Discrimination Capability Research Articles

A nomogram model to predict non-retrieval of short-term retrievable inferior vena cava filters

ObjectiveTo develop and validate a nomogram for predicting non-retrieval of the short-term retrievable inferior vena cava (IVC) filters.MethodsIn this study, univariate and multivariate logistic regression analyses were performed to identify predictive factors of short-term retrievable filter (Aegisy or OptEase) non-retrieval, and a nomogram was then established based on these factors. The nomogram was created based on data from a training cohort and validated based on data from a validation cohort. The predictive value of the nomogram was estimated using area under the curve (AUC) and calibration curve analysis (Hosmer-Lemeshow test).ResultsA total of 1,321 patients who had undergone placement of short-term retrievable filters (Aegisy or OptEase) were included in the analysis. The overall retrieval rate was 68.7%. Age, proximal and distal deep vein thrombosis (DVT) vs. distal DVT, active cancer, history of long-term immobilization, VTE was detected in the intensive care unit, active/recurrent bleeding, IVC thrombosis, and history of venous thromboembolism were independent predictive risk factors for non-retrieval of filters. Interventional therapy for DVT, acute fracture, and interval of ≥14 days between filter placement and patient discharge were independent protective factors for non-retrieval of filters. The nomogram based on these factors demonstrated good ability to predict the non-retrieval of filters (training cohort AUC = 0.870; validation cohort AUC = 0.813.ConclusionThis nomogram demonstrated strong predictive accuracy and discrimination capability. This model may help clinicians identify patients who are not candidates for short-term retrievable filter placement and help clinicians make timely, individualized decisions in filter choice strategies.

Dynamic-Aware Network for Moving Object Detection

Moving object detection (MOD) plays an important role in many applications that aim to identify regions of interest in videos. However, most existing MOD methods ignore the variability brought by time-varying information. Additionally, many network frameworks primarily focus on low-level feature learning, neglecting the higher-level contextual understanding required for accurate detection. To solve the above issues, we propose a symmetric Dynamic-Aware Network (DAN) for MOD. DAN explores the interactions between different types of information via structural design and feature optimization. To locate the object position quickly, we build a Siamese convolutional network to emphasize changes in the scene. Subsequently, a Change-Aware Module (CAM) is designed, which can maximize the perception of object change cues by exploiting complementary depth-varying features and different levels of disparity information, thereby enhancing the feature discrimination capability of the network. Moreover, to reinforce the effective transfer between features, we devise a Motion-Attentive Selection Module (MASM) to construct an autonomous decoder for augmenting detail representation. Experimental results on benchmark datasets indicate the rationality and validity of the proposed approach.

Channel Attention-Based Conditional Diffusion Model Applied to Fault Diagnosis Under Imbalanced Data

Issues such as data scarcity and data imbalance have long posed significant difficulties in the field of intelligent fault diagnosis. They lead to reduced diagnostic accuracy and endanger the safety and reliability of industrial equipment. To address these challenges, this study introduces a novel channel attention-based conditional diffusion model (CAC-DM) that recalibrates features through a squeeze-and-excitation process. This enhancement boosts the model’s ability to focus on critical features while suppressing irrelevant information, thereby improving the UNet network’s discrimination capability in handling small-sample faults that are highly similar in nature. Experimental validation demonstrates that CAC-DM performs exceptionally well in scenarios with high class similarity, effectively distinguishing among categories with similar distributions in limited data and generating high-quality samples. Compared to existing generative methods, the CAC-DM exhibits significant advantages in producing distinguishable fault samples, particularly in cases of sample imbalance. This approach offers an effective new solution for fault diagnosis.

