Meditation aims to guide individuals into a state of deep calm and focused attention, and in recent years, it has shown promising potential in the field of medical treatment. Numerous studies have demonstrated that electroencephalogram (EEG) patterns change during meditation, suggesting the feasibility of using deep learning techniques to monitor meditation states. However, significant inter-subject differences in EEG signals poses challenges to the performance of such monitoring systems. To address this issue, this study proposed a novel model-calibrated multi-source adversarial adaptation network (CMAAN). The model first trained multiple domain-adversarial neural networks in a pairwise manner between various source-domain individuals and the target-domain individual. These networks were then integrated through a calibration process using a small amount of labeled data from the target domain to enhance performance. We evaluated the proposed model on an EEG dataset collected from 18 subjects undergoing methamphetamine rehabilitation. The model achieved a classification accuracy of 73.09%. Additionally, based on the learned model, we analyzed the key EEG frequency bands and brain regions involved in the meditation process. The proposed multi-source domain adaptation framework improves both the performance and robustness of EEG-based meditation monitoring and holds great promise for applications in biomedical informatics and clinical practice.
Humans ; Electroencephalography/methods* ; Meditation ; Calibration ; Neural Networks, Computer ; Brain/physiology* ; Rest/physiology* ; Deep Learning ; Signal Processing, Computer-Assisted

In animal navigation, head direction is encoded by head direction cells within the olfactory-hippocampal structures of the brain. Even in darkness or unfamiliar environments, animals can estimate their head direction by integrating self-motion cues, though this process accumulates errors over time and undermines navigational accuracy. Traditional strategies rely on visual input to correct head direction, but visual scenes combined with self-motion information offer only partially accurate estimates. This study proposed an innovative calibration mechanism that dynamically adjusts the association between visual scenes and head direction based on the historical firing rates of head direction cells, without relying on specific landmarks. It also introduced a method to fine-tune error correction by modulating the strength of self-motion input to control the movement speed of the head direction cell activity bump. Experimental results showed that this approach effectively reduced the accumulation of self-motion-related errors and significantly enhanced the accuracy and robustness of the navigation system. These findings offer a new perspective for biologically inspired robotic navigation systems and underscore the potential of neural mechanisms in enabling efficient and reliable autonomous navigation.
Animals ; Neural Networks, Computer ; Calibration ; Spatial Navigation/physiology* ; Head Movements/physiology* ; Neurons/physiology* ; Models, Neurological ; Head/physiology* ; Action Potentials/physiology*

Medical robotic arm often encounters multi-source and nonlinear errors during the calibration process, making it difficult for traditional mathematical modeling methods to fully characterize system error features, thereby limiting further improvement in calibration accuracy. In this study, a robotic arm parameter error identification model is established, and a calibration method based on an adaptive long short-term memory (ALSTM) neural network is proposed. The method incorporates a particle swarm optimization (PSO) algorithm to optimize the weights of each layer of the LSTM neural network, enabling more effective fitting of robotic arm kinematic errors and ultimately yielding more accurate Denavit-Hartenberg (D-H) parameters. To validate the proposed approach, 110 sets of experimental data are collected using the HSR-JR680 robotic arm calibration system. Experimental results demonstrate that the ALSTM model reduces the root mean square error (RMSE) by 23.07%-80.39% compared to traditional calibration methods, and shortens the convergence time by 32.44% compared to a standard LSTM model. The optimized D-H parameters obtained meet the high-precision calibration requirements of medical robotic arm, confirming the effectiveness of the proposed method.
Calibration ; Neural Networks, Computer ; Algorithms ; Robotics ; Robotic Surgical Procedures ; Models, Theoretical

OBJECTIVES: We propose a new melanoma segmentation model, SG-UNet, to enhance the precision of melanoma segmentation in dermascopy images to facilitate early melanoma detection. METHODS: We utilized a U-shaped convolutional neural network, UNet, and made improvements to its backbone, skip connections, and downsampling pooling sections. In the backbone, with reference to the structure of VGG, we increased the number of convolutions from 10 to 13 in the downsampling part of UNet to achieve a deepened network hierarchy that allowed capture of more refined feature representations. To further enhance feature extraction and detail recognition, we replaced the traditional convolution the backbone section with self-calibrated convolution to enhance the model's ability to capture both spatial and channel dimensional features. In the pooling part, the original pooling layer was replaced by Haar wavelet downsampling to achieve more effective multi-scale feature fusion and reduce the spatial resolution of the feature map. The global attention mechanism was then incorporated into the skip connections at each layer to enhance the understanding of contextual information of the image. RESULTS: The experimental results showed that the SG-UNet model achieved significantly improved segmentation accuracy on ISIC 2017 and ISIC 2018 datasets as compared with other current state-of-the-art segmentation models, with Dice reached 92.41% and 86.62% and IoU reaching 92.31% and 86.48% on the two datasets, respectively. CONCLUSIONS The proposed model is capable of effective and accurate segmentation of melanoma from dermoscopy images.
Melanoma/diagnosis* ; Humans ; Neural Networks, Computer ; Dermoscopy/methods* ; Skin Neoplasms ; Image Processing, Computer-Assisted/methods* ; Calibration ; Algorithms

