Teaching Dilemmas and Incentive Strategies of University Young Teachers: An Expectancy Theory Perspective

As an important academic human resource in universities, young teachers undertake the responsibility of cultivating innovative talents for the country. From the perspective of expectancy theory, this study analyzes the teaching dilemmas of young teachers in universities from three aspects: expectancy, valence, and instrumentality. Based on results, specific suggestions are proposed to stimulate the teaching enthusiasm of young teachers, including establishing a scientifically reasonable assessment system to balance the relationship between teaching and research; optimizing the salary and welfare system of young teachers to motivate their teaching initiative; and enriching training and development programs for young teachers to enhance their teaching abilities. Keywords: teaching; incentive; expectation; young teachers DOI: 10.7176/JEP/14-18-11 Publication date:June 30th 202

Bird watching in China reveals bird distribution changes

This article describes the development of the China Bird Watching Database and its use to understand bird distribution changes

Giant third-order nonlinear Hall effect in misfit layer compound (SnS)1.17{1.17}(NbS2_2)3_3

Nonlinear Hall effect (NLHE) holds immense significance in recognizing the band geometry and its potential applications in current rectification. Recent discoveries have expanded the study from second-order to third-order nonlinear Hall effect (THE), which is governed by an intrinsic band geometric quantity called the Berry Connection Polarizability (BCP) tensor. Here we demonstrate a giant THE in a misfit layer compound, (SnS)${1.17}$(NbS$_2$)$_3$. While the THE is prohibited in individual NbS$_2$ and SnS due to the constraints imposed by the crystal symmetry and their band structures, a remarkable THE emerges when a superlattice is formed by introducing a monolayer of SnS. The angular-dependent THE and its scaling relationship indicate that the phenomenon could be correlated to the band geometry modulation, concurrently with the symmetry breaking. The resulting strength of THE is orders of magnitude higher compared to recent studies. Our work illuminates the modulation of structural and electronic geometries for novel quantum phenomena through interface engineering

Assessing the role of central lymph node ratio in predicting recurrence in N1a low-to-intermediate risk papillary thyroid carcinoma

IntroductionLymph node metastasis in patients with papillary thyroid carcinoma (PTC) is associated with postoperative recurrence. Recently, most studies have focused on the evaluation of recurrence in patients with late-stage PTC, with limited data on those with early-stage PTC. We aimed to assess the relationship between lymph node ratio (LNR) and recurrence in low-to-intermediate-risk patients and validate its diagnostic efficiency in both structural (STR) and biochemical recurrence (BIR).MethodsClinical data of patients with PTC diagnosed at the Affiliated Hospital of Jining Medical University were retrospectively collected. The optimal LNR cut-off values for disease-free survival (DFS) were determined using X-tile software. Predictors were validated using univariate and multivariate Cox regression analyses.ResultsLNR had a higher diagnostic effectiveness than metastatic lymph nodes in patients with low-to-intermediate recurrence risk N1a PTC. The optimal LNR cutoff values for STR and BIR were 0.75 and 0.80, respectively. Multivariate Cox regression analysis showed that LNR≥0.75 and LNR≥0.80 were independent factors for STR and BIR, respectively. The 5-year DFS was 90.5% in the high LNR (≥0.75) and 96.8% in low LNR (<0.75) groups for STR. Regarding BIR, the 5-year DFS was 75.7% in the high LNR (≥0.80) and 86.9% in low LNR (<0.80) groups. The high and low LNR survival curves exhibited significant differences on the log-rank test.ConclusionLNR was associated with recurrence in patients with low-to-intermediate recurrence risk N1a PTC. We recommend those with LNR≥0.75 require a comprehensive evaluation of lateral neck lymphadenopathy and consideration for lateral neck dissection and RAI treatment

Generative AI for Medical Imaging: extending the MONAI Framework

Recent advances in generative AI have brought incredible breakthroughs in several areas, including medical imaging. These generative models have tremendous potential not only to help safely share medical data via synthetic datasets but also to perform an array of diverse applications, such as anomaly detection, image-to-image translation, denoising, and MRI reconstruction. However, due to the complexity of these models, their implementation and reproducibility can be difficult. This complexity can hinder progress, act as a use barrier, and dissuade the comparison of new methods with existing works. In this study, we present MONAI Generative Models, a freely available open-source platform that allows researchers and developers to easily train, evaluate, and deploy generative models and related applications. Our platform reproduces state-of-art studies in a standardised way involving different architectures (such as diffusion models, autoregressive transformers, and GANs), and provides pre-trained models for the community. We have implemented these models in a generalisable fashion, illustrating that their results can be extended to 2D or 3D scenarios, including medical images with different modalities (like CT, MRI, and X-Ray data) and from different anatomical areas. Finally, we adopt a modular and extensible approach, ensuring long-term maintainability and the extension of current applications for future features