeper GNNs with more trainable parameters are successfully trained with a 79.4% lower GPU memory footprint, which solves the difficulty of deeper GNNs are hard to train on large graph datasets, and the accuracy of temperature prediction on unseen DED parts is significantly improved. Secondly, for intelligent molten pool regulation, a semi-analytic temperature solution method is used to create an efficient DED environment in reinforcement learning (RL) workflows. The intelligent control of molten pool depth under complex deposition strategy is realized based on the environmental state represented by molten pool images. A tailored convolutional neural networks (CNNs) model is employed as the agent to output varying laser power and continuously interact with the dynamic environment. Compared with the traditional artificial neural network agent, the total reward scored by the CNN agent is improved by 9.7% in the zigzag deposition process, mitigating the fluctuations in the controlled molten pool depths. Moreover, CNNs are more compatible with in-situ thermal images. This work can provide theoretical and technical support for realizing real-time and even ahead-of-time temperature prediction and the corresponding feedback control during DED process.

THE ROLE OF GEOSPATIAL ARTIFICIAL INTELLIGENCE (GEOAI) IN SMART BUILT ENVIRONMENT MAPPING: AUTOMATIC OBJECT DETECTION OF RASTER TOPOGRAPHIC MAPS IN MALAYSIA

Smart built environment mapping is integrating Geospatial Artificial Intelligence (GeoAI) to enable advanced analysis, pattern recognition, and decision-making processes. This shift in understanding, planning, designing, and managing the built environment is paving the way for a smarter, more sustainable future. This commentary explores the current role of AI in enhancing technology use within the geospatial field, focusing specifically on the application of GeoAI in mapping the built environment. Additionally, the paper presents a selection of case studies related to the implementation of AI in developing automatic vectorization, particularly for geospatial mapping in built environments. This research demonstrates the effectiveness of using Convolutional Neural Network (CNN) models for sorting objects in scanned, old topographic maps of the built environment. The findings of this study are valuable for making informed decisions, devising effective strategies, and identifying opportunities for further research and exploration within the dynamic field of GeoAI in smart built environment mapping and applications.

Enhancing the Recognition of Handwritten Arabic Characters through Hybrid Convolutional and Bidirectional Recurrent Neural Network Models

Handwritten Arabic character recognition remains a challenging task in pattern recognition due to the inherent complexities of the cursive script and visual similarities between characters. While deep learning techniques have demonstrated promising results in this domain, further enhancements to the model architecture can drive even greater performance improvements. This study introduces a hybrid deep learning approach that combines Convolutional Neural Networks (CNNs) with Bidirectional Recurrent Neural Networks, specifically Bidirectional Long Short-Term Memory (Bi-LSTM) and Bidirectional Gated Recurrent Units (Bi-GRU). By leveraging the strengths of both convolutional and recurrent neural network components, the proposed models are able to effectively capture spatial features as well as model the temporal dynamics and contextual relationships present in handwritten Arabic text. Experiments conducted on the AHCD and Hijjaa benchmark datasets show that the CNN-Bi-GRU framework achieved state-of-the-art accuracy rates of 97.05% and 91.78% respectively, outperforming previous deep learning-based methods. These results demonstrate the significant performance gains that can be achieved by integrating specialized temporal modeling and contextual representation capabilities into the handwriting recognition pipeline, without the need for explicit segmentation. The findings of this research represent a crucial advancement in the continued development of sophisticated and precise deep learning systems for Arabic handwriting recognition, with broad applications across domains that rely on efficient text extraction from handwritten documents.

A Big Data Intelligent Evaluation System for Sports Knowledge

The rapid evolution of computer technology has significantly impacted the field of medicine, particularly in the utilization of information and image evidence. In the realm of sports medicine, this technological advancement plays a crucial role in ensuring sports safety, especially in the context of injury recovery following sports-related activities. The necessity to interpret and utilize a vast amount of sports medical data effectively has emerged as a pivotal research avenue. This paper delves into the challenges associated with extracting, studying, and the accuracy training of complex algorithms essential for analyzing critical sporting medical data.Central to this discussion is introducing an Optimized Convolutional Neural Network (OCNN) model, which is based on deep learning principles. This model is designed to enhance the detection and risk assessment of diseases related to sport medicine. It incorporates a novel Self-Adjustment Resizing algorithm (SAR), augmented by a self-coding method of convolution (SCM). The proposed OCNN model comprises two convolutional layers, two pooling layers, a fully connected layer, and a SoftMax structure. This architecture is tailored for the classification and analysis of sport-related medical data.

