Output Feedback Controller Design for a Class of MIMO Nonlinear Systems Using High-Order Sliding-Mode Differentiators With Application to a Laboratory 3-D Crane

This paper addresses the problem of output feedback control design for a class of multi-input-multi-output (MIMO) nonlinear systems where the number of inputs is less than that of outputs. There are two difficulties in this design problem: 1) too few control inputs will not generally allow independent control over all outputs and 2) the state of the system is not available for measurements, and only the outputs are available through measurements. To address the first issue, a practical output feedback control problem is formulated, aiming to regulate only part of the outputs, and a controller structure with two design components in all or some chosen control inputs is proposed. To cope with the second difficulty, the recently developed high-order sliding mode differentiators (HOSMDs) are used to estimate the derivatives of the outputs needed in the controller design. With the derivatives estimated using HOSMDs, an output feedback controller is designed using the backstepping approach. Stability results are established for the designed controller under certain conditions. In order to test the applicability of the proposed output feedback controller in practical industrial problems, experiments are carried out though implementing the controller on a laboratory-scale 3-D crane. The experimental results are presented and reveal the advantage of the proposed controller structure, as well as the effect of controller gain and sampling periods

CartiMorph: a framework for automated knee articular cartilage morphometrics

We introduce CartiMorph, a framework for automated knee articular cartilage morphometrics. It takes an image as input and generates quantitative metrics for cartilage subregions, including the percentage of full-thickness cartilage loss (FCL), mean thickness, surface area, and volume. CartiMorph leverages the power of deep learning models for hierarchical image feature representation. Deep learning models were trained and validated for tissue segmentation, template construction, and template-to-image registration. We established methods for surface-normal-based cartilage thickness mapping, FCL estimation, and rule-based cartilage parcellation. Our cartilage thickness map showed less error in thin and peripheral regions. We evaluated the effectiveness of the adopted segmentation model by comparing the quantitative metrics obtained from model segmentation and those from manual segmentation. The root-mean-squared deviation of the FCL measurements was less than 8%, and strong correlations were observed for the mean thickness (Pearson's correlation coefficient $\rho \in [0.82,0.97]$), surface area ($\rho \in [0.82,0.98]$) and volume ($\rho \in [0.89,0.98]$) measurements. We compared our FCL measurements with those from a previous study and found that our measurements deviated less from the ground truths. We observed superior performance of the proposed rule-based cartilage parcellation method compared with the atlas-based approach. CartiMorph has the potential to promote imaging biomarkers discovery for knee osteoarthritis.Comment: To be published in Medical Image Analysi

An Uncertainty Aided Framework for Learning based Liver T1ρT_1\rho Mapping and Analysis

Objective: Quantitative $T_1\rho$ imaging has potential for assessment of biochemical alterations of liver pathologies. Deep learning methods have been employed to accelerate quantitative $T_1\rho$ imaging. To employ artificial intelligence-based quantitative imaging methods in complicated clinical environment, it is valuable to estimate the uncertainty of the predicated $T_1\rho$ values to provide the confidence level of the quantification results. The uncertainty should also be utilized to aid the post-hoc quantitative analysis and model learning tasks. Approach: To address this need, we propose a parametric map refinement approach for learning-based $T_1\rho$ mapping and train the model in a probabilistic way to model the uncertainty. We also propose to utilize the uncertainty map to spatially weight the training of an improved $T_1\rho$ mapping network to further improve the mapping performance and to remove pixels with unreliable $T_1\rho$ values in the region of interest. The framework was tested on a dataset of 51 patients with different liver fibrosis stages. Main results: Our results indicate that the learning-based map refinement method leads to a relative mapping error of less than 3% and provides uncertainty estimation simultaneously. The estimated uncertainty reflects the actual error level, and it can be used to further reduce relative $T_1\rho$ mapping error to 2.60% as well as removing unreliable pixels in the region of interest effectively. Significance: Our studies demonstrate the proposed approach has potential to provide a learning-based quantitative MRI system for trustworthy $T_1\rho$ mapping of the liver

