Background and Objectives: This study explores the practice and effectiveness of medical technology management in specialized hospitals based on the concept of total quality management (TQM). Methods: Focusing on a specialized tertiary respiratory hospital and guided by the framework of TQM theory, this research takes robotic surgery as an example to delineate whole-process management measures and systems for medical technologies, especially restricted technologies, and analyzes the technology’s practical performance. Results: In 2021, the filing of robotic surgery technology for record was officially approved, marking the start of its clinical use; throughout the year, a total of 710 surgeries were performed. Robotic surgery outperformed traditional surgical approaches in terms of unplanned reoperation, intraoperative and postoperative blood transfusion, and 14-day readmission rates. Conclusion: Implementing relevant measures in technology access management, quality supervision, and performance incentives for robotic surgery may not only promote the vigorous development of the technology but also enhance medical quality and ensure patient safety.
Hyperspectral remote sensing image (HSI) clustering can be defined as the process of segmenting pixels into different sets that satisfy the requirement that the differences between sets are much greater than the differences within sets. According to the fast density peak-based clustering algorithm, we propose an unsupervised HSI clustering method based on the density of pixels in the spectral space and the distance between pixels. For the metric of the density, we present an adaptive-bandwidth probability density function using pixel numbers as the input and the calculated pixel local density as the output, which determines the bandwidth on the basis of the Gaussian assumption. For the metric of the distance, in order to obtain a pixel-level spectral distance, we calculate the Euclidean distance between pixel vectors from the multiple bands. In the proposed approach: 1) use the least-squares method for the curve fitting of the two results; 2) eliminate outliers based on the Pauta criterion; 3) adopt regression calculation; and 4) obtain the cluster centers according to the classification criteria of the local density and the distance between pixel vectors. The other noncluster center points are clustered based on their similarities with the cluster centers by iteration. Finally, we compare the results with those of other unsupervised clustering methods and the reference data sets.
The rapid development of material science is increasing the demand for the multiscale design of materials. The concurrent multiscale topology optimization based on the Direct FE2 method can greatly improve computational efficiency, but it may lead to the checkerboard problem. In order to solve the checkerboard problem and reconstruct the results of the Direct FE2 model, this paper proposes a filtering-based reconstruction method. This solution is of great significance for the practical application of multiscale topology optimization, as it not only solves the checkerboard problem but also provides the optimized full model based on interpolation. The filtering method effectively eliminates the checkerboard pattern in the results by smoothing the element densities. The reconstruction method restores the smoothness of the optimized structure by interpolating between the filtered densities. This method is highly effective in solving the checkerboard problem, as demonstrated in our numerical examples. The results show that the proposed algorithm produces feasible and stable results.
Global and local land-cover mapping products provide important data on land surface. However, the accuracy of land-cover products is the key issue for their further scientific application. There has been neglect of the relationship between inclusion probability and spatial heterogeneity in traditional spatially balanced sampling. The aim of this paper was to propose an improved spatially balanced sampling method using landscape pattern-based inclusion probability. Compared with other global land-cover datasets, Globeland30 has the advantages of high resolution and high classification accuracy. A two-stage stratified spatially balanced sampling scheme was designed and applied to the regional validation of GlobeLand30 in China. In this paper, the whole area was divided into three parts: the Tibetan Plateau region, the Northwest China region, and the East China region. The results show that 7242 sample points were selected, and the overall accuracy of GlobeLand30-2010 in China was found to be 80.46%, which is close to the third-party assessment accuracy of GlobeLand30. This method improves the representativeness of samples, reduces the classification error of remote sensing, and provides better guidance for biodiversity and sustainable development of environment.
Abstract Pulmonary fibrosis, a sequela of lung injury resulting from severe infection such as severe acute respiratory syndrome‐like coronavirus (SARS‐CoV‐2) infection, is a kind of life‐threatening lung disease with limited therapeutic options. Herein, inhalable liposomes encapsulating metformin, a first‐line antidiabetic drug that has been reported to effectively reverse pulmonary fibrosis by modulating multiple metabolic pathways, and nintedanib, a well‐known antifibrotic drug that has been widely used in the clinic, are developed for pulmonary fibrosis treatment. The composition of liposomes made of neutral, cationic or anionic lipids, and poly(ethylene glycol) (PEG) is optimized by evaluating their retention in the lung after inhalation. Neutral liposomes with suitable PEG shielding are found to be ideal delivery carriers for metformin and nintedanib with significantly prolonged retention in the lung. Moreover, repeated noninvasive aerosol inhalation delivery of metformin and nintedanib loaded liposomes can effectively diminish the development of fibrosis and improve pulmonary function in bleomycin‐induced pulmonary fibrosis by promoting myofibroblast deactivation and apoptosis, inhibiting transforming growth factor 1 (TGFβ1) action, suppressing collagen formation, and inducing lipogenic differentiation. Therefore, this work presents a versatile platform with promising clinical translation potential for the noninvasive inhalation delivery of drugs for respiratory disease treatment.
The fat boundary method (FBM) is a fictitious domain method, proposed to solve Poisson problems in a domain with small perforations. It can achieve higher accuracy around holes, which makes it very suitable to solve elasticity problems because stress concentrations often appear around holes. However, there are some strict restrictions of the FBM limiting the wide range of applications. For example, the original FBM deals with perforated rectangular domain with only Dirichlet boundary conditions. Furthermore, because the global domain is extended to the holes, analytical solutions in holes corresponding to the Dirichlet boundary conditions around holes are required. This limits both the boundary conditions around holes and the shape of holes, because for arbitrary holes it is difficult to get the analytical solutions. This article makes an attempt to break these limitations and apply the FBM to elasticity. Firstly, we review the FBM and introduce Neumann boundary conditions to the rectangular domain. A mathematical proof of the conditional convergence of the algorithm is presented. Furthermore, the FBM is compared with the Lagrange multiplier method to clarify that the FBM is one kind of weak imposition method. Then we apply the FBM to linear elasticity and the dual fat boundary method is proposed to solve problems without analytical solutions in holes. Some numerical examples are presented to verify the method proposed here.
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