DEN-HMM: Deep Emission Network Based Hidden Markov Model with Time-Evolving Multivariate Observations

A Hidden Markov Model (HMM) is a popular statistical modeling technique for system health state estimation, monitoring, and prognosis. However, most existing HMMs adopted some simple parametric probability distribution as the distribution of observations for a given state and thus cannot capture the intricate dependency of observations on state and possibly other covariates such as time. To address this, we propose a Deep Emission Network-based Hidden Markov Model (DEN-HMM) to capture the complex evolution of multivariate observations with respect to state and time. We also address the challenging issue of state nondiscrimination in DEN-HMM. To overcome this, we propose a regularized loss function that can prevent certain non-discriminative trivial solutions and enhance the state discriminative capabilities of DEN-HMM. The study further demonstrates extensive numerical studies to show the effectiveness of the proposed DEN-HMM, including a case study on steady-state estimation in ultrasonic cavitation-based dispersion processes.

Differences in near-infrared fluorescence imaging and histological analysis of cheek mucosa in golden hamsters with different pathological states.

To explore and analyze the feasibility of using indocyanine green (ICG) near-infrared fluorescence (NIF) imaging technology for the early diagnosis of oral potential malignant disorders and oral squamous cell carcinoma. 7,12-Dimethylbenz[a]anthracene in acetone solution was used to induce various pathological models of buccal mucosal lesions (mild/moderate dysplasia, severe dysplasia, squamous cell carcinoma) in golden hamster. ICG-NIF was conducted for the quantitative analysis of the fluorescence signal of lesion tissue, and evaluation of the diagnostic and discriminative capabilities of the ICG-NIF technology for mucosal lesions in various pathological states. Immunohistochemical staining was perform to examine the microvessel density (MVD) and microlymphatic vessel density (MLVD) of mucosa in various pathological states and explore the histological reasons underlying the differences in fluorescence signals. The results of ICG-NIF fluorescence quantitative analysis reveal the higher fluorescence intensity of mucosal lesions in the experimental group compared with that of the normal mucosa on the control side, with statistical differences (P<0.05). Moreover, the more severe the malignancy of mucosal lesions in the experimental group, the higher the fluorescence intensity. According to histopathological analysis, the malignant progression of mucosal lesions in golden hamsters was accompanied with an increase in MVD (P<0.05) and a decrease in MLVD (P<0.05). The abnormal proliferation of mucosal lesions in golden hamsters exhibits a difference in ICG-NIF fluorescence signal compared with normal mucosal tissue. Fluorescence quantitative analysis methods can provide assistance in differentiation and show potential for clinical applications.

A new diatom-based multimetric index (MMI-D) for ecological health monitoring in the Tropical Rift Valley Lake (Lake Hawassa, Ethiopia)

Multimetric assessment is one of the important tools for diagnosing, detecting and measuring impairment in ecosystem function in lentic ecosystems. It enhances detection capabilities across a broader variety of stressors and offers a more complete picture of ecological conditions than single metrics or biological indicators. In this context, a diatom-based multimetric index (MMI-D) has been developed to evaluate the ecological health of Lake Hawassa. Physicochemical and benthic diatom sampling was conducted at nine sites along the lakeshore, representing varying levels of human disturbance, from February to November in2015 and 2016. The sampling sites were classified a priori into three categories: minimally disturbed (three sites), moderately disturbed (three sites) and highly disturbed (three sites). This classification, was based on a clustering analysis using the percent disturbance score (PDS). Of the 24-diatom candidate metrics, only ten were chosen as core metrics for the development of MMI-D, based on redundancy analysis, reaction to environmental conditions, percent discriminatory efficiency (%DE) and box plots. The newly established MMI-D clearly distinguished between reference and non-reference sites, and between the lake’s three clusters. The MMI-D’s performance was validated using independent data sets from lakes Hawassa and Ziway and it demonstrated the best capability for discrimination between different disturbance levels. MMI-D 2-stage Least Squares (2SLS) regression analysis revealed an inverse but robust connection with the PDS, indicating its responsiveness to Lake Hawassa habitat quality degradation (n = 9, R 2 = 0.921, p < 0.001). The MMI-D revealed a high %DE (95.1%) and a negative but significant connection with nutrients, total suspended solids (TSS), and turbidity (R 2 > 0.6; p < 0.05). Generally, it can be concluded that this index is a powerful tool that could assist endusers by providing a practical method for measuring the ecological quality of Lake Hawassa.

