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Abstract Capturing forceful interaction with deformable objects during manipulation benefits applications like virtual reality, telemedicine, and robotics. Replicating full hand-object states with complete geometry is challenging because of the occluded object deformations. Here, we report a visual-tactile recording and tracking system for manipulation featuring a stretchable tactile glove with 1152 force-sensing channels and a visual-tactile joint learning framework to estimate dynamic hand-object states during manipulation. To overcome the strain interference caused by contact with deformable objects, an active suppression method based on symmetric response detection and adaptive calibration is proposed and achieves 97.6% accuracy in force measurement, contributing to an improvement of 45.3%. The learning framework processes the visual-tactile sequence and reconstructs hand-object states. We experiment on 24 objects from 6 categories including both deformable and rigid ones with an average reconstruction error of 1.8 cm for all sequences, demonstrating a universal ability to replicate human knowledge in manipulating objects with varying degrees of deformability.

At present, in basketball teaching in China, the traditional basketball training method is for coaches to communicate face-to-face with athletes, observe their basketball movements, and judge the correctness of the movements based on the coach’s personal experience. However, this method mainly relies on the subjective judgment of the coach and lacks objective evaluation of athletes, making it impossible to objectively evaluate their performance. This article mainly studies an athlete’s incorrect action recognition model based on artificial intelligence algorithms and computer vision, and constructs an athlete’s incorrect action recognition model based on a dual channel 3D convolutional neural network (CNN). In this project, spatial attention mechanism (SA) was introduced into 3D CNN. By using inter frame difference information that can represent significant changes in athletes’ motion status, and combining it with grayscale video data, accurate recognition of athletes’ incorrect actions was achieved. The simulation results show that as the number of basketball technical errors increases, the recognition accuracy of this method decreases slowly. When the number of basketball technical errors reaches 400, the accuracy of action recognition is still as high as 87.552%. This indicates that this method can control the error rate within a reasonable range, improve the ability to identify basketball technical errors, and provide strong support for basketball teaching. In addition, the experimental results of this method also include various other achievements in performance calculation, further verifying its superiority in identifying basketball technical errors.

New power system that takes strong smart grid as the platform is supported by source-grid-load-storage interaction and multi-energy complementarity, because the system is green, efficient, flexible and digital. This paper focused on the research of multi-dimensional evaluation method of the new power system. Firstly, the internal and external elements of the new power system were sorted out, and KPI indicators were obtained through the analysis of the logical relationship of indicators. Secondly, this paper innovatively proposed to use “Generation Side Grid and Load” (GSGL) as the internal element and “Reliable Efficient Green and Intelligent” (REGI) as the evaluation effect. Meanwhile, this paper innovatively put forward a new multi-dimensional evaluation system of power system, with 4 secondary indicators and 16 tertiary indicators. Finally, the AAHM-weighted grey relational TOPSIS comprehensive evaluation model was constructed, and the validity and applicability of the proposed evaluation index system and model method were verified by numerical examples. Through the example analysis, under the new multi-dimensional evaluation system of power system, the comprehensive evaluation results and ranking of different provincial power enterprises were obtained. This paper provides theoretical support to further promote the transformation and upgrading of energy and electric power industry and power grid enterprises to speed up the construction of safe, reliable, green, efficient and intelligent modern power grid.

This paper proposes a double-tier bidding(DTB) model on the generation side to introduce gas-fired units(GFU) into the market bidding by analyzing the electricity cost of the whole society and the generation cost of gas-fired units. With the introduction of a sub-process, the GFUs are incorporated into the main bidding process or the sub-process independently, and the bidding prices of each process are cleared separately. Based on the traditional market bidding algorithm, the DTB model can effectively solve the problem of increasing market price after units with higher generation costs join the market. Besides, transmission congestion and power shortages during peak hours caused by the drawback of market mechanism can be avoided. In addition, recessive electricity price subsidy from markets is visualized, realizing the non-price advantages of low-emission units. Thus, the promotion of the overall optimal social benefits and the implementation of the low-carbon development concept are realized. Moreover, the algorithm incorporates a transverse comparison method of generation costs to restrict the market speculation of power suppliers. Numerical example shows that this algorithm is effective and feasible, and can be adopted in other areas with similar market characteristics.

