Multidimensional frailty as a predictor of mortality among older adults: a systematic review and meta-analysis.

Objective: This systematic review examined studies that assessed the relationship between mortality risk and multidimensional frailty. The pooled risk of mortality was estimated via a meta-analysis.

Design: A systematic review and meta-analysis.

Rigdelet neural network and improved partial reinforcement effect optimizer for music genre classification from sound spectrum images.

In this paper, a new approach has been introduced for classifying the music genres. The proposed approach involves transforming an audio signal into a unified representation known as a sound spectrum, from which texture features have been extracted using an enhanced Rigdelet Neural Network (RNN). Additionally, the RNN has been optimized using an improved version of the partial reinforcement effect optimizer (IPREO) that effectively avoids local optima and enhances the RNN's generalization capability.

The Impact of Social Support on Sleep Quality in Elderly Care Institutions in Northeast China: the Chain-Mediating Effect of Psychological Adjustment and Coping Style.

Purpose: The aim of this study was to investigate the sleep quality as well as the influence of social support on the sleep quality of elderly people in nursing homes in northeast China, and analyze the chain-mediating role of psychological adjustment and coping styles in social support and sleep quality, thereby to provide a scientific basis for the development of effective intervention measures in this direction.

Patients And Methods: This study was conducted during January-March 2023 and adopted a cluster sampling method to select 5 elderly care institutions from across the Jilin, Liaoning, and Heilongjiang provinces in Northeast China. A questionnaire survey was conducted using the Self-mate General Situation Questionnaire, Pittsburgh Sleep Quality Index, Nursing Home Adjustment Scale for the Elderly, Social Support Rating Scale, and Medical Coping Modes Questionnaire.

Financial big data management and intelligence based on computer intelligent algorithm.

With the acceleration of China's economic integration process, enterprises have gained greater advantages in the fierce market competition, and gradually formed the trend of grouping and large-scale. However, as the scale of the company increases, the establishment of a branch also causes many problems. For example, in order to obtain more benefits, the business performance of the company can generate false growth, resulting in financial and operational risks.

BGF-Net: Boundary guided filter network for medical image segmentation.

How to fuse low-level and high-level features effectively is crucial to improving the accuracy of medical image segmentation. Most CNN-based segmentation models on this topic usually adopt attention mechanisms to achieve the fusion of different level features, but they have not effectively utilized the guided information of high-level features, which is often highly beneficial to improve the performance of the segmentation model, to guide the extraction of low-level features. To address this problem, we design multiple guided modules and develop a boundary-guided filter network (BGF-Net) to obtain more accurate medical image segmentation.

The Relationship Between Smartphone Addiction and the Interpersonal Competence of Chinese Private College Students: A Moderated Mediation Model.

Purpose: As an aspect of the lives of private college students in China, interpersonal competence (IC) is important to their socialization and employment competence. This study aimed to investigate the relationship between Chinese private college students' smartphone addiction (SPA) and IC, the mediating effect of negative emotions (NE), and the moderating effect of psychological resilience (PR).

Samples And Methods: Using simple random sampling, 5473 Chinese private college students (male 74.

The Development of Response and Interference Inhibition in Children: Evidence from Serious Game Training.

A serious game titled "Crossing the Jungle" was developed in this study to train children's inhibition skills using the Stroop task. The effects of inhibitory control on children were tested by a pre-test, post-test, and one-month follow-up test. In the control groups, children were asked to play a commercial game instead.

Retinal vessel segmentation based on multi-scale feature and style transfer.

Retinal vessel segmentation is very important for diagnosing and treating certain eye diseases. Recently, many deep learning-based retinal vessel segmentation methods have been proposed; however, there are still many shortcomings (e.g.

Effectiveness of Enhanced Self-Positivity Bias Training in Mitigating Depressive Mood following Negative Life Events.

The phenomenon of self-positivity bias refers to the common tendency for individuals to perceive themselves in a more positive light than is objectively warranted. The current study seeks to investigate the impact of enhanced self-positivity bias on depressive mood resulting from negative life events. The study included two experiments, a resistance experiment (exp.

Global Value Chain Governance of the Apparel Design Industry under the Background of Global Sustainable Economic Development.

In recent years, there has been a significant rise in the importance of sustainability in business and personal life. Unsustainable techniques are used throughout the whole lifecycle of the clothing and textiles business, from the time it is made to the time it is recycled or thrown away. The garment sector and its customers must be ready for a more sustainable future as the government, corporations, and society turn their focus toward it.

Inversion of Soil Organic Matter Content Based on Improved Convolutional Neural Network.

Soil organic matter (SOM) is an important source of nutrients required during crop growth and is an important component of cultivated soil. In this paper, we studied the possibility of using deep learning methods to establish a multi-feature model to predict SOM content. Moreover, using Nong'an County of Changchun City as the study area, Sentinel-2A remote sensing images were taken as the data source to construct the dataset by using field sampling and image processing.

Construction and Application of a Piano Playing Pitch Recognition Model Based on Neural Network.

The intonation recognition of piano scores is an important problem in the field of music information retrieval. Based on the neural network theory, this study constructs a piano playing intonation recognition model and uses the optimized result as the feature of piano music to realize the prediction of the music recognition of the intonation preference. The model combines the behavioral preference relationship between intonation and musical notation to measure the similarity between intonations, which is used to calculate the similarity between intonation preference and music, and solves the quantification problem of intonation recognition.

A Multimodal Convolutional Neural Network Model for the Analysis of Music Genre on Children's Emotions Influence Intelligence.