Domain-Invariant Few-Shot Contrastive Learning for Hyperspectral Image Classification

In Hyperspectral Image (HSI) classification, acquiring large quantities of high-quality labeled samples is typically costly and impractical. Traditional deep learning methods are limited in such scenarios due to their dependence on sample quantities. To address this challenge, researchers have turned to Few-Shot Learning (FSL). Although existing FSL methods improve classification performance by enhancing domain invariance through domain adaptation, they often overlook the critical issue of high inter-class similarity and large intra-class variability. Moreover, during domain alignment, features of different categories within the same domain may become confused. To address these issues, this paper proposes a novel Domain-Invariant Few-Shot Contrastive Learning (DIFSCL) method, which combines domain adaptation and contrastive learning strategies to not only learn domain-invariant features but also significantly enhance inter-class discriminability. Based on this, we further design a multi-scale adaptive attention mechanism for a hyperspectral feature extraction network to more effectively extract and optimize generalized features within the DIFSCL framework, significantly improving intra-class consistency and inter-class discriminative capability of features. Experimental results on three widely used HSI datasets demonstrate that our method significantly outperforms existing techniques in few-shot classification tasks.

Retrieval of cloud fraction using machine learning algorithms based on FY-4A AGRI observations

Abstract. Cloud fraction as a vital component of meteorological satellite products plays an essential role in environmental monitoring, disaster detection, climate analysis and other research areas. Random forest (RF) and multilayer perceptron (MLP) algorithms were used in this paper to retrieve the cloud fraction of AGRI (Advanced Geosynchronous Radiation Imager) on board the Fengyun-4A (FY-4A) satellite based on its full-disk level-1 radiance observation. Corrections have been made subsequently to the retrieved cloud fraction in areas where solar glint occurs using a correction curve fitted with sunglint angle as weight. The algorithm includes two steps: the cloud detection is conducted firstly for each AGRI field of view to identify whether it is clear sky, partly cloudy or overcast within the observation field. Then, the cloud fraction is retrieved for the scene identified as partly cloudy. The 2B-CLDCLASS-lidar cloud fraction product from the CloudSat and CALIPSO active remote sensing satellite is employed as the truth to assess the accuracy of the retrieval algorithm. Comparison with the operational AGRI level-2 cloud fraction product is also conducted at the same time. The results indicate that both the RF and MLP cloud detection models achieved high accuracy, surpassing that of operational products. However, both algorithms demonstrated weaker discrimination capabilities for partly cloudy conditions compared to clear-sky and overcast situations. Specifically, they tended to misclassify fields of view with low cloud fractions (e.g., cloud fraction = 0.16) as clear sky and those with higher cloud fractions (e.g., cloud fraction = 0.83) as overcast. Between the two models, RF exhibited higher overall accuracy. Both RF and MLP models performed well in cloud fraction retrieval, showing lower mean error (ME), mean absolute error (MAE) and root mean square error (RMSE) compared to operational products. The ME for both RF and MLP cloud fraction retrieval models was close to zero, while RF had slightly lower MAE and RMSE than MLP. During daytime, the high reflectance in sunglint areas led to larger retrieval errors for both RF and MLP algorithms. However, after correction, the retrieval accuracy in these regions improved significantly. At night, the absence of visible light observations from the AGRI instrument resulted in lower classification accuracy compared to daytime, leading to higher cloud fraction retrieval errors during nighttime.