We estimate the absolute age of the globular cluster NGC 3201 using 10,000 sets of theoretical isochrones constructed through Monte Carlo simulation using the Dartmouth Stellar Evolution Program. These isochrones take into consideration the uncertainty introduced by the choice of stellar evolution parameters. We fit isochrones with three detached eclipsing binaries and obtained an age independent of distance. We also fit isochrones with differential reddening corrected Hubble Space Telescope photometry data utilizing two different Hess diagram-based fitting methods. Results from three different methods analyzing two different types of data agree to within 1 σ , and we find the absolute age of NGC 3201 = 11.85 ± 0.74 Gyr. We also perform a variable importance analysis to study the uncertainty contribution from individual parameters, and we find the distance is the dominant source of uncertainty in photometry-based analysis, while total metallicity, helium abundance, α -element abundance, mixing length, and treatment of helium diffusion are an important source of uncertainties for all three methods.

Feline calicivirus (FCV) is one of the most important pathogens causing upper respiratory tract diseases in cats, posing a serious health threat to these animals. At present, FCV is mainly prevented through vaccination, but the protective efficacy of vaccines in China is limited. In this study, based on the differences in capsid proteins of isolates from different regions in China, as reported in our previous studies, seven representative FCV epidemic strains were selected and tested for their viral titers, virulence, immunogenicity, and extensive cross-protection. Subsequently, vaccine strains were selected to prepare inactivated vaccines. The whole-genome sequencing and analysis results showed that these seven representative FCV strains and 144 reference strains fell into five groups (A, B, C, D, and E). The strains isolated in China mainly fall into groups C and D, exhibiting regional characteristics. These Chinese isolates had a distant evolutionary relationship and low homology with the current FCV-255 vaccine strain. The screened FCV-HB7 and FCV-HB10 strains displayed desirable in vitro culture characteristics, with the highest virus proliferation titers (109.5 TCID50/mL) at 36 h post inoculation at a dose of 0.01 MOI. All five cats infected intranasally with FCV-HB7 or FCV-HB10 strains showed obvious clinical symptoms of FCV. The symptoms of cats infected with the FCV-HB7 strain were more severe than those infected with the FCV-HB10 strain. Both the single-strain inactivated immunization and combined bivalent inactivated vaccine immunization of FCV-HB7 and FCV-HB10 induced high neutralizing antibody titers in five cats immunized. Moreover, bivalent inactivated vaccine immunization protected cats from FCV-HB7 and FCV-HB10 strains. The cross-neutralizing antibody titer against seven representative FCV epidemic strains achieved by combined bivalent inactivated vaccine immunization was higher than that achieved by single-strain immunization, which was much higher than that achieved by commercial vaccine FCV-255 strain immunization. The above results suggest that the FCV-HB7 and FCV-HB10 strains screened in this study have great potential to become vaccine strains with broad-spectrum protective efficacy. However, their immune protective efficacy needs to be further verified by multiple methods before clinical application.

In most acute promyelocytic leukemia (APL) cells, promyelocytic leukemia (PML) fuses to retinoic acid receptor α (RARα) due to chromosomal translocation, thus generating PML/RARα oncoprotein, which is a relatively stable oncoprotein for degradation in APL. Elucidating the mechanism regulating the stability of PML/RARα may help to degrade PML/RARα and eradicate APL cells. Here, we describe a deubiquitinase (DUB)-involved regulatory mechanism for the maintenance of PML/RARα stability and develop a novel pharmacological approach to degrading PML/RARα by inhibiting DUB. We utilized a DUB siRNA library to identify the ovarian tumor protease (OTU) family member deubiquitinase YOD1 as a critical DUB of PML/RARα. Suppression of YOD1 promoted the degradation of PML/RARα, thus inhibiting APL cells and prolonging the survival time of APL cell-bearing mice. Subsequent phenotypic screening of small molecules allowed us to identify ubiquitin isopeptidase inhibitor I (G5) as the first YOD1 pharmacological inhibitor. As expected, G5 notably degraded PML/RARα protein and eradicated APL, particularly drug-resistant APL cells. Importantly, G5 also showed a strong killing effect on primary patient-derived APL blasts. Overall, our study not only reveals the DUB-involved regulatory mechanism on PML/RARα stability and validates YOD1 as a potential therapeutic target for APL, but also identifies G5 as a YOD1 inhibitor and a promising candidate for APL, particularly drug-resistant APL treatment.

