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This article investigates robust multiple fault detection (FD) and fault estimation (FE) schemes, based on adaptive finite‐time observer (AFTO), applied to nonlinear systems with disturbances, actuator faults, and sensor faults. The AFTO is designed, and a parameter adaptive law is constructed to continuously optimize the residual based performance function to improve the AFTO robustness. The schemes are able to simultaneously detect and estimate unknown states, disturbances, and faults. The AFTO's design, finite‐time convergence and stability conditions are guaranteed in terms of linear matrix inequalities. Finally, simulation results, based on two typical practical systems, are given to illustrate the effectiveness of the proposed approaches. The simulation results also show that the designed AFTO can offer faster convergence speed, higher estimation accuracy, and stronger robustness than conventional H∞$$ {H}_{\infty } $$ observer and sliding mode observer. [ABSTRACT FROM AUTHOR]

Accurate extraction of farmland boundaries is crucial for improving the efficiency of farmland surveys, achieving precise agricultural management, enhancing farmers’ production conditions, protecting the ecological environment, and promoting local economic development. Remote sensing and deep learning are feasible methods for creating large-scale farmland boundary maps. However, existing neural network models have limitations that restrict the accuracy and reliability of agricultural parcel extraction using remote sensing technology. In this study, we used high-resolution satellite images (2 m, 1 m, and 0.8 m) and the U2-Net++ model based on the RSU module, deep separable convolution, and the channel-spatial attention mechanism module to extract different types of fields. Our model exhibited significant improvements in farmland parcel extraction compared with the other models. It achieved an F1-score of 97.13%, which is a 7.36% to 17.63% improvement over older models such as U-Net and FCN and a more than 2% improvement over advanced models such as DeepLabv3+ and U2-Net. These results indicate that U2-Net++ holds the potential for widespread application in the production of large-scale farmland boundary maps.

Efforts have been made to find a better therapeutic approach with fewer side effects in treating chronic obstructive pulmonary disease (COPD). This study investigated the effect of Buyuan decoction (BYD) on autophagy in COPD rats. An experimental model with Sprague-Dawley rats was established by lipopolysaccharide (LPS) injection and cigarette smoke exposure. Rats were randomly allocated into blank control (normal control), experimental model, low-dose BYD (8.0 g/kg/day), medium-dose BYD (16.0 g/kg/day), high-dose BYD (32.0 g/kg/day) and 3-MA (methyladenine) groups (6 rats/group). Cell and tissue morphology were observed using hematoxylin and eosin staining. Autophagic vesicles were examined with a transmission electron microscope. Protein expression of LC3-II/I, BNIP- 1, ATG7, p62, PI3K and p-PI3K in lung tissue was detected by Western blotting. Compared with the experimental model group, the inflammatory infiltrate in lung tissue was reduced, the nuclei of the pulmonary epithelial cells were restored to normal, and the expression of LC3, BNIP1, ATG7 and p-PI3K was significantly downregulated, while p62 expression was significantly upregulated after treatment with the BYD. The effect was most significant in the low-dose BYD group (P

Cardiac diffusion tensor imaging (cDTI) is a promising and noninvasive magnetic resonance imaging (MRI) technique for assessing myocardial fiber microstructure. However, its clinical application is hampered by lengthy scanning and susceptibility to motion artifacts. This study proposes a cDTI reconstruction model (OSDTI), which leverages octave convolution (OctConv) and Swin Transformer to reconstruct diffusion‐weighted (DW) images from undersampled k‐space data. Our model capitalizes on the strengths of both OctConv and Swin Transformers. Specifically, the OctConv module excels at capturing intricate details and texture information within DW images, while the residual‐connected Swin Transformer module facilitates information retention across various feature extraction stages, thereby enhancing overall image comprehension and reconstruction accuracy. The evaluation of OSDTI using a human ex vivo heart dataset robustly validated the generalization ability of the model. Comparative analyze against existing deep‐learning methods demonstrated OSDTI's superior performance in both DW image quality and diffusion tensor metrics. [ABSTRACT FROM AUTHOR]

Hybrid porous tilting pad bearings with the advantages of gas porous bearings and tilting pad bearings exhibited excellent rotor-supporting performance with high accuracy and stability. Under hybrid lubrication, the transient adaptive motion of the tilting pads was important for the rotordynamic characteristics. A novel fluid–structure interaction model of hybrid porous tilting pad bearings was developed to investigate the transient hydro forces and the pad's adaptive motion. The rotordynamic coefficients of the hybrid porous tilting pad bearings were identified using the rotor harmonic trajectory. The computational fluid dynamics-fluid–structure interaction model was validated with the experimental data. The effects of eccentricity ratios, rotor speeds, supply pressures, pivot radial stiffnesses, and dampings on the pad adaptive motion and the rotordynamic coefficients were evaluated. The pad self-adaption motions significantly influenced the supporting and vibration-absorbing characteristics of hybrid porous tilting pad bearings. [ABSTRACT FROM AUTHOR]