A Dual-Branch Residual Network with Attention Mechanisms for Enhanced Classification of Vaginal Lesions in Colposcopic Images

Vaginal intraepithelial neoplasia (VAIN), linked to HPV infection, is a condition that is often overlooked during colposcopy, especially in the vaginal vault area, as clinicians tend to focus more on cervical lesions. This oversight can lead to missed or delayed diagnosis and treatment for patients with VAIN. Timely and accurate classification of VAIN plays a crucial role in the evaluation of vaginal lesions and the formulation of effective diagnostic approaches. The challenge is the high similarity between different classes and the low variability in the same class in colposcopic images, which can affect the accuracy, precision, and recall rates, depending on the image quality and the clinician’s experience. In this study, a dual-branch lesion-aware residual network (DLRNet), designed for small medical sample sizes, is introduced, which classifies vaginal lesions by examining the relationship between cervical and vaginal lesions. The DLRNet model includes four main components: a lesion localization module, a dual-branch classification module, an attention-guidance module, and a pretrained network module. The dual-branch classification module combines the original images with segmentation maps obtained from the lesion localization module using a pretrained ResNet network to fine-tune parameters at different levels, explore lesion-specific features from both global and local perspectives, and facilitate layered interactions. The feature guidance module focuses the local branch network on vaginal-specific features by using spatial and channel attention mechanisms. The final integration involves a shared feature extraction module and independent fully connected layers, which represent and merge the dual-branch inputs. The weighted fusion method effectively integrates multiple inputs, enhancing the discriminative and generalization capabilities of the model. Classification experiments on 1142 collected colposcopic images demonstrate that this method raises the existing classification levels, achieving the classification of VAIN into three lesion grades, thus providing a valuable tool for the early screening of vaginal diseases.

Application of Liquid Chromatography-Ion Mobility Spectrometry-Mass Spectrometry-Based Metabolomics to Investigate the Basal Chemical Profile of Olive Cultivars Differing in Verticillium dahliae Resistance.

The limited effectiveness of current strategies to control Verticillium wilt of olive (VWO) prompts the need for innovative approaches. This study explores the basal metabolome of 43 olive cultivars with varying resistance levels to Verticillium dahliae, offering alternative insights for olive crossbreeding programmes. The use of an innovative UHPLC-ESI-TimsTOF MS/MS platform enabled the annotation of more than 70 compounds across different olive organs (root, stem, and leaf) and the creation of a preliminary compilation of TIMSCCSN2 experimental data for more reliable metabolite annotation. Moreover, it allowed the documentation of numerous isomeric species in the studied olive organs by resolving hidden compounds. Multivariate statistical analyses revealed significant metabolome variability between highly resistant and susceptible cultivars, which was further investigated through supervised PLS-DA. Key markers indicative of VWO susceptibility were annotated and characteristic compositional patterns were established. Stem tissue exhibited the highest discriminative capability, while root and leaf tissues also showed significant predictive potential.

Fluorescence sensor array for highly sensitive pattern recognition of biothiols in food based on tricolor upconversion luminescence metal-organic frameworks

Fluorescence nanomaterial sensors have exhibited excellent application potential in biothiols analyses. The fluorescence sensor arrays constructed from upconversion luminescence metal-organic frameworks nanocomposites (LMOFs) can provide impressive discrimination and exquisite fingerprinting capabilities for extremely similar analytes. Herein, an upconversion fluorescence sensor array based on LMOFs featuring UiO-type metal-organic frameworks-functionalized lanthanide-doped upconversion nanoparticles was proposed, wherein Cu2+ can make the fluorescence quenching of LMOFs and preferentially bind biothiols to recover fluorescence in different degrees forming unique fingerprinting. The fluorescence sensor array displayed an excellent pattern recognition for five biothiols (glutathione, homocysteine, N-acetylcysteine, and L/D-cysteine) even at 50 µM by linear discriminant analysis, and the discernment for the enantiomers of L/D-cysteine, as well as the accurate identification (90.0% accuracy) of biothiols in food samples (tea beverage and white wine). Such fluorescence sensor array might provide a simple and efficient detection method for biothiols.