In this paper, a fast scheme for solving unsteady nonlinear convection diffusion problems is proposed and analyzed. At each step, we firstly isolate a nonlinear convection subproblem and a linear diffusion subproblem from the original problem by utilizing operator splitting. By Taylor expansion, we explicitly transform the nonlinear convection one into a linear problem with artificial inflow boundary conditions associated with the nonlinear flux. Then a multistep technique is provided to relax the possible stability requirement, which is due to the explicit processing of the convection problem. Since the self-adjointness and coerciveness of diffusion subproblems, there are so many preconditioned iterative solvers to get them solved with high efficiency at each time step. When using the finite element method to discretize all the resulting subproblems, the major stiffness matrices are same at each step, that is the reason why the unsteady nonlinear systems can be computed extremely fast with the present method. Finally, in order to validate the effectiveness of the present scheme, several numerical examples including the Burgers type and Buckley-Leverett type equations, are chosen as the numerical study.

Tacrolimus is the principal immunosuppressive drug which is administered after heart transplantation. Managing tacrolimus therapy is challenging due to a narrow therapeutic index and wide pharmacokinetic (PK) variability. We aimed to establish a physiologically based pharmacokinetic (PBPK) model of tacrolimus in adult heart transplant recipients to optimize dose regimens in clinical practice. A 15-compartment full-PBPK model (Simbiology® Simulator, version 5.8.2) was developed using clinical observations from 115 heart transplant recipients. This study detected 20 genotypes associated with tacrolimus metabolism. CYP3A5*3 (rs776746), CYP3A4*18B (rs2242480), and IL-10 G-1082A (rs1800896) were identified as significant genetic covariates in tacrolimus pharmacokinetics. The PBPK model was evaluated using goodness-of-fit (GOF) and external evaluation. The predicted peak blood concentration (Cmax) and area under the drug concentration–time curve (AUC) were all within a two-fold value of the observations (fold error of 0.68–1.22 for Cmax and 0.72–1.16 for AUC). The patients with the CYP3A5*3/*3 genotype had a 1.60-fold increase in predicted AUC compared to the patients with the CYP3A5*1 allele, and the ratio of the AUC with voriconazole to alone was 5.80 when using the PBPK model. Based on the simulation results, the tacrolimus dosing regimen after heart transplantation was optimized. This is the first PBPK model used to predict the PK of tacrolimus in adult heart transplant recipients, and it can serve as a starting point for research on immunosuppressive drug therapy in heart transplant patients.

At present, the polarization of online public opinion is becoming more frequent, and individuals actively participate in attitude interactions more and more frequently. Thus, online views have become the dominant force in current public opinion. However, the rapid fermentation of polarized public opinion makes it very easy for actual topic views to go to extremes. Significantly, negative information seriously affects the healthy development of the social opinion ecology. Therefore, it is beneficial to maintain national credibility, social peace, and stability by exploring the communication structure of online public opinions, analyzing the logical model of extreme public attitudes, and guiding the communication of public opinions in a timely and reasonable manner. Starting from the J–A model and BA network, this paper explores the specific attributes of individuals and opinion network nodes. By incorporating parameters such as individual conformity and the strength of individual online relationships, we established a model of online group attitude polarization, then conducted simulation experiments on the phenomenon of online opinion polarization. Through simulations, we found that individual conformity and the difference in environmental attitude greatly influence the direction of opinion polarization events. In addition, crowd mentality makes individuals spontaneously choose the side of a particular, extreme view, which makes it easier for polarization to form and reach its peak.

