The Influence of Emotional State on Mobile Phone Addiction Tendency in College Students: The Mediation Role of Regulatory Emotion Self-efficacy

In order to explore the situation of college students’ emotional state, regulatory emotion self-efficacy and mobile phone addiction tendency as well as their relationships, a total of 350 college students were assessed with Mobile Phone Addiction Tendency Scale (MPATS), Positive Affect and Negative Affect Scale (PANAS) and the Scale of Regulatory Emotional Self-efficacy (SRESE). The result showed that: (1) 40.86% of college students had the tendency of cell phone addiction, which was serious; 72.0% of college students were in a positive emotional state, 22% were in a negative emotional state; college students' regulatory emotion self-efficacy was in the middle level; (2) there was no gender, grade, major type, household registration type (rural and urban) and whether only child difference in mobile phone addiction tendency; (3) the positive emotions of college students were negatively correlated with the tendency of mobile phone addiction, while the negative emotions were positively correlated with the tendency of mobile phone addiction, and the positive emotion was positively correlated with regulatory emotional self-efficacy；(4) the regulatory emotion self-efficacy had a partial mediating effect between the positive emotions and mobile phone addiction tendency and had no mediating effect between the negative emotions and mobile phone addiction tendency.

Multibeam fluorescence diffuse optical tomography using upconverting nanoparticles.

Fluorescence diffuse optical tomography (FDOT) is a biomedical imaging modality that can be used for localization and quantification of fluorescent molecules inside turbid media. In this ill-posed problem, the reconstruction quality is directly determined by the amount and quality of the information obtained from the boundary measurements. Regularly, more information can be obtained by increasing the number of excitation positions in an FDOT system. However, the maximum number of excitation positions is limited by the finite size of the excitation beam. In the present work, we demonstrate a method in FDOT to exploit the unique nonlinear power dependence of upconverting nanoparticles to further increase the amount of information in a raster-scanning setup by including excitation with two beams simultaneously. We show that the additional information can be used to obtain more accurate reconstructions

Quantum Yield Characterization and Excitation Scheme Optimization of Upconverting Nanoparticles

Upconverting nanoparticles suffer from low quantum yield in diffuse optical imaging, especially at low excitation intensities. Here, the power density dependent quantum yield is characterized, and the excitation scheme is optimized based on such characterizatio

Balancing power density based quantum yield characterization of upconverting nanoparticles for arbitrary excitation intensities.

Upconverting nanoparticles (UCNPs) have recently shown great potential as contrast agents in biological applications. In developing different UCNPs, the characterization of their quantum yield (QY) is a crucial issue, as the typically drastic decrease in QY for low excitation power densities can either impose a severe limitation or provide an opportunity in many applications. The power density dependence of the QY is governed by the competition between the energy transfer upconversion (ETU) rate and the linear decay rate in the depopulation of the intermediate state of the involved activator in the upconversion process. Here we show that the QYs of Yb(3+) sensitized two-photon upconversion emissions can be well characterized by the balancing power density, at which the ETU rate and the linear decay rate have equal contributions, and its corresponding QY. The results in this paper provide a method to fully describe the QY of upconverting nanoparticles for arbitrary excitation power densities, and is a fast and simple approach for assessing the applicability of UCNPs from the perspective of energy conversion

Evaluation Kidney Layer Segmentation on Whole Slide Imaging using Convolutional Neural Networks and Transformers

The segmentation of kidney layer structures, including cortex, outer stripe, inner stripe, and inner medulla within human kidney whole slide images (WSI) plays an essential role in automated image analysis in renal pathology. However, the current manual segmentation process proves labor-intensive and infeasible for handling the extensive digital pathology images encountered at a large scale. In response, the realm of digital renal pathology has seen the emergence of deep learning-based methodologies. However, very few, if any, deep learning based approaches have been applied to kidney layer structure segmentation. Addressing this gap, this paper assesses the feasibility of performing deep learning based approaches on kidney layer structure segmetnation. This study employs the representative convolutional neural network (CNN) and Transformer segmentation approaches, including Swin-Unet, Medical-Transformer, TransUNet, U-Net, PSPNet, and DeepLabv3+. We quantitatively evaluated six prevalent deep learning models on renal cortex layer segmentation using mice kidney WSIs. The empirical results stemming from our approach exhibit compelling advancements, as evidenced by a decent Mean Intersection over Union (mIoU) index. The results demonstrate that Transformer models generally outperform CNN-based models. By enabling a quantitative evaluation of renal cortical structures, deep learning approaches are promising to empower these medical professionals to make more informed kidney layer segmentation