Spatial Discrimination Limit Analysis of Macrophage Phagocytosis Between Target Antigens and Non-Target Objects Using Microcapillary Manipulation Assay.

During phagocytosis, the FcGR-IgG bond is thought to be necessary to promote cell-membrane extension as the zipper mechanism. However, does this zipper mechanism provide a spatial antigen discrimination capability that allows macrophages to selectively phagocytose only antigens, especially for clusters with a mixture of antigens and non-antigens? To elucidate the ability and limitation of the zipper mechanism, we fed a coupled 2 μm IgG-coated and 4.5 μm non-coated polystyrene bead mixtures to macrophages and observed their phagocytosis. Macrophage engulfed the mixed clusters, including the 4.5 μm non-coated polystyrene part, indicating that the non-coated particles can be engulfed even without the zipper mechanism as far as coupled to the opsonized particles. In contrast, when the non-opsonized particle part was held by the microcapillary manipulation assay, macrophages pinched off the non-coated polystyrene particle part and internalized the opsonized particle part only. The results suggest that (1) an IgG-coated surface is needed to anchor phagocytosis by cell-membrane protrusion; however, (2) once the antibody-dependent cell phagocytosis is started, phagocytosis can proceed with the uncoated objects as the followers of the internalizing opsonized particles even without the support of the zipper mechanism. They may also indicate the concern of misleading the immune system to target unexpected objects because of their aggregation with target pathogens and the possibility of new medical applications to capture the non-opsonized target objects by the aggregation with small antigens to activate an immune response.

Radiomics-based predictive model for preoperative risk classification of gastrointestinal stromal tumors using multiparametric magnetic resonance imaging: aretrospective study.

The aim of this study was to develop and assess aradiomics model utilizing multiparametric magnetic resonance imaging (MRI) for the prediction of preoperative risk assessment in gastrointestinal stromal tumors (GISTs). An analysis was performed retrospectively on agroup of 121 patients who received ahistological diagnosis of GIST. They were then divided into two sets, with85 in the training set and 36 in the validation set through random partitioning. Radiomics features from five MRI sequences, totaling 600 per patient, were extracted and subjected to feature selection utilizing arandom forest algorithm. The discriminatory efficacy of the models was evaluated through receiver operating characteristic (ROC) and precision-recall (P-R) curve analyses. Model calibration was assessed via calibration curves. Subgroup analysis was performed on GISTs with apathological maximum diameter equal to or less than 5 cm. Furtherly, Kaplan-Meier (K-M) curves and log-rank tests were used to compare the differences in survival status among different groups. Cox regression analysis was employed to identify independent prognostic factors and to construct aprognostic prediction model. The clinical model (ModelC) displayed limited predictive efficacy in the context of GIST. Conversely, aradiomics model (ModelR) incorporating five parameters exhibited robust discriminative capabilities across both the training and validation sets, yielding area under the ROC curve (AUC) values of 0.893 (95% confidence interval [CI]: 0.807-0.949) and 0.855 (95% CI: 0.732-0.978), respectively. The F1max scores derived from the P‑R curves were 0.741 and 0.842 for the training and validation sets, respectively. Noteworthy was the exclusion of the two-dimensional tumor diameter and tumor location when constructing ahybrid model (ModelCR) that amalgamated radiomics and clinical features. ModelR demonstrated asubstantially enhanced discriminative capacity in the training set compared with ModelC (p < 0.005). The net reclassification improvement (NRI) corroborated the superior performance of ModelR over ModelC, thereby enhancing diagnostic accuracy and clinical applicability. Patients in the high-risk group had significantly worse recurrence-free survival (RFS, p < 0.001) and overall survival (OS, p = 0.004), and the radiomics signature is an independent risk factor for RFS. The extended model incorporating the radiomics signature outperformed the baseline model in terms of risk assessment accuracy (p < 0.001). Our investigation underscores the value of integrating radiomics analysis in conjunction with machine learning algorithms for prognostic risk stratification in GIST, presenting promising implications for informing clinical decision-making processes as well as optimizing management strategies.

