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BACKGROUND Recently, the negative effects of the COVID-19 pandemic on mental health have been addressed from the perspective of general public health. The role of problematic Internet use as a risk factor for psychological distress during the epidemic is another area of increased concern. Preliminary evidence suggests an increased psychological vulnerability for elementary and middle school teachers. OBJECTIVE The current study analyzed the role of problematic Internet use, including problematic smartphone (PSU) and problematic social media use (PSMU), as explanatory variables in terms of primary and middle school teachers’ fear of COVID-19. The effects of PNT of online teaching and fear of COVID-19 were also evaluated in terms of teachers’ psychological distress. The purpose was to empirically evaluate the relationships among these research variables using a proposed model of factors contributing to teachers’ psychological distress during COVID-19. METHODS Online survey data was collected from 9030 primary and middle school teachers. A conventional model based on recent publications in the field of problematic Internet use and psychological distress during COVID-19 was compared with a proposed model, based on prior findings indicating problematic Internet use variables serve as explanatory, rather than outcome, variables. RESULTS Structural Equation Modelling confirmed the superior goodness of fit of the proposed model (Χ2 (348) = 6220.27, RMSEA = 0.04, NNFI = 0.07, CFI = 0.99, SRMR = 0.07), as evidenced by aΔAIC of 578.505 (significant when greater than 10) between the conventional model and proposed model. The data also demonstrated that the proportion of participants with psychological distress was relatively high: depression (20.4%), anxiety (26.4%), and stress (10.2%). The problematic Internet use behaviors were significantly associated with fear of COVID-19 (PSU: t = 17.19, P < 0.001; PSMU: t = 7.91, P < 0.001). Fear of COVID-19 and PNT of online teaching were both positively related with psychological distress (Fear of COVID-19: t = 9.65, P < 0.001; PNT of online teaching: t = 10.83, P < 0.001). A significant moderating effect was found for PNT of online teaching on psychological distress (t = 5.68, P < 0.001), in that PNT of online teaching enhances the harmful effect of fear of COVID-19 on psychological distress. CONCLUSIONS The findings suggest that problematic Internet use behaviors contribute to fear of COVID-19 which, in turn, results in psychological distress. PNT of online teaching was both directly associated with increased psychological distress as well as serving as a moderator enhancing the impact of fear of COVID-19 on psychological distress. We suggest that school administrators pay attention to teachers’ psychological needs and make efforts to assist teachers in experiencing greater autonomy and relatedness from interpersonal relationships to alleviate the psychological need thwarting that may arise from online teaching tasks.

Problematic Internet use (PIU) is a risk factor for psychological distress during COVID-19, as teachers are a psychologically vulnerable population. We explored the role of PIU in terms of primary and middle school teachers’ fear of COVID-19 and psychological need thwarting (PNT) of online teaching. We empirically evaluated the relationships among these research variables in explaining teachers’ psychological distress during COVID-19. Online survey data were collected from 9,030 teachers. A high proportion of participants demonstrated psychological distress: depression (20.4%), anxiety (26.4%), and stress (10.2%). Structural equation modeling was used to test our proposed conceptual model, wherein PIU behaviors served as predictors, mediated by fear of COVID-19 and PNT of online teaching, for teachers’ psychological distress. With ideal model fit, the results of the path coefficients indicated that PIU behaviors were associated with fear of COVID-19 (p &lt, 0.001), fear of COVID-19 and PNT of online teaching were associated with psychological distress (p &lt, and fear of COVID-19 was also positively associated with PNT of online teaching (p &lt, 0.001). PSU and PSMU had an indirect positive effect on psychological distress through the mediator of fear of COVID-19 and PNT of online teaching. As such, we suggest that school administrators pay greater attention to teachers’ psychological needs through efforts to enhance teachers’ autonomy and relatedness from interpersonal relationships, alleviating PNT of online teaching. Our PNT of online teaching scale may also serve as a contribution for further research and practice.

One SUV frame in the report was analyzed(FEA)with FE software HyperMesh,modal calculated with Nastran,and then we analyzed the results with HyperView. Fortunately,we obtained five natural frequencies which show that the natural frequencies of the frame above the road surface exciting frequency,where would be no resonance occur. As a result we can image the later improved design of frame structure could provide a theoretical basis.