This paper designs a multimodal convolutional neural network model for the intelligent analysis of the influence of music genres on children's emotions by constructing a multimodal convolutional neural network model and profoundly analyzing the impact of music genres on children's feelings. Considering the diversity of music genre features in the audio power spectrogram, the Mel filtering method is used in the feature extraction stage to ensure the effective retention of the genre feature attributes of the audio signal by dimensional reduction of the Mel filtered signal, deepening the differences of the extracted features between different genres, and to reduce the input size and expand the model training scale in the model input stage, the audio power spectrogram obtained by feature extraction is cut the MSCN-LSTM consists of two modules: multiscale convolutional kernel convolutional neural network and long and short term memory network. The MSCNN network is used to extract the EEG signal features, the LSTM network is used to remove the temporal characteristics of the eye-movement signal, and the feature fusion is done by feature-level fusion.

A whirlpool of emotion: How entrepreneurs' empathy affects employees' emotional exhaustion.

Drawing upon upper echelons and self-determination theories, we hypothesize and test a mediating process linking entrepreneurs' empathy to employees' emotional exhaustion and the moderating role of psychological empowerment. Based on a dyadic-survey study of entrepreneurs and their employees in high-tech new ventures in China, we conducted the empirical test by using hierarchical linear modeling (HLM) and found that entrepreneurs' empathy has a negative effect on employees' emotional exhaustion, and psychological empowerment not only partially mediates the relationship between entrepreneurs' empathy and employees' emotional exhaustion but also positively moderates the above relationship. This study frames an integrative perspective of emotions and psychologies and sheds a nuanced understanding of the mechanisms linking empathy with emotional exhaustion.

Clothing Design Style Recommendation Using Decision Tree Algorithm Combined with Deep Learning.

Most clothing recommendation methods have problems such as high resource consumption and inconsistent subjectively labeled clothing labels. Based on this, a multilabel classification algorithm based on deep learning (DL) theory is introduced, based on which the clothing style recognition model is constructed. Next, the concept of the decision tree algorithm is given, and the clothing recommendation model is built based on this algorithm.

Managing Students' Creativity in Music Education - The Mediating Role of Frustration Tolerance and Moderating Role of Emotion Regulation.

Artificial intelligence (AI) era challenges the use and functions of emotion in college students and the students' college life is often experienced as an emotional rollercoaster, negative and positive emotion can affect the emotional outcomes, but we know very little about how students can ride it most effectively to increase their creativity. We introduce frustration tolerance as a mediator and emotion regulation as a moderator to investigate the mechanism of creativity improvement under negative emotion. Drawing on a sample of 283 students from professional music colleges or music major in normal universities, we find that negative emotion are generally associated with a lower creativity, while frustration tolerance can mediate the relationship between negative emotion and creativity, but these effects depend on the emotion regulation.

Exploring the Domain of Emotional Intelligence in Organizations: Bibliometrics, Content Analyses, Framework Development, and Research Agenda.

Emotion is a kind of micro foundation that can affect human behaviors even in the digital era. Emotional intelligence (EI) is an important psychological factor that affects the growth and development of organizations from the view of emotion. Based on current bodies of literature, a comprehensive review of EI can contribute to its theory development in organization research and facilitate EI research burgeoning.

Curv-Net: Curvilinear structure segmentation network based on selective kernel and multi-Bi-ConvLSTM.

Purpose: Accurately segmenting curvilinear structures, for example, retinal blood vessels or nerve fibers, in the medical image is essential to the clinical diagnosis of many diseases. Recently, deep learning has become a popular technology to deal with the image segmentation task, and it has obtained remarkable achievement. However, the existing methods still have many problems when segmenting the curvilinear structures in medical images, such as losing the details of curvilinear structures, producing many false-positive segmentation results.

Natural Xanthine Oxidase Inhibitor 5--Caffeoylshikimic Acid Ameliorates Kidney Injury Caused by Hyperuricemia in Mice.

Xanthine oxidase (XOD) inhibition has long been considered an effective anti-hyperuricemia strategy. To identify effective natural XOD inhibitors with little side effects, we performed a XOD inhibitory assay-coupled isolation of compounds from (SGR), a traditional Chinese medicine frequently prescribed as anti-hyperuricemia agent for centuries. Through the in vitro XOD inhibitory assay, we obtained a novel XOD inhibitor, 5--caffeoylshikimic acid (#, 5OCSA) with IC of 13.

Convergence analysis of AdaBound with relaxed bound functions for non-convex optimization.

Clipping on learning rates in Adam leads to an effective stochastic algorithm-AdaBound. In spite of its effectiveness in practice, convergence analysis of AdaBound has not been fully explored, especially for non-convex optimization. To this end, we address the convergence of the last individual output of AdaBound for non-convex stochastic optimization problems, which is called individual convergence.

Adaptive Attention Memory Graph Convolutional Networks for Skeleton-Based Action Recognition.

Graph Convolutional Networks (GCNs) have attracted a lot of attention and shown remarkable performance for action recognition in recent years. For improving the recognition accuracy, how to build graph structure adaptively, select key frames and extract discriminative features are the key problems of this kind of method. In this work, we propose a novel Adaptive Attention Memory Graph Convolutional Networks (AAM-GCN) for human action recognition using skeleton data.

A Lightweight Fusion Distillation Network for Image Deblurring and Deraining.

Recently, deep learning-based image deblurring and deraining have been well developed. However, most of these methods fail to distill the useful features. What is more, exploiting the detailed image features in a deep learning framework always requires a mass of parameters, which inevitably makes the network suffer from a high computational burden.