Construction and validation of a nomogram prediction model for the risk of new-onset atrial fibrillation following percutaneous coronary intervention in acute myocardial infarction patients

ObjectiveThe objective of this study was to investigate risk factors for new-onset atrial fibrillation (NOAF) post-percutaneous coronary intervention (PCI) in patients with acute myocardial infarction (AMI), aiming to develop a predictive nomogram for NOAF risk.MethodsA retrospective cohort study involving 397 AMI patients who underwent PCI at a tertiary hospital in Anhui, China, from January 2021 to July 2022 was performed. Patients were divided into NOAF (n = 63) and non-NOAF (n = 334) groups based on post-PCI outcomes. Clinical data were extracted from the hospital information system (HIS) and analyzed using univariate and multivariate logistic regression to identify independent risk factors. A nomogram was generated utilizing R software (version 3.6.1), with its performance evaluated through receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis (DCA), and Bootstrap resampling.ResultsIndependent risk factors for NOAF included age, left atrial diameter (LAD), Gensini score, N-terminal pro-B-type natriuretic peptide (NT-proBNP), alanine transaminase (ALT), low-density lipoprotein cholesterol (LDL-C), left ventricular end-systolic diameter (LVESD), and ventricular rate (P < 0.05). The nomogram’s ROC curve demonstrated an area under the curve (AUC) of 0.925 (95% CI: 0.887–0.963), supported by a Bootstrap-verified AUC of 0.924 (95% CI: 0.883–0.954), reflecting strong discriminative capability. The calibration curve indicated a mean absolute error (MAE) of 0.031 and 0.017 prior to and following Bootstrap verification, respectively, signifying robust calibration. The DCA curve illustrated that the nomogram offered optimal clinical net benefit for patients with a threshold probability of NOAF ranging from 0.01 to 0.99.ConclusionThe nomogram developed from independent risk factors for NOAF exhibits significant predictive accuracy and clinical relevance for evaluating the risk of NOAF in AMI patients following PCI, thereby enabling the identification of high-risk individuals for targeted interventions.

The Methodology of Adaptive Levels of Interval for Laser Speckle Imaging.

A methodology is proposed for use in the laser speckle imaging field. This methodology modified the graphical and numerical speckle pattern imaging methods to improve their extraction and discrimination capabilities when processing the embedded temporal activity in the images of laser speckle patterns. This is through enabling these methods to adapt the levels of speckle images' interval during processing to speed up the process and overcome the lack of discrimination when they deal with a complex scattering medium having regions of various scales of activity. The impact of using the new methodology on the imaging methods' performance was evaluated using graphical and numerical evaluation tests, in addition, an exceptional laser speckle imaging system was designed and implemented to undertake a series of experimental validation tests on this methodology. The evaluation and experimental validation tests show the effectiveness of this methodology on the extraction and discrimination capabilities for the standard imaging speckle pattern methods and prove its ability to provide high performance with the real images of speckle patterns. The results also show an improvement in the processing speed for both graphical and numerical methods when the adaptive levels methodology is applied to them, which reaches 78% for the graphical and 87% for the numerical speckle processing methods.