Utilizing customized CNN for brain tumor prediction with explainable AI

Timely diagnosis of brain tumors using MRI and its potential impact on patient survival are critical issues addressed in this study. Traditional DL models often lack transparency, leading to skepticism among medical experts owing to their "black box" nature. This study addresses this gap by presenting an innovative approach for brain tumor detection. It utilizes a customized Convolutional Neural Network (CNN) model empowered by three advanced explainable artificial intelligence (XAI) techniques: Shapley Additive Explana-tions (SHAP), Local Interpretable Model-agnostic Explanations (LIME), and Gradient-weighted Class Activation Mapping (Grad-CAM). The study utilized the BR35H dataset, which includes 3060 brain MRI images encompassing both tumorous and non-tumorous cases. The proposed model achieved a remarkable training accuracy of 100 % and validation accuracy of 98.67 %. Precision, recall, and F1 score metrics demonstrated exceptional performance at 98.50 %, confirming the accuracy of the model in tumor detection. Detailed result analysis, including a confusion matrix, comparison with existing models, and generalizability tests on other datasets, establishes the superiority of the proposed approach and sets a new benchmark for accuracy. By integrating a customized CNN model with XAI techniques, this research enhances trust in AI-driven medical diagnostics and offers a promising pathway for early tumor detection and potentially life-saving interventions.

Deep-learning and data-resampling: A novel approach to predict cyanobacterial alert levels in a reservoir

The proliferation of harmful algal blooms results in adverse impacts on aquatic ecosystems and public health. Early warning system monitors algal bloom occurrences and provides management strategies for promptly addressing high-concentration algal blooms following their occurrence. In this study, we aimed to develop a proactive prediction model for cyanobacterial alert levels to enable efficient decision-making in management practices. We utilized 11 years of water quality, hydrodynamic, and meteorological data from a reservoir that experiences frequent harmful cyanobacterial blooms in summer. We used these data to construct a deep-learning model, specifically a 1D convolution neural network (1D-CNN) model, to predict cyanobacterial alert levels one week in advance. However, the collected distribution of algal alert levels was imbalanced, leading to the biased training of data-driven models and performance degradation in model predictions. Therefore, an adaptive synthetic sampling method was applied to address the imbalance in the minority class data and improve the predictive performance of the 1D-CNN. The adaptive synthetic sampling method resolved the imbalance in the data during the training phase by incorporating an additional 156 and 196 data points for the caution and warning levels, respectively. The selected optimal 1D-CNN model with a filter size of 5 and comprising 16 filters achieved training and testing prediction accuracies of 97.3% and 85.0%, respectively. During the test phase, the prediction accuracies for each algal alert level (L-0, L-1, and L-2) were 89.9%, 79.2%, and 71.4%, respectively, indicating reasonably consistent predictive results for all three alert levels. Therefore, the use of synthetic data addressed data imbalances and enhanced the predictive performance of the data-driven model. The reliable forecasts produced by the improved model can support the development of management strategies to mitigate harmful algal blooms in reservoirs and can aid in building an early warning system to facilitate effective responses.

Using the GoogLeNet deep-learning model to distinguish between benign and malignant breast masses based on conventional ultrasound: a systematic review and meta-analysis.