In this paper, a new region-based active contour model is proposed for magnetic resonance image segmentation and denoising based on the global minimization framework and level set evolution. A new region fitting energy based on Nadaraya-Watson estimator and local image information is defined to enforce the curve evolution. By this improved region fitting term, the images with noise and intensity un-uniformity can be segmented and denoised. Inspired by the Perona-Malik diffusion equation, an edge-preserving regularization term is defined through the duality formulation to penalize the length of region boundaries. By this new regularization term, the edge information is utilized to improve the contour@?s ability of capturing the edge and remaining smooth during the evolution. The energy functional of the proposed model is minimized by an efficient dual algorithm avoiding the inefficiency of the gradient descent method. Experiments on medical images demonstrate the proposed model provides a hybrid way to perform image segmentation and image denoising simultaneously.

A novel region active contour model (ACM) for image segmentation is proposed in this paper. In order to perform an accurate segmentation of images with non-homogeneous intensity, the original region fitting energy in the general region-based ACMs is improved by an anisotropic region fitting energy to evolve the contour. Using the local image information described by the structure tensor, this new region fitting energy is defined in terms of two anisotropic fitting functions that approximate the image intensity along the principal directions of variation of the intensity. Therefore, the anisotropic fitting functions extract intensity information more precisely, which enable our model to cope with the boundaries with low-contrast and complicated structures. It is incorporated into a variational formula with a total variation (TV) regularization term with respect to level set function, from which the segmentation process is performed by minimizing this variational energy functional. Experiments on the vessel and brain magnetic resonance images demonstrate the advantages of the proposed method over Chan-Vese (CV) active contours and local binary active contours (LBF) in terms of both efficiency and accuracy.

This paper proposes a novel region-based active contour model (ACM) for image segmentation, which is robust to noise and intensity non-uniformity. The energy functional of the proposed model consists of three terms, i.e., the patch-statistical region fitting term, the improved regularization term, and the intensity variation penalization term. The patch-statistical region fitting term computes the local statistical information in each patch as the basis for driving the curve accurately with resist to intensity non-uniformity and weak boundaries. And the regularization term coupling with the gradient information improves the ability of capturing the boundaries with cusps and narrow topology structures. Furthermore, an intensity variation penalization term is proposed to make sure that the segmentation result is robust to the irregular intensity variation. Experiments on medical and natural images show that the proposed model is more robust than the popular active contour models for image segmentation with noise and intensity non-uniformity.

In this paper, we propose a new region-based active contour model (ACM) for image segmentation. In particular, this model utilizes an improved region fitting term to partition the regions of interests in images depending on the local statistics regarding the intensity and the magnitude of gradient in the neighborhood of a contour. By this improved region fitting term, images with noise, intensity non-uniformity, and low-contrast boundaries can be well segmented. Integrated with the duality theory and the anisotropic diffusion process based on structure tensor, a new regularization term is defined through the duality formulation to penalize the length of active contour. By this new regularization term, the structural information of images is utilized to improve the ability of capturing the geometric features such as corners and cusps. From a numerical point of view, we minimize the energy function of our model by an efficient dual algorithm, which avoids the instability and the non-differentiability of traditional numerical solutions, e.g. the gradient descent method. Experiments on medical and natural images demonstrate the advantages of the proposed model over other segmentation models in terms of both efficiency and accuracy.

In this paper, the design space is utilized to obtain the analytical relationship between the link lengths of planar 3-DOF paralel manipulator with actuation redundancy and performance criteria based on the global velocity index. The corresponding performance atlases has been plotted, the atlases are an important base of reference for the mechanism design of this parallel manipulator.

We propose a structured feature active contour model based on the level set method for image segmentation. We make the following two contributions. First, an adaptive data fitting term detects intensity variation based on the direction and global information. Second, integrated with a structured gradient vector flow (SGVF) method, we formulate a new regularization term with respect to the level set function via the duality formulation to penalize the length of active contour. We compare the proposed method to the classical active contour methods and demonstrate through the experiments on synthetic and medical images.

This paper proposes adaptively combining the known total variation model and more recent Frobenius norm regularization for multi-frame super-resolution (SR). In contrast to existing literature, both composite prior modeling and variational optimization are achieved in the Bayesian framework by utilizing the Kullback-Leibler divergence, and the parameters related to the composite prior and noise statistics are determined adaptively and automatically, resulting in a spatially adaptive SR reconstruction method. Experimental results show that the new method can produce a high-resolution image with higher signal-to-noise ratio and better visual perception.

In this paper, a new region-based active contour model is proposed for image segmentation. Different from the general region-based active contour models, this model partitions the regions of interests in images depending on the local statistics of the intensity and the magnitude of gradient in the neighborhood of the contour. Inspired by the structure tensor method, an improved regularization term is defined through the duality formulation to penalize the length of region boundaries. Experiments on medical images demonstrate the proposed model outperforms the classical segmentation models in terms of efficiency and accuracy.