Abstract This research addresses the issue of State of Charge (SOC) prediction for electric vehicle batteries by employing a dynamic Kalman neural network model. The model is optimized using a Genetic algorithm to adjust the neural network weights. Additionally, a strategy involving support vector machines for model optimization is proposed. This strategy involves preprocessing the data, selecting appropriate kernel functions for training, and merging prediction results to enhance the stability of the model. Results indicated that the Dynamic Genetic Kalman Neural Network (DGKNN) model achieved the minimum prediction error percentage of only 0.1529% when the correction coefficient was set to 0.7. The DGKNN model consistently exhibited the lowest error percentage, average absolute error, mean square error, and root mean square error when handling small, medium, and large datasets. For instance, in the small dataset, the error percentage was only 0.1518, and the root mean square error was only 0.0604. The research findings demonstrated that the proposed model exhibited high real-time accuracy in predicting battery SOC, enabling real-time monitoring of battery operating parameters. The method proposed in this study can accurately predict the state of battery charge, extend the life of battery packs, and improve the performance of electric vehicles. It has important significance for promoting the development of the electric vehicle industry.

Chronic obstructive pulmonary disease (COPD) is a widely prevalent disease with significant mortality and disability rates and has become the third leading cause of death globally. Patients with acute exacerbation of COPD (AECOPD) often substantially suffer deterioration and death. Therefore, COPD patients deserve special consideration regarding treatment in this fragile population for pre-clinical health management. Based on the above, this paper proposes an AECOPD prediction model based on the Auto-Metric Graph Neural Network (AMGNN) using inspiratory and expiratory chest low-dose CT images. This study was approved by the ethics committee in the First Affiliated Hospital of Guangzhou Medical University. Subsequently, 202 COPD patients with inspiratory and expiratory chest CT Images and their annual number of AECOPD were collected after the exclusion. First, the inspiratory and expiratory lung parenchyma images of the 202 COPD patients are extracted using a trained ResU-Net. Then, inspiratory and expiratory lung Radiomics and CNN features are extracted from the 202 inspiratory and expiratory lung parenchyma images by Pyradiomics and pre-trained Med3D (a heterogeneous 3D network), respectively. Last, Radiomics and CNN features are combined and then further selected by the Lasso algorithm and generalized linear model for determining node features and risk factors of AMGNN, and then the AECOPD prediction model is established. Compared to related models, the proposed model performs best, achieving an accuracy of 0.944, precision of 0.950, F1-score of 0.944, ad area under the curve of 0.965. Therefore, it is concluded that our model may become an effective tool for AECOPD prediction.

Coloration is a crucial trait that allows species to adapt and survive in different environments. Wild boars exhibit alternating black (dark) and yellow (light) longitudinal stripes on their back during their infancy (juvenile stripes), and as adults, they transform into uniform wild-type coat color. Aiming to record the procedure of juvenile stripes disappearing, piglets (WD) with juvenile stripes were produced by crossing a wild boar with Duroc sows, and photos of their coat color were taken from 20 d to 220 d. The pigments in the hairs from the black and yellow stripes were determined. Furthermore, the differentially expressed genes between the black and yellow stripes were investigated in 5 WD with the age of 30 d using whole-transcriptome sequencing to explore the genetic mechanism of the juvenile stripes. The juvenile stripes started to disappear at about 70 d, and stripes were not distinguished with the naked eye at about 160 d; that is, the juvenile stripe completely disappeared. A hotspot of a differentially expressing (DE) region was found on chromosome 13, containing/covering 2 of 13 DE genes and 8 of 10 DE lncRNAs in this region. A network among ZIC4, ssc-miR-532-3p, and ENSSSCG00000056225 might regulate the formation of juvenile stripes. Altogether, this study provides new insights into spatiotemporal coat color pattern.

Abstract Background A large proportion of pulmonary embolism (PE) heritability remains unexplained, particularly among the East Asian (EAS) population. Our study aims to expand the genetic architecture of PE and reveal more genetic determinants in Han Chinese. Methods We conducted the first genome-wide association study (GWAS) of PE in Han Chinese, then performed the GWAS meta-analysis based on the discovery and replication stages. To validate the effect of the risk allele, qPCR and Western blotting experiments were used to investigate possible changes in gene expression. Mendelian randomization (MR) analysis was employed to implicate pathogenic mechanisms, and a polygenic risk score (PRS) for PE risk prediction was generated. Results After meta-analysis of the discovery dataset (622 cases, 8853 controls) and replication dataset (646 cases, 8810 controls), GWAS identified 3 independent loci associated with PE, including the reported loci FGG rs2066865 (p-value = 3.81 × 10−14), ABO rs582094 (p-value = 1.16 × 10−10) and newly reported locus FABP2 rs1799883 (p-value = 7.59 × 10−17). Previously reported 10 variants were successfully replicated in our cohort. Functional experiments confirmed that FABP2-A163G(rs1799883) promoted the transcription and protein expression of FABP2. Meanwhile, MR analysis revealed that high LDL-C and TC levels were associated with an increased risk of PE. Individuals with the top 10% of PRS had over a fivefold increased risk for PE compared to the general population. Conclusions We identified FABP2, related to the transport of long-chain fatty acids, contributing to the risk of PE and provided more evidence for the essential role of metabolic pathways in PE development.

