The analysis of regional ice and snow tourist destinations under back propagation neural network.

This study aims to analyze the evolutionary characteristics and development levels of regional ice and snow tourist destinations by integrating the Back Propagation Neural Network (BPNN) within an Internet of Things (IoT) framework. Data from multiple sources are gathered through web scraping technology from various online platforms and are then subjected to cleaning, standardization, and normalization. A feature recognition model for ice and snow tourism is constructed based on a BPNN combined with a Spatial-Temporal Graph Convolutional Network (ST-GCN) algorithm.

The analysis of rural tourism image optimization under the internet of things and deep learning.

This study aims to utilize deep learning technology to optimize rural tourism image, enhance visitor experience, and promote sustainable development. By deploying sensors for real-time monitoring of the environment and visitor flow in rural scenic areas, combined with a Dense Convolutional Neural Network (DenseNet), automatic identification and analysis of rural landscapes are achieved. Using rural tourism along the Yellow River as a case study, this study constructs a tourism image evaluation and optimization model based on big data.

Evaluation of rural tourism development level using BERT-enhanced deep learning model and BP algorithm.

To address the insufficient expressive capabilities of traditional methods in assessing the development level of rural tourism, this study explores the fusion application of the Bidirectional Encoder Representations from Transformers (BERT) and the Back Propagation (BP) algorithm to enhance the accuracy and comprehensiveness of rural tourism development assessment. Firstly, this study introduces the BERT deep learning model and its applications in natural language processing, alongside the role of the BP algorithm in pattern recognition and predictive analysis. Subsequently, a framework for assessing rural tourism development levels, integrating BERT and the BP algorithm, is proposed.

Based on sustainable development of skating sports training new ecological and environmental protection measures.

In recent years, under the background of exam-oriented physical education reform, the government has issued a lot of physical education documents and policies. As a result, many schools have made fundamental changes in physical education activities. These policies are mainly aimed at getting young people to be more active and autonomous in school sports.

The analysis of ecological security and tourist satisfaction of ice-and-snow tourism under deep learning and the Internet of Things.

This paper aims to propose a prediction method based on Deep Learning (DL) and Internet of Things (IoT) technology, focusing on the ecological security and tourist satisfaction of Ice-and-Snow Tourism (IST) to solve practical problems in this field. Accurate predictions of ecological security and tourist satisfaction in IST have been achieved by collecting and analyzing environment and tourist behavior data and combining with DL models, such as convolutional and recurrent neural networks. The experimental results show that the proposed method has significant advantages in performance indicators, such as accuracy, F1 score, Mean Squared Error (MSE), and correlation coefficient.

ICT diffusion and financial development: Comparing high, middle, and low-income countries.

Given the importance of ICT diffusion in the development of the financial sector, this analysis is an effort to analyze the transmission channels between the two in high-income and middle and low-income economies over 2001-2019. We have used three variables, including the ICT index, individuals using the internet, and mobile subscribers, to represent ICT and three indices, including the financial development index, financial institution index, and financial market index, to make our results reliable and robust. We utilized a GMM method for conducting the empirical analysis.

Research on the influence of family capital on academic achievement of first-generation college students in China.

Objective: The objective of this study is to explore the influence of family capital (including family economic capital, family cultural capital and family social capital) on the academic achievement (including ability development, academic performance and self-concept) of first-generation college students.

Methods: The questionnaires are based on the CFPS (China Family Panel Studies) database and tailored to the specific circumstances. Data was collected from 1524 first-generation college students from five universities in Liaoning Province.

Interactive Design of Personalized Website Search Interface Based on Visual Communication.

Aiming at the problems of low user satisfaction and long search time in the traditional interactive design method of the personalized website search interface, a personalized website search interface interactive design method based on visual communication is proposed. Under the analysis of personalized website users' search behavior, the interactive personalized website search interface is designed through a navigation module, search module, link module, interactive layout module, and visual rendering module; in the visual rendering module, the advanced texture mapping method is used to render the personalized website search interface; on the personalized website search interface, the disturbance function is imported along the normal vector, the simplified new normal vector is intelligently calculated through the concave convex texture mapping algorithm, the normal vector is solved to generate the intersection point of the high-precision interface, the illumination brightness value of each pixel of the interface is intelligently calculated, the visual communication rendering model is constructed, and the visual communication effect of the interface is improved. The simulation results show that the website interface search time of this method is within 4.

Analysis of Logistics Linkage by Digital Twins Technology and Lightweight Deep Learning.

The present work expects to meet the personalized needs of the continuous development of various products and improve the joint operation of the intraenterprise Production and Distribution (P-D) process. Specifically, this paper studies the enterprise's P-D optimization. Firstly, the P-D linkage operation is analyzed under dynamic interference.

Characterization of an intracellular humanized single-chain antibody to matrix protein (M1) of H5N1 virus.

We developed a human intracellular antibody based on the M1 protein from avian influenza virus H5N1 (A/meerkat/Shanghai/SH-1/2012) and then characterized the properties of this antibody. The M1 protein sequence was amplified by RT-PCR using the cDNA of the H5N1 virus as a template, expressed in bacterial expression system BL21 (DE3) and purified. A human strain, high affinity, and single chain antibody (HuScFv) against M1 protein was obtained by phage antibody library screening using M1 as an antigen.

Estimation of the Spatial Suitability of Winter Tourism Destinations Based on Copula Functions.

Climate and weather are important factors that determine winter tourism destinations and snow resources and temperature affect the income of the winter tourism industry. Against the background of climate change, abnormal fluctuations in climate elements bring a series of challenges for winter tourism and cause potential losses to the tourism industry. To effectively assess and plan winter tourism destinations, this study establishes the snow abundance and meteorological suitability indices from snow resource and weather conditions to express winter tourism resources, respectively.