The Status and Prospects of Community Education Workers in China

Professionalization, career development prospects, and social value are the three basic components of the status and prospects of community education workers, which influence their choice to continue their careers or not. In China, these problems are complex and lacking in systematic research, and the current situation does not meet the needs of community education. This study interviewed 24 community workers regarding their salaries, working conditions, and training and career advancement opportunities to evaluate this situation in Ningbo City. The findings highlight challenges in the evaluation processes and work motivations of community education workers, including teams without professional knowledge, lack of training opportunities, unsupportive policies, and low salaries. These findings can be used by governments and community workers to find collaborative ways to facilitate community education processes, including the provision of adult education for community educators. New legal policies to raise the status of community educators are also suggested

3D Medical Image Segmentation based on multi-scale MPU-Net

The high cure rate of cancer is inextricably linked to physicians' accuracy in diagnosis and treatment, therefore a model that can accomplish high-precision tumor segmentation has become a necessity in many applications of the medical industry. It can effectively lower the rate of misdiagnosis while considerably lessening the burden on clinicians. However, fully automated target organ segmentation is problematic due to the irregular stereo structure of 3D volume organs. As a basic model for this class of real applications, U-Net excels. It can learn certain global and local features, but still lacks the capacity to grasp spatial long-range relationships and contextual information at multiple scales. This paper proposes a tumor segmentation model MPU-Net for patient volume CT images, which is inspired by Transformer with a global attention mechanism. By combining image serialization with the Position Attention Module, the model attempts to comprehend deeper contextual dependencies and accomplish precise positioning. Each layer of the decoder is also equipped with a multi-scale module and a cross-attention mechanism. The capability of feature extraction and integration at different levels has been enhanced, and the hybrid loss function developed in this study can better exploit high-resolution characteristic information. Moreover, the suggested architecture is tested and evaluated on the Liver Tumor Segmentation Challenge 2017 (LiTS 2017) dataset. Compared with the benchmark model U-Net, MPU-Net shows excellent segmentation results. The dice, accuracy, precision, specificity, IOU, and MCC metrics for the best model segmentation results are 92.17%, 99.08%, 91.91%, 99.52%, 85.91%, and 91.74%, respectively. Outstanding indicators in various aspects illustrate the exceptional performance of this framework in automatic medical image segmentation.Comment: 37 page

Deforming black holes with even multipolar differential rotation boundary

Motivated by the novel asymptotically global AdS$_4$ solutions with deforming horizon in [JHEP {\bf 1802}, 060 (2018)], we analyze the boundary metric with even multipolar differential rotation and numerically construct a family of deforming solutions with quadrupolar differential rotation boundary, including two classes of solutions: solitons and black holes. In contrast to solutions with dipolar differential rotation boundary, we find that even though the norm of Killing vector $\partial_t$ becomes spacelike for certain regions of polar angle $\theta$ when $\varepsilon>2$, solitons and black holes with quadrupolar differential rotation still exist and do not develop hair due to superradiance. Moreover, at the same temperature, the horizonal deformation of quadrupolar rotation is smaller than that of dipolar rotation. Furthermore, we also study the entropy and quasinormal modes of the solutions, which have the analogous properties to that of dipolar rotation.Comment: 18 pages, 21 figure

Performance analysis and algorithm design for distributed transmit beamforming

Wireless sensor networks has been one of the major research topics in recent years because of its great potential for a wide range of applications. In some application scenarios, sensor nodes intend to report the sensing data to a far-field destination, which cannot be realized by traditional transmission techniques. Due to the energy limitations and the hardware constraints of sensor nodes, distributed transmit beamforming is considered as an attractive candidate for long-range communications in such scenarios as it can reduce energy requirement of each sensor node and extend the communication range. However, unlike conventional beamforming, which is performed by a centralized antenna array, distributed beamforming is performed by a virtual antenna array composed of randomly located sensor nodes, each of which has an independent oscillator. Sensor nodes have to coordinate with each other and adjust their transmitting signals to collaboratively act as a distributed beamformer. The most crucial problem of realizing distributed beamforming is to achieve carrier phase alignment at the destination. This thesis will investigate distributed beamforming from both theoretical and practical aspects. First, the bit error ratio performance of distributed beamforming with phase errors is analyzed, which is a key metric to measure the system performance in practice. We derive two distinct expressions to approximate the error probability over Rayleigh fading channels corresponding to small numbers of nodes and large numbers of nodes respectively. The accuracy of both expressions is demonstrated by simulation results. The impact of phase errors on the system performance is examined for various numbers of nodes and different levels of transmit power. Second, a novel iterative algorithm is proposed to achieve carrier phase alignment at the destination in static channels, which only requires one-bit feedback from the destination. This algorithm is obtained by combining two novel schemes, both of which can greatly improve the convergence speed of phase alignment. The advantages in the convergence speed are obtained by exploiting the feedback information more efficiently compared to existing solutions. Third, the proposed phase alignment algorithm is modified to track time-varying channels. The modified algorithm has the ability to detect channel amplitude and phase changes that arise over time due to motion of the sensors or the destination. The algorithm can adjust key parameters adaptively according to the changes, which makes it more robust in practical implementation