Search Results (5,291)

With the application of tracked mobile robots in detection and rescue, how to improve their stability and trafficability has become the research focus. In order to improve the driving ability and trafficability of tracked mobile robots in rugged terrain, this paper proposes a new type of tracked mobile robot using passive suspension. By adding a connecting rod differential mechanism between the left and right track mechanisms, the contact stability between the track and terrain is enhanced. The kinematics model and attitude relationship of the suspension are analyzed and established, and the rationality of the passive suspension scheme is verified by dynamic simulation. The simulation results show that the tracked robot with passive suspension shows good obstacle surmounting performance, but there will be a heading deflection problem. Therefore, a track drive speed of the driving state compensation control is proposed based on the driving scene, which can effectively solve the problem of slip and heading deflection. Through the field test of the robot prototype, the effectiveness of the suspension scheme and control system is verified, which provides a useful reference for the scheme design and performance improvement of the tracked mobile robot in complex field scenes. Full article

Background/Objectives: This study aimed to examine the changes in brain metabolites and water molecule diffusion using chemical exchange saturation transfer (CEST) imaging and diffusion-weighted imaging (DWI) after 15 Gy of X-ray irradiation in a rat model of glioma. Methods: The glioma-derived cell line, C6, was implanted into the striatum of the right brain of 7-week-old male Wistar rats. CEST imaging and DWI were performed on days 8, 10, and 17 after implantation using a 7T-magnetic resonance imaging. X-ray irradiation (15 Gy) was performed on day 9. Magnetization transfer ratio (MTR) and apparent diffusion coefficient (ADC) values were calculated for CEST and DWI, respectively. Results: On day 17, the MTR values at 1.2 ppm, 1.5 ppm, 1.8 ppm, 2.1 ppm, and 2.4 ppm in the irradiated group decreased significantly compared with those of the control group. The standard deviation for the ADC values on a pixel-by-pixel basis increased from day 8 to day 17 (0.6 ± 0.06 → 0.8 ± 0.17 (×10⁻³ mm²/s)) in the control group, whereas it remained nearly unchanged (0.6 ± 0.06 → 0.8 ± 0.11 (×10⁻³ mm²/s)) in the irradiated group. Conclusions: This study revealed the effects of 15 Gy X-ray irradiation in a rat model of glioma using CEST imaging and DWI. Full article

Background/Objectives: Penetrating intraocular foreign bodies (IOFBs) are ocular emergencies, often leading to preventable vision loss. This case report highlights a unique presentation of a work-related penetrating IOFB that mimicked a golden hypopyon. Methods: A 35-year-old male presented to the emergency department with sudden-onset pain and vision loss in the left eye while he was cutting a tree with metallic scissors. He had a visual acuity of 20/30 in the right eye and counting fingers in the left eye. A dilated slit-lamp examination and CT scan confirmed the presence of a 6–8 mm metallic IOFB in the anterior chamber, with no involvement of the lens or the posterior segment. Surgical removal was performed. Results: The metallic IOFB was removed surgically with IOFB forceps using a single paracentesis. The patient reported resolving pain and regained baseline visual acuity of 20/20 postoperatively, which remained stable at one-month follow-up. Conclusions: This case illustrates the successful surgical management of a penetrating metallic IOFB with a unique presentation mimicking a hypopyon. Emphasis on unique presentations of IOFBs can aid in timely management, ultimately reducing the risk of complications. Full article

Introduction: This study evaluated the performance of automated dual-energy computed tomography (DECT)-based quantification of perfusion defects (PDs) in acute pulmonary embolism and examined its correlation with clinical parameters. Methods: We retrospectively analyzed data from 171 patients treated for moderate-to-severe acute pulmonary embolism, who underwent DECT imaging at two separate time points. PDs were quantified using a fully automated AI-based segmentation method that relied exclusively on iodine perfusion maps. This was compared with a semi-automatic clinician-guided segmentation, where radiologists manually adjusted thresholds to eliminate artifacts. Clinical variables including the Miller obstruction score, right-to-left ventricular diameter ratio, oxygen saturation, and patient-reported symptoms were also collected. Results: The semiautomatic method demonstrated stronger correlations with embolic burden (Miller score; r = 0.4, p < 0.001 at follow-up) and a negative correlation with oxygen saturation (r = −0.2, p = 0.04). In contrast, the fully automated AI-based quantification consistently produced lower PD values and demonstrated weaker associations with clinical parameters. Conclusions: Semiautomatic quantification of PDs currently provides superior accuracy and clinical relevance for evaluating lung PDs in acute pulmonary embolism. Future multimodal AI models that incorporate both anatomical and clinical data may further enhance diagnostic precision. Full article