The determinants of COVID-19 vaccine uptake among migrants from 109 countries residing in China: A cross-sectional study

BackgroundThe present study aimed to investigate the prevalence of COVID-19 vaccine uptake among foreign migrants in China and to explore the determinants of their vaccine uptake behavior.MethodsFrom June to October 2021, we used convenience and snowball sampling to recruit a sample of 764 participants from five cities in which the overwhelming majority of foreign migrants in China live. The chi-square (χ2) tests were used to examine vaccination distribution according to demographic characteristics. Multivariate logistic regression models visualized by forest plot were used to investigate the associations between significant determinants and vaccine uptake.ResultsOverall, the prevalence of vaccination rate was 72.9% [95% confidence interval (CI): 69.9–76.0%]. Migrants whose social participation was very active [adjusted odds ratio (AOR): 2.95, 95% CI: 1.36–6.50, P = 0.007] or had perceived COVID-19 progression prevention by the vaccine (AOR: 1.74, 95% CI: 1.01–3.02, P = 0.012) had higher odds of vaccination compared to those whose social participation was inactive or who did not have this perception. Migrants who perceived the vaccine uptake process as complex (AOR: 0.47, 95% CI: 0.27–0.80, P = 0.016) or were unsure of their physical suitability for the vaccine (AOR: 0.40, 95% CI: 0.24–0.68, P < 0.001) had lower odds of vaccination compared to those who did not have these perceptions. Furthermore, migrants from emerging and developing Asian countries (AOR: 2.32, 95% CI: 1.07–5.21, P = 0.04) and the Middle East and Central Asia (AOR: 2.19, 95% CI: 1.07–4.50, P = 0.03) had higher odds of vaccination than those from major advanced economies (G7) countries, while migrants from other advanced economic countries (OR: 0.27, 95% CI: 0.11–0.63, P = 0.003) had lower odds of vaccination than those from G7 countries.ConclusionIt may be beneficial to promote vaccine uptake among migrants by ensuring effective community engagement, simplifying the appointment and uptake process, and advocating the benefits and target populations of the COVID-19 vaccine

Uncertainty-weighted Multi-tasking for T1ρT_{1\rho} and T2_2 Mapping in the Liver with Self-supervised Learning

Multi-parametric mapping of MRI relaxations in liver has the potential of revealing pathological information of the liver. A self-supervised learning based multi-parametric mapping method is proposed to map T$T_{1\rho}$ and T$_2$ simultaneously, by utilising the relaxation constraint in the learning process. Data noise of different mapping tasks is utilised to make the model uncertainty-aware, which adaptively weight different mapping tasks during learning. The method was examined on a dataset of 51 patients with non-alcoholic fatter liver disease. Results showed that the proposed method can produce comparable parametric maps to the traditional multi-contrast pixel wise fitting method, with a reduced number of images and less computation time. The uncertainty weighting also improves the model performance. It has the potential of accelerating MRI quantitative imaging

On the Approximability of the Exemplar Adjacency Number Problem for Genomes with Gene Repetitions

In this paper, we apply a measure, exemplar adjacency number, which complements and extends the well-studied breakpoint distance between two permutations, to measure the similarity between two genomes (or in general, between any two sequences drawn from the same alphabet). For two genomes and drawn from the same set of n gene families and containing gene repetitions, we consider the corresponding Exemplar Adjacency Number problem (EAN), in which we delete duplicated genes from and such that the resultant exemplar genomes (permutations) G and H have the maximum adjacency number. We obtain the following results. First, we prove that the one-sided 2-repetitive EAN problem, i.e., when one of and is given exemplar and each gene occurs in the other genome at most twice, can be linearly reduced from the Maximum Independent Set problem. This implies that EAN does not admit any -approximation algorithm, for any , unless P = NP. This hardness result also implies that EAN, parameterized by the optimal solution value, is W[1]-hard. Secondly, we show that the two-sided 2-repetitive EAN problem has an -approximation algorithm, which is tight up to a constant factor