Breast cancer is one of the most common malignancies in women worldwide, and early and accurate diagnosis is crucial for improving treatment outcomes. Conventional ultrasound (CUS) is a widely used screening method for breast cancer; however, the subjective nature of interpreting the results can lead to diagnostic errors. The current study sought to estimate the effectiveness of using a GoogLeNet deep-learning convolutional neural network (CNN) model to identify benign and malignant breast masses based on CUS. A literature search was conducted of the Embase, PubMed, Web of Science, Wanfang, China National Knowledge Infrastructure (CNKI), and other databases to retrieve studies related to GoogLeNet deep-learning CUS-based models published before July 15, 2023. The diagnostic performance of the GoogLeNet models was evaluated using several metrics, including pooled sensitivity (PSEN), pooled specificity (PSPE), the positive likelihood ratio (PLR), the negative likelihood ratio (NLR), the diagnostic odds ratio (DOR), and the area under the curve (AUC). The quality of the included studies was evaluated using the Quality Assessment of Diagnostic Accuracy Studies Scale (QUADAS). The eligibility of the included literature were independently searched and assessed by two authors. All of the 12 studies that used pathological findings as the gold standard were included in the meta-analysis. The overall average estimation of sensitivity and specificity was 0.85 [95% confidence interval (CI): 0.80-0.89] and 0.86 (95% CI: 0.78-0.92), respectively. The PLR and NLR were 6.2 (95% CI: 3.9-9.9) and 0.17 (95% CI: 0.12-0.23), respectively. The DOR was 37.06 (95% CI: 20.78-66.10). The AUC was 0.92 (95% CI: 0.89-0.94). No obvious publication bias was detected. The GoogLeNet deep-learning model, which uses a CNN, achieved good diagnostic results in distinguishing between benign and malignant breast masses in CUS-based images.

Simulation and prediction of daytime surface urban heat island intensity under multiple scenarios via fully connected neural network

The intensification of the Surface Urban Heat Island (SUHI), driven by urbanization, land use and land cover (LULC) changes, and population growth, presents significant environmental and public health risks in urban areas. Simulating and predicting SUHI, particularly through the identification of future high SUHI intensity (SUHII) zones, has been recognized as a critical step in mitigating these effects. This study employs a Fully Convolutional Neural Network (FCNN) model, trained on data from four research sites, to simulate the current daytime SUHII across six validation sites in Singapore, utilizing 15 key independent variables identified in previous studies. The model exhibits high validation accuracy, achieving 87.45%. Three projection scenarios, based on projected population growth and LULC changes, predict a decrease in High SUHII across all validation sites, ranging from 98.3% to 9%. This reduction is attributed to the LULC improvements proposed in the 2019 Master Plan. Spatial analysis of the predicted SUHII maps indicates that the majority of High SUHII locations across scenarios remain consistent with the current situation. This research also suggests that the model could be a valuable tool for urban planners, allowing them to assess whether new urban development plans will effectively reduce High SUHII to desired thresholds, thereby mitigating SUHII in urban environments.

A stethoscope-guided interpretable deep learning framework for powder flow diagnosis in cold spray additive manufacturing

Cold spray (CS) particle deposition, also known as cold spray additive manufacturing, has garnered great attention as an advanced additive manufacturing (AM) and surface deposition technology, facilitating rapid and scalable production of functional parts and surfaces in a solid-state manner. In CS, consistent and precise feeding of functional feedstock powders is crucial for achieving effective particle deposition. However, vibratory-based powder feeders often face challenges associated with powder delivery and powder segregation. This underscores the critical need for a precise diagnostic framework to effectively control powder flow during cold spraying. To this end, the present study proposes a powder flow monitoring framework for the CS process using a stethoscope sound-guided interpretable deep learning (IDL) model. Internal sound data from the vibrated powder feeder is collected through a stethoscope sensor to train a two-stage model. In the first stage, a convolutional autoencoder (CAE) is trained to build an unsupervised learning-based anomaly detector, which identifies classification thresholds based on the receiver operation characteristic curve. In the second stage, a convolutional neural network (CNN) model is trained as the powder flow diagnostic tool by considering process anomalies, namely (i) no powder flow; (ii) feeder clogging; and (iii) no gas flow. The results reveal that the stethoscope sound-guided model achieves a classification accuracy of 95% on the test set, significantly outperforming benchmark utilizing typical external-sound recording microphones in diagnosing CS powder flow. Furthermore, the model is visualized and interpreted by employing t-distribution stochastic neighbor embedding and integrated gradients techniques to enhance reliability of CS powder flow diagnosis. This research highlights the effectiveness of the stethoscope sound-guided IDL model for in situ powder flow monitoring and process diagnosis in the domain of cold spray additive manufacturing, contributing to effective particle deposition.