Background: Despite the survival benefit of ICDs in patients with HFrEF, most recipients do not receive appropriate therapy during follow-up. Existing risk models based on echocardiographic and clinical parameters show limited predictive accuracy for arrhythmic events. This study aimed to assess whether ECG-derived markers outperform conventional measures in predicting appropriate ICD shocks. Methods: This retrospective observational study included 375 patients with HFrEF who underwent ICD implantation for primary prevention at least six months before study enrollment. Twelve-lead surface ECGs were analyzed for a QTc interval, Tp-e/QT ratio, frontal QRS-T angle, and maximum deflection index (MDI). Clinical, echocardiographic, and arrhythmic event data obtained from device interrogations were evaluated. Receiver operating characteristic (ROC) curve analysis and multivariate logistic regression were performed to identify independent predictors of appropriate ICD shocks. Results: Patients who experienced appropriate ICD shocks had significantly higher rates of a complete bundle branch block, digoxin use, QRS duration, QTc, Tp-e/QT ratio, frontal QRS-T angle, MDI, and right-ventricular pacing ratio. Conversely, beta-blocker use was significantly lower in this group. In multivariate analysis, independent predictors of appropriate shocks included the patient’s digoxin use (OR = 2.931, p = 0.003), beta-blocker use (OR = 0.275, p = 0.002), frontal QRS-T angle (OR = 1.009, p < 0.001), QTc interval (OR = 1.020, p < 0.001), and Tp-e/QT ratio (OR = 4.882, p = 0.050). The frontal QRS-T angle had a cutoff value of 105.5° for predicting appropriate ICD shocks (sensitivity: 73.6%, specificity: 85.2%, AUC = 0.758, p < 0.001). Conclusions: Electrocardiographic markers, particularly the frontal QRS-T angle, QTc interval, and Tp-e/QT ratio, demonstrated superior predictive power for appropriate ICD shocks compared to conventional echocardiographic and clinical measures. These easily obtainable, non-invasive ECG parameters may improve current risk stratification models and support more individualized ICD implantation strategies. Full article

Background and Objectives: Risk stratification in acute heart failure (AHF) remains challenging, particularly in settings where biomarker availability is limited. Echocardiography offers valuable hemodynamic insights, but no single parameter fully captures the complexity of biventricular dysfunction and pressure overload. This study aimed to evaluate a novel echocardiographic index (ViRTUE Index–VTI-RVRA-TAPSE Unified Evaluation) integrating a peak systolic gradient between the right ventricle and right atrium (RV-RA gradient), tricuspid annular plane systolic excursion (TAPSE), the velocity–time integral in the left ventricular outflow tract (VTI LVOT), NT-proBNP (N-terminal pro–B-type Natriuretic Peptide) levels, and in-hospital mortality among patients with AHF. Materials and Methods: We retrospectively analyzed 123 patients admitted with AHF. Echocardiographic evaluation at admission included TAPSE, VTI LVOT, and the RV-RA gradient. An index was calculated as ${\displaystyle \frac{RV-RA gradient }{TAPSE x VTI LVOT}}$. NT-proBNP levels and in-hospital outcomes were recorded. Statistical analysis included correlation, logistic regression, and ROC curve evaluation. Results: The proposed index showed a significant positive correlation with NT-proBNP values (r = 0.543, p < 0.0001) and good discriminative ability for elevated NT-proBNP (AUC = 0.79). It also correlated with in-hospital mortality (r = 0.193, p = 0.032) and showed moderate prognostic performance (AUC = 0.68). Higher index values were associated with greater mortality risk. Conclusions: This novel index, based on standard echocardiographic measurements, reflects both systolic dysfunction and pressure overload in AHF. Its correlation with NT-proBNP and in-hospital mortality highlights its potential as a practical, accessible bedside tool for early risk stratification, particularly when biomarker testing is unavailable or delayed. Full article

