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Levels as well as related hazard to health evaluation involving bug sprays, phthalates as well as precious metals in bananas via Shanghai, China.
RESULTS All mechanisms are successfully coordinated to mimic the infant feeding mechanism. Suckling frequency and pressure values on the breast phantom from the experimental apparatus are in good agreement with the clinical data. The change in nipple deformation captured by BIBS matches with those from in vivo clinical ultrasound images. SIGNIFICANCE The fully-developed breastfeeding simulator provides a powerful tool for understanding the bio-mechanics of breastfeeding and a foundation for future breastfeeding device development.OBJECTIVE In minimally invasive surgery (MIS), in situ augmented reality (AR) navigation systems are usually implemented using a glasses-free 3D display to represent the preoperative tissue structure, and can provide intuitive see-through guidance information. However, due to changes in intraoperative tissue, the preoperative tissue structure is not able to exactly correspond to reality, which influences the precision of in situ AR navigation. To solve this problem, we propose a method to update the tissue structure for in situ AR navigation in such way to reflect changes in intraoperative tissue. METHODS The proposed method to update the tissue structure is based on the calibrated ultrasound and two-level surface warping technologies. Firstly, the particle filter-based calibration is implemented to perform ultrasound calibration and obtain intraoperative position of anatomical points. Secondly, intraoperative positions of anatomical points are inputted in the two-level surface warping method to update the preoperative tissue structure. Finally, the glasses-free real 3-D display of the updated tissue structure is finished, and is superimposed onto a patient by a translucent mirror for in situ AR navigation. RESULTS we validated the proposed method by simulating liver tissue intervention, and achieved the tissue updating accuracy of 92.86%. Furthermore, the targeting error of AR navigation based on the proposed method was also evaluated through minimally invasive liver surgery, and the acquired mean targeting error was 1.92 mm. CONCLUSION The results demonstrate that the proposed AR navigation method is effective. SIGNIFICANCE The proposed navigation method can facilitate MIS, as it provides accurate 3D navigation information.Correlation filter has been demonstrated remarkable success for visual tracking recently. However, most existing methods often face model drift caused by several factors, such as unlimited boundary effect, heavy occlusion, fast motion, and distracter perturbation. To address the issue, this paper proposes a unified dynamic collaborative tracking framework that can perform more flexible and robust position prediction. Specifically, the framework learns the object appearance model by jointly training the objective function with three components target regression submodule, distracter suppression submodule, and maximum margin relation submodule. The first submodule mainly takes advantage of the circulant structure of training samples to obtain the distinguishing ability between the target and its surrounding background. The second submodule optimizes the label response of the possible distracting region close to zero for reducing the peak value of the confidence map in the distracting region. Inspired by the structure output support vector machines, the third submodule is introduced to utilize the differences between target appearance representation and distracter appearance representation in the discriminative mapping space for alleviating the disturbance of the most possible hard negative samples. In addition, a CUR filter as an assistant detector is embedded to provide effective object candidates for alleviating the model drift problem. Comprehensive experimental results show that the proposed approach achieves the state-of-the-art performance in several public benchmark data sets.Despite the promising progress made in recent years, person reidentification (re-ID) remains a challenging task due to the complex variations in human appearances from different camera views. This paper proposes to tackle this task by jointly learning feature representation and distance metric in an end-to-end manner. Existing deep metric learning-based re-ID methods usually encounter the following two weaknesses 1) most works based on pairwise or triplet constraints often suffer from slow convergence and poor local optima, partially because they use very limited samples for each update and 2) hard negative sample mining has been widely applied in existing works. However, hard positive samples, which also contribute to the training of network, have not received enough attention. To alleviate these problems, we develop a novel structural metric learning objective for person re-ID, in which each positive pair is allowed to be compared against all negative pairs in a minibatch and each positive pair is adaptively assigned a hardness-aware weight to modulate its contribution. The introduced positive pair weighting strategy enables the algorithm to focus more on the hard positive samples. Furthermore, we propose to enhance the proposed loss function by adding a global loss term to reduce the variances of positive/negative pair distances, which is able to improve the generalization capability of the network model. By this approach, person images can be nonlinearly mapped into a low-dimensional embedding space where similar samples are kept closer and dissimilar samples are pushed farther apart. We implement the proposed algorithm using the inception architecture and evaluate it on three large-scale re-ID data sets. Experiment results demonstrate that our approach is able to outperform most state of the arts while using much lower dimensional deep features."Schools rethink security training" was the headline on page 1 of the 30 December 2019 issue of The Baltimore Sun daily newspaper. The accompanying article went on to explain that Maryland school students felt unsafe at school. Congo Red cell line Students on average rated their physical safety at 3.5 and emotional safety at 5.4, each on a scale of 1 to 10, with 10 being the best score. Many students gave their physical safety scores at 1 out of 10. And this is despite active shooter drills that are meant to teach them what to do if there is a violent confrontation, and in which they have all had to participate.
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