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Independent predictors and thresholds of in vitro fertilization outcomes in patients with diminished ovarian reserve
Intermittent control and retinal optic flow when maintaining a curvilinear path
Abstract The topic of how humans navigate using vision has been studied for decades. Research has identified the emergent patterns of retinal optic flow from gaze behavior may play an essential role in human curvilinear locomotion. However, the link towards control has been poorly understood. Lately, it has been shown that human locomotor behavior is corrective, formed from intermittent decisions and responses. A simulated virtual reality experiment was conducted where fourteen participants drove through a texture-rich simplistic road environment with left and right curve bends. The goal was to investigate how human intermittent lateral control can be associated with the retinal optic flow-based cues and vehicular heading as sources of information. This work reconstructs dense retinal optic flow using a numerical estimation of optic flow with measured gaze behavior. By combining retinal optic flow with the drivable lane surface, a cross-correlational relation to intermittent steering behavior could be observed. In addition, a novel method of identifying constituent ballistic correction using particle swarm optimization was demonstrated to analyze the incremental correction-based behavior. Through time delay analysis, our results show a human response time of approximately 0.14 s for retinal optic flow-based cues and 0.44 s for heading-based cues, measured from stimulus onset to steering correction onset. These response times were further delayed by 0.17 s when the vehicle-fixed steering wheel was visibly removed. In contrast to classical continuous control strategies, our findings support and argue for the intermittency property in human neuromuscular control of muscle synergies, through the principle of satisficing behavior: to only actuate when there is a perceived need for it. This is aligned with the human sustained sensorimotor model, which uses readily available information and internal models to produce informed responses through evidence accumulation to initiate appropriate ballistic correction, even amidst another correction.
Novel host factors associated with resistance to highly pathogenic avian influenza in wild birds inferred from primary cell culture
Investigating asymmetry in fetal and maternal heart rate accelerations and decelerations
Integrating landsat NDVI data with climate and anthropogenic factors reveals drivers of vegetation dynamics in the semi-arid Basin of Western China
A cross-sectional study of oral health and disease prevalence in HIV-positive patients in Tabriz, Iran (2024)
Innovative integration of automated breast volume scan and ultrasound elastography for enhanced differentiation of benign and malignant breast lesions
Empirical analysis of influencer attributes and social satisfaction effects on purchase intentions in chinese social media
Abstract Underpinned by attribution theory and source credibility theory, this study investigates how influencer characteristics and customer’s prior product knowledge affect purchase decisions in the context of social media marketing. A conceptual model incorporating nine potential antecedents was developed based on identified research gaps. Confirmatory factor analysis (CFA) and structural equation modeling (SEM) were conducted using data from an online survey of 363 respondents who follow entertainment-type influencers. Results reveal that social satisfaction mediates the relationship between influencer characteristics and purchase intention, while customers’ product knowledge moderates this mediated relationship. Specifically, visual aesthetics and denotative inspiration significantly influence social satisfaction, whereas influencer level and connotative inspiration show no significant effects. The study contributes to the theoretical understanding of influencer marketing by integrating attribution theory in a digital context, particularly within the Chinese market. These findings offer practical insights for businesses and marketers in optimizing influencer selection and content strategies, with particular relevance for the rapidly evolving Chinese social media landscape.
Reassessment of aviation risk safety barriers using stochastic and lexical uncertainty
Construction and performance verification of an automated assembly system for aero engine principal shaft enabled by multi sensor fusion
Nomogram for predicting secondary surgery in patients with concomitant exotropia
Author Correction: Microbiota-derived 3-IAA influences chemotherapy efficacy in pancreatic cancer
Evaluation of machine learning and deep learning algorithms for fire prediction in Southeast Asia
Abstract Vegetation fires are most common in Southeast Asian (SEA) countries, causing biodiversity loss, habitat destruction, and air pollution. Accurately predicting fire occurrences in SEA remains challenging due to its complex spatiotemporal dynamics. Improved fire predictions enable timely interventions, helping to control and mitigate fires. In this study, we utilize Visible Infrared Imaging Radiometer Suite (VIIRS) satellite-derived fire data alongside six machine learning (ML) and deep learning (DL) models, Simple Persistence, Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), CNN-Long Short-Term Memory (CNN-LSTM), and Convolutional Long Short-Term Memory (ConvLSTM) to determine the most effective fire prediction model. We evaluated model performance using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and R2 (coefficient of determination). Our results indicate that the CNN performs best in regions with strong spatial dependencies, such as Brunei, Indonesia, Malaysia, the Philippines, Timor-Leste, and Thailand. Conversely, the ConvLSTM excels in countries with complex spatiotemporal dynamics, like Laos, Myanmar, and Vietnam. The CNN-LSTM hybrid model also performed well in Cambodia, suggesting a need for a balanced approach in areas requiring both spatial and temporal feature extraction. Furthermore, simpler models, such as Simple Persistence and MLP, showed limitations in capturing dynamic patterns and temporal dependencies. Our findings highlight the importance of evaluating various ML and DL models before integrating them into any decision support systems (DSS) for fire management studies. By tailoring models to specific regional fire data, prediction accuracy and responsiveness can be enhanced, ultimately improving fire risk management in Southeast Asia and beyond.
The barriers perceived by Chilean athletes with disability at different social ecological levels
Development of a 3D ex vivo model of brain-leukemia interaction to study the role of activin A in the central nervous system microenvironment
The relationship between hysterectomy, menopause, and tubal ligation, with coronary heart diseases in North of Iran: a population-based case–control study
Abstract Previously, menopause, hysterectomy, and tubal ligation (TL) have been evaluated as coronary heart disease (CHD) risk factors. However, the results regarding the significance of these associations were conflicting. Thus, the present study aimed to assess whether hysterectomy, menopause, and TL increase the odds of CHD. This case-control study included data from the enrollment phase of the Tabari cohort study (TCS) consisting of 564 cases of CHD and 564 healthy controls. Logistic regression was used to calculate the odds ratio (OR) of CHD in relation to hysterectomy, menopause, and TL status after adjustment for confounders. The univariate logistic regression analysis showed a significantly higher odds of CHD among post-menopausal participants (OR: 5.09, 95%CI 3.92–6.61), participants with TL (OR: 1.81, 95%CI 1.41–2.32), and women with hysterectomy (OR: 2.43, 95%CI 1.69–3.50). However, none of the associations were statistically significant (Hysterectomy: OR: 1.21, 95%CI 0.8–1.85; Menopause: OR: 1.43, 95%CI 0.88–2.31; TL: OR: 1.01, 95%CI 0.74–1.37) in the fully adjusted model (after adjustment for age, diabetes, hypertension, residential area, waist-to-hip ratio, pregnancy number, socio-economic state, occupation, education, and physical activity). Although some models showed significance, none of the reproductive factors showed a significant association with CHD after full adjustment.