The accurate perception of visual stimuli in human–machine systems is crucial for improving system safety, usability, and task performance. The widespread adoption of digital technology has significantly increased the importance of visual interfaces and information. Therefore, it is essential to design visual interfaces and information with user characteristics in mind to ensure accurate perception of visual information. This study employed the Cognitive Perceptual Assessment for Driving (CPAD) to evaluate and compare gender differences in the ability to perceive visual signals within complex visual stimuli. The experimental setup included a computer with CPAD installed, along with a touch monitor, mouse, joystick, and keyboard. The participants included 11 male and 20 female students, with an average age of 22 for males and 21 for females. Prior to the experiment, participants were instructed to determine whether a signal stimulus was present: if a square, presented as the signal, was included in the visual stimulus, they moved the joystick to the left; otherwise, they moved it to the right. Each participant performed a total of 40 trials. The entire experiment was recorded on video to measure overall response times. The experiment measured the number of correct detections of signal presence, response times, the number of misses (failing to detect the signal when present), and false alarms (detecting the signal when absent). The analysis of experimental data revealed no significant differences in perceptual ability or response times for visual stimuli between genders. However, males demonstrated slightly superior perceptual ability and marginally shorter response times compared to females. Analyses of sensitivity and response bias, based on signal detection theory, also indicated a slightly higher perceptual ability in males. In conclusion, although these differences were not statistically significant, males demonstrated a slightly better perception ability for visual stimuli. The findings of this study can inform the design of information, user interfaces, and visual displays in human–machine systems, particularly in light of the recent trend of increased female participation in the industrial sector. Future research will focus on diverse types of visual information to further validate these findings. Full article

Automated movement intention is crucial for brain–computer interface (BCI) applications. The automatic identification of movement intention can assist patients with movement problems in regaining their mobility. This study introduces a novel approach for the automatic identification of movement intention through finger tapping. This work has compiled a database of EEG signals derived from left finger taps, right finger taps, and a resting condition. Following the requisite pre-processing, the captured signals are input into the proposed model, which is constructed based on graph theory and deep convolutional networks. In this study, we introduce a novel architecture based on six deep convolutional graph layers, specifically designed to effectively capture and extract essential features from EEG signals. The proposed model demonstrates a remarkable performance, achieving an accuracy of 98% in a binary classification task when distinguishing between left and right finger tapping. Furthermore, in a more complex three-class classification scenario, which includes left finger tapping, right finger tapping, and an additional class, the model attains an accuracy of 92%. These results highlight the effectiveness of the architecture in decoding motor-related brain activity from EEG data. Furthermore, relative to recent studies, the suggested model exhibits significant resilience in noisy situations, making it suitable for online BCI applications. Full article

This paper introduces a robust and efficient Localized Spline-based Path-Planning (LSPP) algorithm designed to enhance autonomous vehicle navigation on highways. The LSPP approach prioritizes smooth maneuvering, obstacle avoidance, passenger comfort, and adherence to road constraints, including lane boundaries, through optimized trajectory generation using quintic spline functions and a dynamic speed profile. Leveraging real-time data from the vehicle’s sensor fusion module, the LSPP algorithm accurately interprets the positions of surrounding vehicles and obstacles, creating a safe, dynamically feasible path that is relayed to the Model Predictive Control (MPC) track-following module for precise execution. The theoretical distinction of LSPP lies in its modular integration of: (1) a finite state machine (FSM)-based decision-making layer that selects maneuver-specific goal states (e.g., keep lane, change lane left/right); (2) quintic spline optimization to generate smooth, jerk-minimized, and kinematically consistent trajectories; (3) a multi-objective cost evaluation framework that ranks competing paths according to safety, comfort, and efficiency; and (4) a closed-loop MPC controller to ensure real-time trajectory execution with robustness. Extensive simulations conducted in diverse highway scenarios and traffic conditions demonstrate LSPP’s effectiveness in delivering smooth, safe, and computationally efficient trajectories. Results show consistent improvements in lane-keeping accuracy, collision avoidance, enhanced materials wear performance, and planning responsiveness compared to traditional path-planning methods. These findings confirm LSPP’s potential as a practical and high-performance solution for autonomous highway driving. Full article