The construction method and simulation parameter settings for the discrete element model of Jianwei Xiaoshi Granules, as the primary material of Jianwei Xiaoshi Tablets, are not yet clear. The accuracy of the simulation model significantly influences the dynamic response characteristics between granules. Therefore, it is necessary to calibrate the parameters to improve the accuracy of the simulation parameters. Using the repose angle of Jianwei Xiaoshi Granules as the response value, the response surface methodology was employed to optimize and calibrate the discrete element parameters. Physical experiments were conducted to determine the physical properties of Jianwei Xiaoshi Granules. Based on the Hertz-Mindlin with Johnson-Kendall-Roberts(JKR) V2 model and virtual simulation methods, a repose angle determination model was constructed in EDEM software. The repose angle was measured using image analysis and numerical fitting methods. The Plackett-Burman experiment was used to screen the initial parameters for significance in the discrete element simulation. The significant parameters were then subjected to a steepest ascent experiment to determine the optimal parameter range. Furthermore, based on the Box-Behnken experiment, a second-order regression equation between significant parameters and repose angle was established, with the repose angle of 37.64° in the physical experiment as the target value. The regression equation was optimized and solved. The significance screening experiment revealed that the granule-granule static friction coefficient, granule-granule rolling friction, and granule-steel plate rolling friction of Jianwei Xiaoshi Granules significantly influenced the simulated repose angle. The optimal parameter combination was found to be 0.330, 0.222, and 0.229. The simulation results with this optimal parameter combination showed that there was no significant difference between the simulated repose angle and the repose angle obtained in the physical experiment, with a relative error of 0.05%, which further validated the reliability of the calibrated discrete element parameters for Jianwei Xiaoshi Granules.
Drugs, Chinese Herbal/chemistry* ; Calibration ; Computer Simulation

OBJECTIVE: To propose a sensitivity test method for geometric correction position deviation of cone-beam CT systems. METHODS: We proposed the definition of center deviation and its derivation. We analyzed the influence of the variation of the three-dimensional spatial center of the steel ball point, the projection center and the size of the steel ball point on the deviation of geometric parameters and the reconstructed image results by calculating the geometric correction parameters based on the Noo analytical method using the FDK reconstruction algorithm for image reconstruction. RESULTS: The radius of the steel ball point was within 3 mm. The deviation of the center of the calibration parameter was within the order of magnitude and negligible. A 10% Gaussian perturbation of a single pixel in the 3D spatial coordinates of the steel ball point produced a deviation of about 3 pixel sizes, while the same Gaussian perturbation of the 2D projection coordinates of the steel ball point produced a deviation of about 2 pixel sizes. CONCLUSION The geometric correction is more sensitive to the deviation generated by the three-dimensional spatial coordinates of the steel ball point with limited sensitivity to the deviation generated by the two-dimensional projection coordinates of the steel ball point. The deviation sensitivity of a small diameter steel ball point can be ignored.
Algorithms ; Calibration ; Cone-Beam Computed Tomography ; Steel

OBJECTIVE: To construct an inherent interpretability machine learning model as an explainable boosting machine model (EBM) for predicting one-year risk of death in patients with severe ischemic stroke. METHODS: We randomly divided the data of 2369 eligible patients with severe ischemic stroke in the MIMIC-Ⅳ(2.0) database, who were admitted in ICU in 2008 to 2019, into a training dataset (80%) and a test dataset (20%), and assessed the prognosis of the patients using the EBM model. The prediction performance of the model was evaluated by calculating the area under the receiver operating characteristic (AUC) curve. The calibration curve and Brier score were used to evaluate the degree of calibration of the model, and a decision curve was generated to assess the net clinical benefit. RESULTS: The EBM model constructed in this study had good discrimination power, calibration and net benefit, with an AUC of 0.857 (95% CI: 0.831-0.887) for predicting prognosis of severe ischemic stroke. Calibration curve analysis showed that the standard curve of the EBM model was the closest to the ideal curve. Decision curve analysis showed that the model had the greatest net benefit rate at the prediction probability threshold of 0.10 to 0.80. The top 5 independent predictive variables based on the EBM model were age, SOFA score, mean heart rate, mechanical ventilation, and mean respiratory rate, whose significance scores ranged from 0.179 to 0.370. CONCLUSION This EBM model has a good performance for predicting the risk of death within one year in patients with severe ischemic stroke and allows clinicians to better understand the contributing factors of the patients' outcomes through the model interpretability.
Humans ; Ischemic Stroke ; Calibration ; Databases, Factual ; Intensive Care Units ; Machine Learning

