Stabilizing the Oxygen Lattice and Reversible Oxygen Redox Chemistry through Structural Dimensionality in Lithium-Rich Cathode Oxides.

Lattice-oxygen redox (l-OR) has become an essential companion to the traditional transition-metal (TM) redox charge compensation to achieve high capacity in Li-rich cathode oxides. However, the understanding of l-OR chemistry remains elusive, and a critical question is the structural effect on the stability of l-OR reactions. Herein, the coupling between l-OR and structure dimensionality is studied. We reveal that the evolution of the oxygen-lattice structure upon l-OR in Li-rich TM oxides which have a three-dimensional (3D)-disordered cation framework is relatively stable, which is in direct contrast to the clearly distorted oxygen-lattice framework in Li-rich oxides which have a two-dimensional (2D)/3D-ordered cation structure. Our results highlight the role of structure dimensionality in stabilizing the oxygen lattice in reversible l-OR, which broadens the horizon for designing high-energy-density Li-rich cathode oxides with stable l-OR chemistry

Thermal sensitive shape recovery and mass transfer properties of polyurethane/modified MWNT composite membranes synthesized via in-situ solution pre-polymerization

Polyurethane/multiwalled carbon nanotube (MWNT) composite membranes with thermal sensitive shape memory and mass transfer properties were synthesized via in situ pre-polymerization from concentration pretreated MWNT in DMF (N,N-dimethylformamide). Composite membranes were made from these polyurethane/MWNT solutions by casting them into Teflon plates. SEM photographs demonstrated that the MWNT distributed relatively homogenously in polyurethane matrix at low content and preferred to align in the membrane 2-dimension plane. The shape recovery and mass transfer properties of the prepared membranes were studied. Thermo-mechanical cyclic tensile testing results suggested that the shape recovery ratio increased prominently at below 2.0 wt% of MWNT because of the MWNTs high interaction with the hard segments; however at 3.0 wt% MWNT content, the shape recovery ratios decreased which can be ascribed to the relatively inhomogeneous distribution of MWNT and the decrease phase separation of the polyurethane at this high MWNT content. The water vapor permeability (WVP) studies showed that at below the soft segment melting transition temperature, at 0.25 wt% MWNT content, the WVP of the composite membrane decreased because the nano-sized MWNT acting as nucleating agent, thus, enhanced soft segment ordered crystal structure. In contrast, with increasing MWNT content, the WVP increased because in one aspect, MWNT constrained the forming of ordered soft segment phase structure; in another aspect, the straight MWNT with large aspect ratios offered a relatively straight “free” pathway for water molecule diffusion on the surface of MWNT or inside MWNT to pass through. At temperature above the soft segment phase melting transition temperature, the WVP increased markedly with increasing MWNT content. This could also be attributed to the pathway effect of MWNT by forming a channel through which water molecules could diffuse rapidly

3-Dimensional Modeling and Attitude Control of Multi-Joint Autonomous Underwater Vehicles

To achieve rapid and flexible vertical profile exploration of deep-sea hybrid structures, a multi-joint autonomous underwater vehicle (MJ-AUV) with orthogonal joints was designed. This paper focuses on the 3-dimensional (3D) modeling and attitude control of the designed vehicle. Considering the situation of gravity and buoyancy imbalance, a 3D model of the MJ-AUV was established according to Newton’s second law and torque balance principle. And then the numerical simulation was carried out to verify the credibility of the model. To solve the problems that the pitch and yaw attitude of the MJ-AUV are coupled and the disturbance is unknown, a linear quadratic regulator (LQR) decoupling control method based on a linear extended state observer (LESO) was proposed. The system was decoupled into pitch and yaw subsystems, treated the internal forces and external disturbances of each subsystem as total disturbances, and estimated the total disturbances with LESO. The control law was divided into two parts. The first part was the total disturbance compensator, while the second part was the linear state feedback controller. The simulation results show that the overshoot of the controlled system in the dynamic process is nearly 0 rad, reaching the design value very smoothly. Moreover, when the controlled system is in a stable state, the control precision is within 0.005%

Asymptotic Normality of the Estimators for Fractional Brownian Motions with Discrete Data

This paper deals with the problem of estimating the Hurst parameter in the fractional Brownian motion when the Hurst index is greater than one half. The estimation procedure is built upon the marriage of the autocorrelation approach and the maximum likelihood approach. The asymptotic properties of the estimators are presented. Using the Monte Carlo experiments, we compare the performance of our method to existing ones, namely, R/S method, variations estimators, and wavelet method. These comparative results demonstrate that the proposed approach is effective and efficient

Proceedings of the 1st Chinese-Austrian Workshop on Environmental Odour: Emission-Dispersion-Impact Assessment

Table of Contents Spatial distribution of odorous compounds in an enclosed waste mechanical biological treatment plant Assessment of odour activity value coeffcient and odor contribution based on binary interaction effects in a waste disposal plant Mapping odour sources with a mobile robot in a time variant airflow environment Odour dispersion modelling in Austria Odour impact criteria to avoid annoyance Experimental comparison of random search strategies for multirobot based odour finding without wind information Odour prediction model using odor activity value from pharmaceutical industr

An EEG-Based Depressive Detection Network with Adaptive Feature Learning and Channel Activation

Electroencephalography (EEG) plays a pivotal role in the diagnosis of various neurological conditions, most notably major depressive disorder (MDD). However, deep learning-based methods currently employed for MDD detection tasks exhibit inadequate generalization capabilities, particularly across different EEG electrode channels, and demonstrate limited feature representation capacity. In this paper, we present a novel approach referred to as adaptive feature learning (AFL), which leverages kernel embedding to facilitate the learning of domain-invariant features across subjects within a reproducing kernel Hilbert space. This method aims to enhance the model's ability to generalize across multiple subjects' EEG signals. Furthermore, our research revealed that batch normalization (BN) layers within the existing MDD detection network frequently result in feature channel suppression, potentially compromising the representation power of the features. To address this issue, we propose channel activation (CA), which employs decorrelation to reactivate suppressed feature maps, thereby enhancing the model’s feature representation capability, particularly for subtle EEG changes. The effectiveness of the proposed methods is evaluated using the leave-one-subject-out protocol on MODMA and PRED+CT datasets, yielding detection accuracies of 90.56\% (MODMA) and 96.51\% (PRED+CT). Our experimental findings exhibit the superior performance of our method compared to state-of-the-art (SOTA) methods in terms of MDD recognition

Adaptive Local Maximum-Entropy Surrogate Model and Its Application to Turbine Disk Reliability Analysis

The emerging Local Maximum-Entropy (LME) approximation, which combines the advantages of global and local approximations, has an unsolved issue wherein it cannot adaptively change the morphology of the basis function according to the local characteristics of the sample, which greatly limits its highly nonlinear approximation ability. In this research, a novel Adaptive Local Maximum-Entropy Surrogate Model (ALMESM) is proposed by constructing an algorithm that adaptively changes the LME basis function and introduces Particle Swarm Optimization to ensure the optimality of the adaptively changed basis function. The performance of the ALMESM is systematically investigated by comparison with the LME approximation, a Radial basis function, and the Kriging model in two explicit highly nonlinear mathematical functions. The results show that the ALMESM has the highest accuracy and stability of all the compared models. The ALMESM is further validated by a highly nonlinear engineering case, consisting of a turbine disk reliability analysis under geometrical uncertainty, and achieves a desirable result. Compared with the direct Monte Carlo method, the relative error of the ALMESM is less than 1%, which indicates that the ALMESM has considerable potential for highly nonlinear problems and structural reliability analysis