Computed tomography (CT) has been the most effective modality for characterizing and quantifying chronic obstructive pulmonary disease (COPD). Radiomics features extracted from the region of interest in chest CT images have been widely used for lung diseases, but they have not yet been extensively investigated for COPD. Therefore, it is necessary to understand COPD from the lung radiomics features and apply them for COPD diagnostic applications, such as COPD stage classification. Lung radiomics features are used for characterizing and classifying the COPD stage in this paper. First, 19 lung radiomics features are selected from 1316 lung radiomics features per subject by using Lasso. Second, the best performance classifier (multi-layer perceptron classifier, MLP classifier) is determined. Third, two lung radiomics combination features, Radiomics-FIRST and Radiomics-ALL, are constructed based on 19 selected lung radiomics features by using the proposed lung radiomics combination strategy for characterizing the COPD stage. Lastly, the 19 selected lung radiomics features with Radiomics-FIRST/Radiomics-ALL are used to classify the COPD stage based on the best performance classifier. The results show that the classification ability of lung radiomics features based on machine learning (ML) methods is better than that of the chest high-resolution CT (HRCT) images based on classic convolutional neural networks (CNNs). In addition, the classifier performance of the 19 lung radiomics features selected by Lasso is better than that of the 1316 lung radiomics features. The accuracy, precision, recall, F1-score and AUC of the MLP classifier with the 19 selected lung radiomics features and Radiomics-ALL were 0.83, 0.83, 0.83, 0.82 and 0.95, respectively. It is concluded that, for the chest HRCT images, compared to the classic CNN, the ML methods based on lung radiomics features are more suitable and interpretable for COPD classification. In addition, the proposed lung radiomics combination strategy for characterizing the COPD stage effectively improves the classifier performance by 12% overall (accuracy: 3%, precision: 3%, recall: 3%, F1-score: 2% and AUC: 1%).

With the increasing popularity of wearable devices, flexible electronics with a negative permittivity property have been widely applied to wearable devices, sensors, and energy storage. In particular, a low-frequency dispersion negative permittivity in a wide frequency range can effectively contribute to the stable working performance of devices. In this work, polyvinylidene fluoride (PVDF) was selected as the flexible matrix, and copper nanowires (CuNWs) were used as the conductive functional filler to prepare a flexible CuNWs/PVDF composite film with a low-frequency dispersion negative permittivity. As the content of CuNWs increased, the conductivity of the resulting composites increased sharply and presented a metal-like behavior. Moreover, the negative permittivity consistent with the Drude model was observed when CuNWs formed a percolative network. Meanwhile, the negative permittivity exhibited a low-frequency dispersion in the whole test frequency range, and the fluctuation of the permittivity spectra was relatively small (−760 to −584) at 20 kHz–1 MHz. The results revealed that the high electron mobility of CuNWs is reasonable for the low-frequency dispersion of negative permittivity. CuNWs/PVDF composite films with a frequency-independent negative permittivity provide a new idea for the development of flexible wearable electronic devices.