A deep learning framework for automated anomaly detection and localization in fused filament fabrication

Under-extrusion poses a common challenge in filament 3D printing, necessitating precise adjustments to printing parameters for optimal resolution. Swift identification of this flaw is crucial for implementing timely corrective measures. In response, we present a novel machine learning approach designed to detect anomalies in 3D printing, specifically in the fused filament fabrication process (FFF). Our framework utilizes “You Only Look Once” (YOLO), a real-time object detection system, and “Visual Geometry Group-16” (VGG-16), a Convolutional Neural Network (CNN) model for image recognition, to accurately identify and localize under-extrusion events. Initially, models such as VGG-16, VGG-19, and ResNet-50 were trained without the inclusion of YOLO to establish baseline accuracies. Subsequently, an automated image pre-processing phase employs YOLO to discern the nozzle head, facilitating subsequent cropping around the region of interest for retraining the models. This inclusion of the nozzle head detection approach significantly improved the accuracy of all models, with the combined application of YOLO and VGG-16 demonstrating the most substantial enhancement, boosting detection accuracy to 97%. Moreover, Gradient-weighted Class Activation Mapping (Grad-CAM) technique, another CNN-based method, is employed to effectively highlight predicted areas in under-extrusion scenarios. While our method significantly advances the automatic detection of printing anomalies, it primarily serves as a diagnostic tool, signaling the need for intervention. Our rigorous testing on images of varying complexities confirms the model’s robustness, evaluated using comprehensive metrics such as precision, recall, and F1 score.

Super-resolution reconstruction of turbulence for Newtonian and viscoelastic fluids with a physical constraint

Super-resolution reconstruction (SR) of turbulent flow fields with high physical fidelity from low-resolution turbulence data is a novel and cost-effective way in a turbulence study. However, some naive image-to-image machine learning methods often produce nonphysical features inconsistent with the physical characteristics of turbulence. The present work proposes, respectively, convolutional neural network and generative adversarial network-based turbulence SR models using the kinetic energy spectra of turbulence flow as a physical constraint. The models have been validated in turbulence SR reconstruction for a Newtonian fluid under the flow condition of homogeneous isotropic turbulence at Reynolds number, Re=3140 and 4710 and viscoelastic fluid at the same Re numbers and Weisenberg number, Wi=0.796 and 1.194 (the elasticity number El=Wi/Re=0.000 254), respectively. The results show that with the energy spectra constraint (ESC), not only the nonphysical features occurred in the energy spectra of velocity field could be eliminated by the SR models, the errors of their reconstructed vorticity fields in comparison with the results of direct numerical simulation are also significantly smaller than those of the SR models without ESC. Therefore, incorporation of physical constraints is vital in preserving physical characteristics in SR of turbulent flow.

Feasibility study on discrimination of Polygonatum kingianum origins by NIR and MIR spectra data.

Most existing studies have focused on identifying the origin of species with protected geographical indications while neglecting to determine the proximate geographical origin of different species. In this study, we investigated the feasibility of using near- and mid-infrared spectroscopy to identify the origin of 156 Polygonatum kingianum samples from six regions in Yunnan, China. In this work, spectral images of different modes reveal more information about the P. kingianum. Comparing the performance of traditional machine learning models according to single spectrum and data fusion, the middle-level data fusion-principal component model has the best performance, and its sensitivity, specificity, and accuracy are all 1, and the model has the least number of variables. The residual convolutional neural network (ResNet) model constructed in the 1050-850cm-1 band confirms that fewer variables are beneficial in improving the accuracy of the model. In conclusion, this study verifies the feasibility of the proposed strategy and establishes a practical model to determine the source of P. kingianum.