This paper proposes a novel adaptive intelligent tracking controller design scheme for a type of nonlinear delayed systems with completely unknown nonlinearities and non-strict-feedback structure. In the backsteppping-based design architecture, the intelligent estimation technique is utilized to approximate the unknown nonlinear functions via neural networks (NNs), and Lyapunov-Krasovskii functionals are designed to deal with the unknown delay terms. The constructed adaptive intelligent controller guarantees the semi-global boundedness of the resulting closed-loop system and the system output eventually converges to a small neighborhood around the desired reference signal. In the end, the presented simulation results verify the effectiveness of the proposed design method.

Purpose: This study aims to compute 3D model similarity by extracting and comparing shape features from the neutral files. Design/methodology/approach: In this work, the clear text encoding document STEP (Standard for The Exchange of Product model data) of 3D models was analysed, and the models were characterized by two-depth trees consisting of both surface and shell nodes. All surfaces in the STEP files can be subdivided into three kinds, namely, free, analytical and loop surfaces. Surface similarity is defined by the variation coefficients of distances between data points on two surfaces, and subsequently, the shell similarity and 3D model similarity are determined using an optimal algorithm for bipartite graph matching. Findings: This approach is used to experimentally verify the effectiveness of the 3D model similarity algorithm. Originality/value: The novelty of this study research lies in the computation of 3D model similarity by comparison of all surfaces. In addition, the study makes several key observations: surfaces reflect the most information concerning the functions and attributes of a 3D model and so the similarity between surfaces generates more comprehensive content (both external and internal); semantic-based 3D retrieval can be obtained under the premise of comparison of surface semantics; and more accurate similarity of 3D models can be obtained using the optimal algorithm of bipartite graph matching for all surfaces.

Purpose: The objective of this study was to establish a reverse-transcription loop-mediated isothermal amplification (RT-LAMP) method for rapid detection of human immunodeficiency virus type 1 (HIV-1). Materials and Methods: The HIV-1 integrase gene region was selected because it was a conserved part of the HIV-1 genome. Six primers specific to eight regions of the HIV-1 integrase gene were designed. A total of 171 samples (18 HIV-1 confirmed positive samples and 153 serum specimens were collected in this study) were tested by RT-LAMP and reverse-transcription polymerase chain reaction (RT-PCR). After amplification in an isothermal water bath for 45 min, samples containing HIV-1 generated the expected ladder-like products while other viruses generated no product. Results: The sensitivity and specificity of the RT-LAMP assay were evaluated by comparison with RT-PCR. The assay was significantly more sensitive than normal gel-based RT-PCR. Conclusion: Because it is specific and simple, the RT-LAMP assay can be widely applied in clinical laboratories for rapid detection of HIV-1.

The segmentation and quantitative features of periventricular white matter injury in magnetic resonance imaging are helpful for determining an accurate diagnosis of neonatal hypoxia ischemia encephalopathy (HIE) in clinical practice, but they are tedious tasks. In this paper, we present an interactive segmentation method for clinical magnetic resonance imaging (MRI) using sample estimation at the subvoxel level to establish a quantitative diagnosis. The coarse-grained voxels in clinical MRI are divided into subvoxels, and the intensities of subvoxels are adjusted using gradient adaptive filtering. Because the cerebrospinal fluid is more obvious in grayscale than the white matter and gray matter on T1-weighted images (T1WI), T2-weighted images (T2WI) and fluid-attenuated inversion recovery (FLAIR) images, and its population variance follows a Chi-square distribution, we segment the periventricular region as an anchor by estimating the parameters of the selected data and expand the anchor to obtain periventricular white matter of various widths. After aligning the segmented periventricular white matter in various modalities, we compute the radiomics features of regions of interest for the quantitative diagnosis of HIE. Current clinical practice confirms that our approach is effective and satisfies the clinical diagnostic requirements for neonatal HIE.