OBJECTIVE: To compare the predictive ability of two extended Cox models in nonlinear survival data analysis. METHODS: Through Monte Carlo simulation and empirical study and with the conventional Cox Proportional Hazards model and Random Survival Forests as the reference models, we compared restricted cubic spline Cox model (Cox_RCS) and DeepSurv neural network Cox model (Cox_DNN) for their prediction ability in nonlinear survival data analysis. Concordance index was used to evaluate the differentiation of the prediction results (a larger concordance index indicates a better prediction ability of the model). Integrated Brier Score was used to evaluate the calibration degree of the prediction (a smaller index indicates a better prediction ability). RESULTS: For data that met requirement of the proportion risk, the Cox_RCS model had the best prediction ability regardless of the sample size or deletion rate. For data that failed to meet the proportion risk, the prediction ability of Cox_DNN was optimal for a large sample size (≥500) with a low deletion (< 40%); the prediction ability of Cox_RCS was superior to those of other models in all other scenarios. For example data, the Cox_RCS model showed the best performance. CONCLUSION In analysis of nonlinear low maintenance data, Cox_RCS and Cox_DNN have their respective advantages and disadvantages in prediction. The conventional survival analysis methods are not inferior to machine learning or deep learning methods under certain conditions.
Proportional Hazards Models ; Survival Analysis ; Calibration ; Computer Simulation ; Data Analysis

The mixing process is a critical link in the formation of oral solid preparations of traditional Chinese medicine. This paper took the extract powder of Guizhi Fuling Capsules and Paeonol powder as research objects. The angle of repose, loose packing density, and particle size of the two powders were measured to calibrate discrete element simulation parameters for the mixing process. The discrete element method was used to calibrate the simulated solid density of Paeonol powder and extract powder of Guizhi Fuling Capsules based on the Hertz-Mindlin with JKR V2 contact model and particle scaling. The Plackett-Burman experimental design was used to screen out the critical contact parameters that had a significant effect on the simulation of the angle of repose. The regression model between the critical contact parameters and the simulated angle of repose was established by the Box-Behnken experimental design, and the critical contact parameters of each powder were optimized based on the regression model. The best combination of critical contact parameters of the extract powder of Guizhi Fuling Capsules was found to be 0.51 for particle-particle static friction coefficient, 0.31 for particle-particle rolling friction coefficient, and 0.64 for particle-stainless steel static friction coefficient. For Paeonol powder, the best combination of critical contact parameters was 0.4 for particle-particle static friction coefficient and 0.19 for particle-particle rolling friction coefficient. The best combination of contact parameters between Paeonol powder and extract powder of Guizhi Fuling Capsules was 0.27 for collision recovery coefficient, 0.49 for static friction coefficient, and 0.38 for rolling friction coefficient. The verification results show that the relative error between the simulated value and the measured value of the angle of repose of the two single powders is less than 1%, while the relative error between the simulated value and the measured value of the angle of repose of the mixed powder with a mass ratio of 1∶1 is less than 4%. These research results provide reliable physical property simulation data for the mixed simulation experiment of extract powder of Guizhi Fuling Capsules and Paeonol powder.
Wolfiporia ; Calibration ; Powders ; Medicine, Chinese Traditional ; Capsules

Keloids are benign skin tumors resulting from the excessive proliferation of connective tissue in wound skin. Precise prediction of keloid risk in trauma patients and timely early diagnosis are of paramount importance for in-depth keloid management and control of its progression. This study analyzed four keloid datasets in the high-throughput gene expression omnibus (GEO) database, identified diagnostic markers for keloids, and established a nomogram prediction model. Initially, 37 core protein-encoding genes were selected through weighted gene co-expression network analysis (WGCNA), differential expression analysis, and the centrality algorithm of the protein-protein interaction network. Subsequently, two machine learning algorithms including the least absolute shrinkage and selection operator (LASSO) and the support vector machine-recursive feature elimination (SVM-RFE) were used to further screen out four diagnostic markers with the highest predictive power for keloids, which included hepatocyte growth factor (HGF), syndecan-4 (SDC4), ectonucleotide pyrophosphatase/phosphodiesterase 2 (ENPP2), and Rho family guanosine triphophatase 3 (RND3). Potential biological pathways involved were explored through gene set enrichment analysis (GSEA) of single-gene. Finally, univariate and multivariate logistic regression analyses of diagnostic markers were performed, and a nomogram prediction model was constructed. Internal and external validations revealed that the calibration curve of this model closely approximates the ideal curve, the decision curve is superior to other strategies, and the area under the receiver operating characteristic curve is higher than the control model (with optimal cutoff value of 0.588). This indicates that the model possesses high calibration, clinical benefit rate, and predictive power, and is promising to provide effective early means for clinical diagnosis.
Humans ; Keloid/genetics* ; Nomograms ; Algorithms ; Calibration ; Machine Learning