Effects of different remifentanil doses on the stress reaction and BIS value of video laryngoscope-guided tracheal intubation

Purpose: To explore the affinity of different remifentanil doses for  intravenous anesthesia in video laryngoscope-guided tracheal intubation.Methods: Eighty patients who required anesthesia for elective non-ophthalmic surgery were included. They were divided into four groups (A, B, C and D) and received a different dose of either 1, 1.5, or 2 μg/kg remifentanil or a dose of 2 μg/kg fentanyl, respectively. An anesthetic state was achieved and maintained by administration of 3 - 5 mg/kg propofolum and 0.1 - 0.3 mg/kg remifentanil. The mean value of the various indices, including arterial pressure (MAP), bispectral index and heart rate (HR) wererecorded prior to anesthesia induction (T0), prior to intubation (T1),  instantly before intubation (T2), and at 1 (T3), 3 (T4) and 5 (T5) after the intubation. Cortisol concentration was measured at T0, T1 and T5.Results: Remifentanil (1 μg/kg) induced a moderate increase in HR and MAP at T3 compared with fentanyl. HR and MAP in the lower dose group were significantly higher than those in groups B and C at T3. Compared to T1, the concentrations of cortisol decreased after anesthesia and then significantly increased during tracheal intubation. Cortisol concentration in group B was the lowest at T5.Conclusion: The most effective concentrations of remifentanil are 1 and 1.5 μg/kg for anesthesia induction and tracheal intubation, respectively.Keywords: Remifentanil, Stress reaction, Bispectral index, Video laryngoscope, Tracheal intubatio

Multidimensional Uncertainty-Aware Evidential Neural Networks

Traditional deep neural networks (NNs) have significantly contributed to the state-of-the-art performance in the task of classification under various application domains. However, NNs have not considered inherent uncertainty in data associated with the class probabilities where misclassification under uncertainty may easily introduce high risk in decision making in real-world contexts (e.g., misclassification of objects in roads leads to serious accidents). Unlike Bayesian NN that indirectly infer uncertainty through weight uncertainties, evidential NNs (ENNs) have been recently proposed to explicitly model the uncertainty of class probabilities and use them for classification tasks. An ENN offers the formulation of the predictions of NNs as subjective opinions and learns the function by collecting an amount of evidence that can form the subjective opinions by a deterministic NN from data. However, the ENN is trained as a black box without explicitly considering inherent uncertainty in data with their different root causes, such as vacuity (i.e., uncertainty due to a lack of evidence) or dissonance (i.e., uncertainty due to conflicting evidence). By considering the multidimensional uncertainty, we proposed a novel uncertainty-aware evidential NN called WGAN-ENN (WENN) for solving an out-of-distribution (OOD) detection problem. We took a hybrid approach that combines Wasserstein Generative Adversarial Network (WGAN) with ENNs to jointly train a model with prior knowledge of a certain class, which has high vacuity for OOD samples. Via extensive empirical experiments based on both synthetic and real-world datasets, we demonstrated that the estimation of uncertainty by WENN can significantly help distinguish OOD samples from boundary samples. WENN outperformed in OOD detection when compared with other competitive counterparts.Comment: AAAI 202

Recent advances in theory and technology of oil and gas geophysics

Oil and gas are important energy resources and industry materials. They are stored in pores and fractures of subsurface rocks over thousands of meters in depth, making the finding and distinguishing them to be a significant challenge. The geophysical methods, especially the seismic and well-logging methods, are the effective ways to identify the oil and gas reservoirs and are widely used in industry. Due to the complexity of near surface and subsurface structures of new exploration targets, the geophysical methods based on advanced computation methods and physical principles are continuously proposed to cope with the emerging challenges. Thus, some new advances in theory and technology of oil and gas geophysics are summarized in this editorial material, especially focusing on the geophysical data processing, numerical simulation technology, rock physics modeling, and reservoir characterization.Document Type: EditorialCited as: Wang, Y., Liu, Y., Zou, Z., Bao, Q., Zhang, F., Zong, Z. Recent advances in theory and technology of oil and gas geophysics. Advances in Geo-Energy Research, 2023, 9(1): 1-4. https://doi.org/10.46690/ager.2023.07.0

A novel prestack sparse azimuthal AVO inversion

In this paper we demonstrate a new algorithm for sparse prestack azimuthal AVO inversion. A novel Euclidean prior model is developed to at once respect sparseness in the layered earth and smoothness in the model of reflectivity. Recognizing that methods of artificial intelligence and Bayesian computation are finding an every increasing role in augmenting the process of interpretation and analysis of geophysical data, we derive a generalized matrix-variate model of reflectivity in terms of orthogonal basis functions, subject to sparse constraints. This supports a direct application of machine learning methods, in a way that can be mapped back onto the physical principles known to govern reflection seismology. As a demonstration we present an application of these methods to the Marcellus shale. Attributes extracted using the azimuthal inversion are clustered using an unsupervised learning algorithm. Interpretation of the clusters is performed in the context of the Ruger model of azimuthal AVO