Background: Transcranial direct current stimulation (tDCS) has emerged as a promising neuromodulatory tool for language rehabilitation in chronic aphasia. However, the effects of bi-hemispheric, multisite stimulation remain largely unexplored, especially in people with chronic and treatment-resistant language impairments. The goal of this study is to look at the effects on behavior and brain activity of an individualized language training program that combines bi-hemispheric multisite anodal tDCS with personalized language training for Albert, a patient with long-standing, treatment-resistant non-fluent aphasia. Methods: Albert, a right-handed retired physician, had transcortical motor aphasia (TCMA) subsequent to a left-hemispheric ischemic stroke occurring more than six years before the operation. Even after years of traditional treatment, his expressive and receptive language deficits remained severe and persistent despite multiple rounds of traditional therapy. He had 15 sessions of bi-hemispheric multisite anodal tDCS aimed at bilateral dorsal language streams, administered simultaneously with language training customized to address his particular phonological and syntactic deficiencies. Psycholinguistic evaluations were performed at baseline, immediately following the intervention, and at 1, 2, 3, and 6 months post-intervention. Resting-state fMRI was conducted at baseline and following the intervention to evaluate alterations in functional connectivity (FC). Results: We noted statistically significant enhancements in auditory sentence comprehension and oral reading, particularly at the 1- and 3-month follow-ups. Neuroimaging showed decreased functional connectivity (FC) in the left inferior frontal and precentral regions (dorsal stream) and in maladaptive right superior temporal regions, alongside increased FC in left superior temporal areas (ventral stream). This pattern suggests that language networks may be reorganizing in a more efficient way. There was no significant improvement in phonological processing, which may indicate reduced connectivity in the left inferior frontal areas. Conclusions: This case underscores the potential of combining individualized, network-targeted language training with bi-hemispheric multisite tDCS to enhance recovery in chronic, treatment-resistant aphasia. The convergence of behavioral gains and neuroplasticity highlights the importance of precision neuromodulation approaches. However, findings are preliminary and warrant further validation through controlled studies to establish broader efficacy and sustainability of outcomes. Full article

The deformation of the array shape during the turning process of an autonomous underwater vehicle (AUV)-towed line array sonar can significantly degrade its remote sensing performance. In this paper, a method for circular arc array modeling and dynamic deformation estimation is proposed. By treating the array shape as a hyperparameter, an adaptive central angle (shape) marginal likelihood maximization (ASMLM) algorithm is derived to jointly estimate the array shape and the directions of arrival (DOAs) of sources. The high-resolution ASMLM algorithm is used to improve the DOA estimation performance, effectively suppress left–right ambiguity and significantly reduce computational complexity, making it suitable for AUV platforms with limited computational resources. Experimental results from sea trials in the South China Sea are used to validate the superior performance of the proposed method over existing methods. Full article

Background: The influence of the initial ablation modality on pulmonary vein (PV) reconnection and substrate characteristics in redo procedures for recurrent atrial fibrillation (AF) remains unclear. We assessed how different thermal strategies—ablation index (AI)-guided radiofrequency (RF) versus cryoballoon (CB) ablation—affect remapping findings during redo pulmonary vein isolation (PVI). Methods: We included patients undergoing redo ablation between 2015 and 2024 with high-density electroanatomic mapping. Initial PVI modalities were retrospectively classified as low-power, long-duration (LPLD) RF; high-power, short-duration (HPSD) RF; or second-/third-generation CB. Reconnection sites were mapped using multielectrode catheters. Redo PVI was performed using AI-guided RF. Segments showing PV reconnection were reisolated; if all PVs remained isolated and AF persisted, posterior wall isolation was performed. Results: Among 195 patients (LPLD: 63; HPSD: 30; CB: 102), complete PVI at redo was observed in 0% (LPLD), 23.3% (HPSD), and 10.1% (CB) (p < 0.01 for LPLD vs. HPSD). Reconnection patterns varied by technique; LPLD primarily affected the right carina, while HPSD and CB showed reconnections at the LSPV ridge. Organized atrial tachycardia was least frequent after CB (12.7%, p < 0.002). Conclusion: Initial ablation strategy significantly influences PV reconnection and post-PVI arrhythmia patterns, with implications for redo procedure planning. Full article