Clinical efficacy of DSA-based features in predicting outcomes of acupuncture intervention on upper limb dysfunction following ischemic stroke

Background and objectivesThis study aimed to employ machine learning techniques to predict the clinical efficacy of acupuncture as an intervention for patients with upper limb motor dysfunction following ischemic stroke, as well as to assess its potential utility in clinical practice.MethodsMedical records and digital subtraction angiography (DSA) imaging data were collected from 735 ischemic stroke patients with upper limb motor dysfunction who were treated with standardized acupuncture at two hospitals. Following the initial screening, 314 patient datasets that met the inclusion criteria were selected. We applied three deep-learning algorithms (YOLOX, FasterRCNN, and TOOD) to develop the object detection model. Object detection results pertaining to the cerebral vessels were integrated into a clinical efficacy prediction model (random forest). This model aimed to classify patient responses to acupuncture treatment. Finally, the accuracies and discriminative capabilities of the prediction models were assessed.ResultsThe object detection model achieved an optimal recognition rate, The mean average precisions of YOLOX, TOOD, and FasterRCNN were 0.61, 0.7, and 0.68, respectively. The prediction accuracy of the clinical efficacy model reached 93.6%, with all three-treatment response classification area under the curves (AUCs) exceeding 0.95. Feature extraction using the prediction model highlighted the significant influence of various cerebral vascular stenosis sites within the internal carotid artery (ICA) on prediction outcomes. Specifically, the initial and C1 segments of the ICA had the highest predictive weights among all stenosis sites. Additionally, stenosis of the middle cerebral, anterior cerebral, and posterior cerebral arteries exerted a notable influence on the predictions. In contrast, the stenosis sites within the vertebral artery exhibited minimal impact on the model's predictive abilities.ConclusionsResults underscore the substantial predictive influence of each cerebral vascular stenosis site within the ICA, with the initial and C1 segments being pivotal predictors.

What the visual system can learn from the non-dominant hand: The effect of graphomotor engagement on visual discrimination.

Previous studies have demonstrated that engaging in graphomotor activity for creating graphemes can enhance their subsequent visual discrimination. This suggests a positive influence of the motor system on visual learning. However, existing studies have emphasized the dominant hand, which is superiorly dexterous in fine-motor movements. This near-exclusive focus prompts the inquiry of whether the observed perceptual facilitation is a general characteristic of the motor system, or specific to pathways controlling the skilled over-trained dominant hand. Furthermore, the mechanistic underpinning of visual facilitation from graphomotor training (i.e., the individual contribution of motor activity, temporal evolution of the visual trace, variability of visual output) remain unclear. To address these questions, we assessed visual discrimination capabilities of healthy right-handed participants (N = 60) before and after graphomotor or visual training. Contrary to our initial expectation, graphomotor engagement with the non-dominant hand did not yield additional benefits to visual learning beyond those attainable through visual training alone. Moreover, graphomotor training with the non-dominant hand resulted in visual discrimination improvements comparable to those of dominant hand training, despite the inherent differences between hands in motor performance and in the amount of improvement in shape tracing throughout training. We conclude that the motor components of graphomotor activity may not be critical for visual learning of shapes through tracing activity. Instead, our results are in agreement with the symbolic theoretical account, suggesting that basic shape features required for discrimination can be acquired through visual inspection alone, providing a perspective on the improvements observed in prior studies.

Learning super-resolution and pyramidal convolution residual network for vehicle re-identification

Vehicle re-identification (Vehicle Re-ID) aims at retrieving and tracking the specified target vehicle with multiple other cameras, which can provide help in checking violations and catching fugitives, but there are still the following problems that need to be solved urgently. First, the existing collected Vehicle Re-ID data often have low resolution and blur in local regions, so that the Vehicle Re-ID algorithm cannot accurately extract subtle feature representations. In addition, small features are easy to cause the disappearance of features under the operation of a large convolution kernel, which makes the model unable to capture and learn subtle features, resulting in inaccurate judgment of vehicles. In this study, we propose a Vehicle Re-ID method based on super resolution and pyramidal convolution residual network. Firstly, a super-resolution image generation network leveraging generative adversarial networks (GANs) is proposed. This network employs both content loss and adversarial loss as optimization criteria, ensuring an efficient transformation from a low-resolution image into a super-resolution counterpart, while meticulously preserving intricate high-frequency details. Then, multi levels of pyramidal convolution operations are designed to generate multi-scale features, which can capture information on different scales. Moreover, the concept of residual learning is applied between the multi levels of pyramidal convolution operations to expedite model optimization and enhance recognition capabilities. Ultimately, the double pyramidal convolutions are meticulously employed on both the original image and the super-resolution image, yielding low-noise feature representations and intricate semantic information respectively. By seamlessly fusing these two diverse sources of information, the resultant combined features exhibit heightened discrimination capabilities and significantly bolster the robustness of image features. In order to verify the effectiveness of the proposed method, extensive experiments are carried out on VeRi-776 and VehicleID datasets. The experimental results show that the method proposed in this paper effectively captures the detail information of vehicle images, accurately distinguishes the subtle differences between different vehicles of the same type, and is superior to state-of-the-art methods.