IntroductionBecause of persistent airflow limitation in chronic obstructive pulmonary disease (COPD), patients with COPD often have complications of dyspnea. However, as a leading symptom of COPD, dyspnea in COPD deserves special consideration regarding treatment in this fragile population for pre-clinical health management in COPD. Methods: Based on the above, this paper proposes a multi-modal data combination strategy by combining the local and global features for dyspnea identification in COPD based on the multi-layer perceptron (MLP) classifier.MethodsFirst, lung region images are automatically segmented from chest HRCT images for extracting the original 1,316 lung radiomics (OLR, 1,316) and 13,824 3D CNN features (O3C, 13,824). Second, the local features, including five selected pulmonary function test (PFT) parameters (SLF, 5), 28 selected lung radiomics (SLR, 28), and 22 selected 3D CNN features (S3C, 22), are respectively selected from the original 11 PFT parameters (OLF, 11), 1,316 OLR, and 13,824 O3C by the least absolute shrinkage and selection operator (Lasso) algorithm. Meantime, the global features, including two fused PFT parameters (FLF, 2), six fused lung radiomics (FLR, 6), and 34 fused 3D CNN features (F3C, 34), are respectively fused by 11 OLF, 1,316 OLR, and 13,824 O3C using the principal component analysis (PCA) algorithm. Finally, we combine all the local and global features (SLF + FLF + SLR + FLR + S3C + F3C, 5+ 2 + 28 + 6 + 22 + 34) for dyspnea identification in COPD based on the MLP classifier.ResultsOur proposed method comprehensively improves classification performance. The MLP classifier with all the local and global features achieves the best classification performance at 87.7% of accuracy, 87.7% of precision, 87.7% of recall, 87.7% of F1-scorel, and 89.3% of AUC, respectively.DiscussionCompared with single-modal data, the proposed strategy effectively improves the classification performance for dyspnea identification in COPD, providing an objective and effective tool for COPD management.

BackgroundChronic obstructive pulmonary disease (COPD), a preventable lung disease, has the highest prevalence in the elderly and deserves special consideration regarding earlier warnings in this fragile population. The impact of age on COPD is well known, but the COPD risk of the aging process in the lungs remains unclear. Therefore, it is necessary to understand the COPD risk of the aging process in the lungs, providing an early COPD risk decision for adults.MethodsCOPD risk is evaluated for adults to make an early COPD risk decision from the perspective of lung radiomics features. First, the subjects are divided into four groups according to the COPD stages. Their ages are divided into eight equal age intervals in each group. Second, four survival Cox models are established based on the lung radiomics features to evaluate the risk probability from COPD stage 0 to suffering COPD and COPD stages. Finally, four risk ranks are defined by equally dividing the COPD risk probability from 0 to 1. Subsequently, the COPD risk at different stages is evaluated with varying age intervals to provide an early COPD risk decision.ResultsThe evaluation metrics area under the curve (AUC)/C index of four survival Cox models are 0.87/0.94, 0.84/0.83, 0.94/0.89, and 0.97/0.86, respectively, showing the effectiveness of the models. The risk rank levels up every 5 years for the subjects who had suffered COPD after 60. For the subjects with COPD stage 0, the risk rank of suffering COPD stage I levels up every 5 years after the age of 65 years, and the risk rank of suffering COPD stages II and III & IV levels up every 5 years after the age of 70 years.ConclusionOnce the age is above 60 years, the patients with COPD need to take action to prevent the progress and deterioration of COPD. Once the age is above 65 years, the patients with COPD stage 0 need to take precautions against COPD.

Chronic obstructive pulmonary disease (COPD) is a preventable, treatable, progressive chronic disease characterized by persistent airflow limitation. Patients with COPD deserve special consideration regarding treatment in this fragile population for preclinical health management. Therefore, this paper proposes a novel lung radiomics combination vector generated by a generalized linear model (GLM) and Lasso algorithm for COPD stage classification based on an auto-metric graph neural network (AMGNN) with a meta-learning strategy. Firstly, the parenchyma images were segmented from chest high-resolution computed tomography (HRCT) images by ResU-Net. Second, lung radiomics features are extracted from the parenchyma images by PyRadiomics. Third, a novel lung radiomics combination vector (3 + 106) is constructed by the GLM and Lasso algorithm for determining the radiomics risk factors (K = 3) and radiomics node features (d = 106). Last, the COPD stage is classified based on the AMGNN. The results show that compared with the convolutional neural networks and machine learning models, the AMGNN based on constructed novel lung radiomics combination vector performs best, achieving an accuracy of 0.943, precision of 0.946, recall of 0.943, F1-score of 0.943, and ACU of 0.984. Furthermore, it is found that our method is effective for COPD stage classification.