Background/Objectives: Iron deficiency (ID) is a common and prognostically relevant comorbidity in heart failure with reduced ejection fraction (HFrEF). It contributes to reduced functional status, exercise capacity, and survival. Intravenous ferric carboxymaltose (FCM) improves symptoms, but its effect on cardiac structure and function remains incompletely understood. The aim of this study was to assess the impact of intravenous FCM on echocardiographic indices of left ventricular (LV), left atrial (LA), and right ventricular (RV) morphology and function in HFrEF patients with ID and determine whether these changes correlate with improvements in exercise capacity. Methods: This sub-analysis of the RESAFE-HF registry (NCT04974021) included 86 HFrEF patients with ID (median age 71.8 years, 83% male). Transthoracic echocardiography was performed at baseline and 12 months post-FCM. Parameters assessed included LV ejection fraction (LVEF), LV global longitudinal strain (GLS), LV diastolic function grade, LAVi, LA strain, TAPSE, and RV free wall strain (FWS). Peak VO₂ was measured to assess exercise capacity. Results: LVEF improved from 29.3 ± 7.8% to 32.5 ± 10.6% (p < 0.001), LV GLS from −7.89% to −8.62%, and the LV diastolic dysfunction grade improved (p < 0.001). LAVi, peak LA strain, TAPSE, and RV FWS also showed significant improvement. Peak VO₂ increased from 11.3 ± 3.2 to 12.1 ± 4.1 mL/min/kg (p < 0.001). Improvements in LVEF, RV FWS, and LV GLS were independent predictors of VO₂ increase (p < 0.001, p < 0.001, and p = 0.01, respectively), explaining 42% of the variance. Conclusions: FCM therapy improves biventricular and atrial function, with echocardiographic gains correlating with an enhanced exercise capacity in HFrEF patients with ID. Full article

Change point models are widely used in medical and epidemiological studies to capture the threshold effects of continuous covariates on health outcomes. These threshold effects represent critical points at which the relationship between biomarkers or risk factors and disease risk shifts, often reflecting underlying biological mechanisms or clinically relevant intervention points. While most existing methods focus on right-censored data, interval censoring is common in large-scale clinical trials and follow-up studies, where the exact event times are not observed but are known to fall within time intervals. In this paper, we propose a semiparametric transformation model with an unknown change point for interval-censored data. The model allows flexible transformation functions, including the proportional hazards and proportional odds models, and it accommodates both main effects and their interactions with the threshold variable. Model parameters are estimated via the EM algorithm, with the change point identified through a profile likelihood approach using grid search. We establish the asymptotic properties of the proposed estimators and evaluate their finite-sample performance through extensive simulations, showing good accuracy and coverage properties. The method is further illustrated through an application to the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial data. Full article

Large, high-quality patient datasets are essential for applications like economic modeling and patient simulation. However, real-world data is often inaccessible or incomplete. Synthetic patient data offers an alternative, and current methods often fail to preserve clinical plausibility, real-world correlations, and logical consistency. This study presents a patient cohort generator designed to produce realistic, statistically valid synthetic datasets. The generator uses predefined probability distributions and Cholesky decomposition to reflect real-world correlations. A dependency matrix handles variable relationships in the right order. Hard limits block unrealistic values, and binary variables are set using percentiles to match expected rates. Validation used two datasets, NHANES (2021–2023) and the Framingham Heart Study, evaluating cohort diversity (general, cardiac, low-dimensional), data sparsity (five correlation scenarios), and model performance (MSE, RMSE, R², SSE, correlation plots). Results demonstrated strong alignment with real-world data in central tendency, dispersion, and correlation structures. Scenario A (empirical correlations) performed best (R² = 86.8–99.6%, lowest SSE and MAE). Scenario B (physician-estimated correlations) also performed well, especially in a low-dimensions population (R² = 80.7%). Scenario E (no correlation) performed worst. Overall, the proposed model provides a scalable, customizable solution for generating synthetic patient cohorts, supporting reliable simulations and research when real-world data is limited. While deep learning approaches have been proposed for this task, they require access to large-scale real datasets and offer limited control over statistical dependencies or clinical logic. Our approach addresses this gap. Full article