Klavsenhaahr2360

Our results demonstrate a statistically significant improvement (p less then 0.001) in the prediction of in-hospital mortality among patients susceptible to sepsis when implementing our proposed strategies. Area under the receiver operator curve (AUC) and accuracy values of 0.8928 and 0.8904 are achieved by optimizing the point values of the SOFA score.High linearity/sensitivity and a wide dynamic sensing range are the most desirable features for pressure sensors to accurately detect and respond to external pressure stimuli. Even though a number of recent studies have demonstrated a low-cost pressure sensing device for a smart insole system by using scalable and deformable conductive materials, they still lack stretchability and desirable properties such as high sensitivity, hysteresis, linearity, and fast response time to obtain accurate and reliable data. To resolve this issue, a flexible and stretchable piezoresistive pressure sensor with high linear response over a wide pressure range is developed and integrated in a wearable insole system. The sensor uses multi-walled carbon nanotubes and polydimethylsiloxane (MWCNT/PDMS) composites with gradient density double-stacked configuration as well as randomly distributed surface microstructure (RDSM). The randomly distributed surface of the MWCNT/PDMS composite is easily and non-artificially generated by the evaporation of residual IPA solvent during a composite curing process. Due to two functional features consisting of the double-stacked composite configuration with different gradient MWCNT density and RDSM, the pressure sensor shows high linear sensitivity (~82.5 kPa) and a pressure range of 0-1 MPa, providing extensive potential applications in monitoring human motions. Moreover, for a practical wearable application detecting the users real-time motions, a custom-designed output signal acquisition system has been developed and integrated with the insole pressure sensor. As a result, the insole sensor can successfully detect walking, running, and jumping movements and can be used in daily life to monitor gait patterns by virtue of its long-term stability.Differentiable neural computers (DNCs) extend artificial neural networks with an explicit memory without interference, thus enabling the model to perform classic computation tasks, such as graph traversal. However, such models are difficult to train, requiring long training times and large datasets. JZL184 In this work, we achieve some of the computational capabilities of DNCs with a model that can be trained very efficiently, namely, an echo state network with an explicit memory without interference. This extension enables echo state networks to recognize all regular languages, including those that contractive echo state networks provably cannot recognize. Furthermore, we demonstrate experimentally that our model performs comparably to its fully trained deep version on several typical benchmark tasks for DNCs.Sarcasm is a sophisticated construct to express contempt or ridicule. It is well-studied in multiple disciplines (e.g., neuroanatomy and neuropsychology) but is still in its infancy in computational science (e.g., Twitter sarcasm detection). In contrast to previous methods that are usually geared toward a single discipline, we focus on the multidisciplinary cross-innovation, i.e., improving embryonic sarcasm detection in computational science by leveraging the advanced knowledge of sarcasm cognition in neuroanatomy and neuropsychology. In this work, we are oriented toward sarcasm detection in social media and correspondingly propose a multimodal, multi-interactive, and multihierarchical neural network (M₃N₂). We select Twitter, image, text in image, and image caption as the input of M₃N₂ since the brain's perception of sarcasm requires multiple modalities. To reasonably address the multimodalities, we introduce singlewise, pairwise, triplewise, and tetradwise modality interactions incorporating gate mechanism and guide attention (GA) to simulate the interactions and collaborations of involved regions in the brain while perceiving multiple modes. Specifically, we exploit a multihop process for each modality interaction to extract modal information multiple times using GA for obtaining multiperspective information. Also, we adopt a two-hierarchical structure leveraging self-attention accompanied by attention pooling to integrate multimodal semantic information from different levels mimicking the brain's first- and second-order comprehensions of sarcasm. Experimental results show that M₃N₂ achieves competitive performance in sarcasm detection and displays powerful generalization ability in multimodal sentiment analysis and emotion recognition.In this article, we address the disturbance/uncertainty estimation of maritime autonomous surface ships (MASSs) with unknown internal dynamics, unknown external disturbances, and unknown input gains. In contrast to existing disturbance observers where some prior knowledge on kinetic model parameters such as the control input gains is available in advance, reduced- and full-order data-driven adaptive disturbance observers (DADOs) are proposed for estimating unknown input gains, as well as total disturbance composed of unknown internal dynamics and external disturbances. An advantage of the proposed DADOs is that the total disturbance and input gains can be simultaneously estimated with guaranteed convergence via data-driven adaption. We apply the proposed full-order DADO for the trajectory tracking control of an MASS without kinetic modeling and present a model-free trajectory tracking control law for the ship based on the DADO and a backstepping technique. We report the simulation results to substantiate the efficacy of the proposed DADO approach to model-free trajectory tracking control of an autonomous surface ship without knowing its dynamics.This article focuses on the problem of adaptive bipartite output tracking for a class of heterogeneous linear multiagent systems (MASs) by asynchronous edge-event-triggered communications under jointly connected signed topologies. By designing the observers to estimate the states of followers and the dynamic compensators to estimate the states of zero input and nonzero input leader, respectively, the fully distributed edge-event-triggered control protocol is presented. Moreover, it is proven that the bipartite output tracking problem is implemented, and the systems do not exhibit Zeno behavior under a fully distributed control strategy with edge-event-triggered mechanisms. Compared with the existing works, one of the highlights of this article is the design of triggering mechanisms, under which the leader avoids continuous information transmission and any pair of followers that make up the edge asynchronously transmit information through the edge. The methods greatly avoid unnecessary information transmission in the systems.