Fatigued driving is one of the leading causes of traffic accidents, and detecting fatigued driving effectively is critical to improving driving safety. Given the variety and individual variability of the driving surroundings, the drivers’ states of weariness, and the uncertainty of the key characteristic factors, in this paper, we propose a deep-learning-based study of the MAX-MIN driver fatigue detection algorithm. First, the ShuffleNet V2K16 neural network is used for driver face recognition, which eliminates the influence of poor environmental adaptability in fatigue detection; second, ShuffleNet V2K16 is combined with Dlib to obtain the coordinates of driver face feature points; and finally, the values of EAR and MAR are obtained by comparing the first 100 frames of images to EAR-MAX and MAR-MIN. Our proposed method achieves 98.8% precision, 90.2% recall, and 94.3% F-Score in the actual driving scenario application.

Multimodal fusion models are widely applied in practical tasks such as image classification and sentiment analysis. However, real-world data often contain noisy or imbalanced modality inputs, significantly affecting the performance of traditional fusion methods. To address this challenge, we propose an Uncertainty-Aware Multimodal Fusion (UAMF) framework designed to enhance robustness under noisy conditions. UAMF jointly models aleatoric uncertainty from noisy data and epistemic uncertainty from model limitations. It integrates a Dynamic Direct Computation (DDC) module for adaptive uncertainty estimation and an Uncertainty Fusion Mechanism (UFM) that fuses predictions using Bayesian updates. We evaluate the effectiveness of our method on two practical tasks: image classification (FOOD101, CUB-200) and multimodal sentiment analysis (MOSI, MOSEI). Compared with the baseline model QMF, UAMF achieves an average accuracy improvement of 2.3% across tasks (e.g., +3.1% on CUB-200 and +1.2% on MVSA-Single under Gaussian noise). In addition, it reduces inference time by 52%, from 187.2 ms to 89.5 ms, compared to the recent state-of-the-art model EAU. The results demonstrate that UAMF outperforms existing methods across both application domains, particularly under noisy or modality-impaired conditions. These findings highlight the robustness and generalizability of the proposed method in real-world multimodal learning scenarios.

Background Aspiration pneumonia (AP) is a common complication in the intensive care unit (ICU), which is associated with significantly increased morbidity and mortality and has a significant impact on patient prognosis. Antibiotics are commonly used in the clinical treatment of AP. However, the prognostic impact of antibiotics on patients with AP has not been adequately characterized. The purpose of this study is to illustrate the relationship between the use of antibiotics and in-hospital mortality of AP patients, as well as to analyze the effects of different antibiotic treatment regimens on the prognosis of the patients, and to further understand the distribution of pathogens and drug resistance in AP patients, so as to provide guidance information for the rational use of medication for patients in the clinic. Methods Clinical data of AP patients were extracted from the MIMIC-IV database. Statistical methods included multivariate logistic regression, propensity score matching (PSM), and inverse probability weighting (IPW) based on propensity scores to ensure the robustness of the findings. In addition, the characteristics of the medications used by patients with AP were described using statistical graphs and tables. Results A total of 4132 patients with AP were included. In-hospital mortality was significantly lower in the group using antibiotics compared to the group not using antibiotics (odds ratio [OR] = 0.44, 95% confidence interval [CI] 0.27- 0.71, P = 0.001). Furthermore, in the group using mechanical ventilation (MV), antibiotics use significantly reduced in-hospital mortality (OR = 0.30, 95% CI 0.15-0.57, P < 0.001). Vancomycin and cephalosporins are the most commonly used antibiotics to treat AP. Specifically, vancomycin in combination with piperacillin-tazobactam was used most frequently with 396 cases. The highest survival rate (97.6\%) was observed in patients treated with levofloxacin combined with metronidazole. Additionally, vancomycin combined with piperacillin-tazobactam had many inflammation related features that differed significantly from those in patients who did not receive medication. Conclusions Antibiotics use is closely associated with lower in-hospital mortality in ICU patients with AP. Moreover, understanding antibiotics use, the composition of pathogenic bacteria, and the rates of drug resistance in patients with AP can aid in disease prevention and prompt infection control.

Generalized zero-shot learning (GZSL) aims to classify classes that do not appear during training. Recent state-of-the-art approaches rely on generative models, which use correlating semantic embeddings to synthesize unseen classes visual features; however, these approaches ignore the semantic and visual relevance, and visual features synthesized by generative models do not represent their semantics well. Although existing GZSL methods based on generative model disentanglement consider consistency between visual and semantic models, these methods consider semantic consistency only in the training phase and ignore semantic consistency in the feature synthesis and classification phases. The absence of such constraints may lead to an unrepresentative synthesized visual model with respect to semantics, and the visual and semantic features are not modally well aligned, thus causing the bias between visual and semantic features. Therefore, an approach for GZSL is proposed to enhance semantic-consistent features and discriminative features transformation (ESTD-GZSL). The proposed method can enhance semantic-consistent features at all stages of GZSL. A semantic decoder module is first added to the VAE to map synthetic and real features to the corresponding semantic embeddings. This regularization method allows synthesizing unseen classes for a more representative visual representation, and synthetic features can better represent their semantics. Then, the semantic-consistent features decomposed by the disentanglement module and the features output by the semantic decoder are transformed into enhanced semantic-consistent discriminative features and used in classification to reduce the ambiguity between categories. The experimental results show that our proposed method achieves more competitive results on four benchmark datasets (AWA2, CUB, FLO, and APY) of GZSL.