PPEIM: A preference path-based early-stage influence accumulation model for influential nodes identification in locally dense multi-core networks

The identification of influential nodes, a critical problem in the field of complex networks, has been extensively studied. However, previous research has primarily focused on maximizing terminal influence across all network structures indiscriminately, making it challenging to accurately identify influential nodes in specific structures. Moreover, overlooking the influence of the temporal dynamics of propagation significantly diminishes the benefits of identifying influential nodes. Therefore, we propose an influential nodes identification model, Preference Path-based Early-stage Influence Accumulation Model (PPEIM), tailored for typical locally dense multi-core networks. The key idea of PPEIM is to identify more influential nodes in early-stage propagation by aggregating dynamic influence propagation volumes superimposed on multiple paths. Specifically, early-stage influence performance is enhanced by sampling paths, mitigating the risk of dense influential nodes resulting from redundant relationships. Moreover, the K-shell, degree, influence distance and link direction are integrated to define connection strength between nodes to guide path selection. And the concept of influence propagation volume is introduced to accurately simulate the influence residuals and losses during the propagation process. To validate the effectiveness and superiority of PPEIM in locally dense multi-core networks, five sets of simulation experiments are conducted on seven real-world datasets. Experimental results demonstrate that PPEIM outperforms six state-of-the-art methods in overall propagation capability, early-stage influence capability, disintegration capability, node dispersion, and discrimination capability.

Development of a Frailty Prediction Model Among Older Adults in China: A Cross-Sectional Analysis Using the Chinese Longitudinal Healthy Longevity Survey.

To identify the risk factors associated with frailty among older adults in China and develop a predictive model for assessing their frailty risk. Secondary cross-sectional analysis. The 2018 Chinese Longitudinal Healthy Longevity Survey (CLHLS) provided data for this study. A total of 9006 participants were included in the analysis. Their general demographic, socioeconomic status and health behaviour risk factors were collected in the CLHLS. Frailty was assessed using the Frailty Index. A visual nomogram model was constructed based on independent predictors identified using multivariate analysis. The nomogram's discrimination and calibration capabilities were evaluated using the C-statistics and calibration curves. A 1000-times resampling enhanced bootstrap method was performed for internal validation of the nomogram. The results showed that living in rural settings, having a primary education level, having a spouse, having basic living security, smoking, drinking, exercising and social activities were protective factors against frailty. Increasing age, being underweight or obese, adverse self-assessed economic status and poor sleep quality were risk factors of frailty. The AUC values of the internal validation set were 0.830. The calibration curve was close to ideal. The Brier score was 0.122. The above results showed that the nomogram model had a good predictive performance. A simple and fast frailty risk prediction model was developed in this study to help healthcare professionals screen older adults at high risk of frailty in China. The frailty risk prediction model will assist healthcare professionals in risk management and decision-making and provide targeted frailty prevention interventions. Screening high-risk older adults and early intervention can reduce the risk of adverse outcomes and save medical expenses for older adults and society, thereby realising cost-effective planning of health resources and healthy ageing. No patient or public contribution. This study was a cross-sectional, secondary analysis of the CLHLS data.