Cities play a key role in achieving the climate-neutral supply of heating and cooling. This paper compares the policy frameworks as well as practical implementation of smart heating and cooling in six cities: Munich, Dresden and Bad Nauheim in Germany; and Jinan, Chengdu and Haiyan in China, to explore strategies to enhance policy support, financial mechanisms, and consumer engagement, ultimately aiming to facilitate the transition to climate-neutral heating and cooling systems. The study is divided into three parts: (i) an examination of smart heating and cooling policy frameworks in Germany and China over the past few years; (ii) an analysis of heating and cooling strategies in the six case study cities within the context of smart energy systems; and (iii) an exploration of the practical solutions adopted by these cities as part of their smart energy transition initiatives. The findings reveal differences between the two countries in the strategies and regulations adopted by municipal governments as well as variations within each country. The policy frameworks and priorities set by city governments can greatly influence the development and implementation of smart heating and cooling systems. The study found that all six cities are actively engaged in pioneering innovative heating and cooling projects which utilise diverse energy sources such as geothermal, biomass, solar, waste heat and nuclear energy. Even the smaller cities were seen to be making considerable progress in the adoption of smart solutions.

Introduction: With the adoption of the rural rehabilitation strategy in recent years, China’s rural tourist industry has entered a golden age of growth. Due to the lack of management and decision-support systems, many rural tourist attractions in China experience a “tourist overload” problem during minor holidays or Golden Week, an extended vacation of seven or more consecutive days in mainland China formed by transferring holidays during a specific holiday period. This poses a severe challenge to tourist attractions and relevant management departments. Objective: This study aims to summarize the elements influencing passenger flow by examining the features of rural tourist attractions outside China’s largest cities. Additionally, the study will investigate the variations in the flow of tourists. Method: Grey Model (1,1) is a first-order, single-variable differential equation model used for forecasting trends in data with exponential growth or decline, particularly when dealing with small and incomplete datasets. Four prediction algorithms—the conventional GM(1,1) model, residual time series GM(1,1) model, single-element input BP neural network model, and multi-element input BP network model—were used to anticipate and assess the passenger flow of scenic sites. Result: The multi-input BP neural network model and residual time series GM(1,1) model have significantly higher prediction accuracy than the conventional GM(1,1) model and unit-input BP neural network model. A multi-input BP neural network model and the residual time series GM(1,1) model were used in tandem to develop a short-term passenger flow warning model for rural tourism in China’s outskirts. Conclusion: This model can guide tourists to staggered trips and alleviate the problem of uneven allocation of tourism resources.

Accurate prediction of US Treasury bond yields is crucial for investment strategies and economic policymaking. This paper explores the application of advanced machine learning techniques, specifically Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) models, in forecasting these yields. By integrating key economic indicators and policy changes, our approach seeks to enhance the precision of yield predictions. Our study demonstrates the superiority of LSTM models over traditional RNNs in capturing the temporal dependencies and complexities inherent in financial data. The inclusion of macroeconomic and policy variables significantly improves the models’ predictive accuracy. This research underscores a pioneering movement for the legacy banking industry to adopt artificial intelligence (AI) in financial market prediction. In addition to considering the conventional economic indicator that drives the fluctuation of the bond market, this paper also optimizes the LSTM to handle situations when rate hike expectations have already been priced-in by market sentiment.

This study examines the intricate interplay between the digital environment and the evolving communication dynamics of Generation Z, specifically focusing on the impact of social media on familial bonds. The research objective is to explore the ways in which Generation Z’s social media consumption patterns shape their relationships and lives, providing insights into the intricate interplay between technology and human connections. Adopting Hirschi and Wellman’s theoretical framework, this investigation employs a survey method, utilizing a questionnaire to gather data from 384 Iranian Generation Z social media users. The findings reveal a significant and negative correlation between family bonds and social media usage, dependency on the platform, and support received from it. Excessive use diminishes interaction and intimacy, highlighting social media’s potential consequences for family relationships, which are crucial for individual and societal well-being. The study underscores the significance of balanced social media usage and encourages initiatives promoting face-to-face interactions, empathy, and responsible digital citizenship. The findings hold significant implications for academics and policymakers in developing strategies that promote responsible digital habits, foster healthy relationships, and contribute to digital citizenship advancement. This may involve regulatory initiatives, guidelines for social media platforms, and public awareness campaigns emphasizing the importance of balanced digital habits.

Imagining people’s functions in everyday life and work without the use of ICT, seems difficult. Their application is ubiquitous everywhere, regardless of which aspect it is viewed from, because it has a strong function in ensuring the competitiveness of various systems at the micro and macro levels. Numerous national and multinational strategies try to encourage educational systems to put a greater focus on ICT to more efficiently acquire skills, competencies, and knowledge, which should represent added value to all generations in the future. This article analyzes the progress of the ICT development index (IDI) in Scandinavian countries by comparing these countries in the European region. It is known that the Scandinavian countries belong to that part of the countries that have recognized the importance of involving ICT in education programs, which improves the economy of a certain country. Given this, the research reveals how ICTs play a key role in improving socio-economic development in Scandinavian countries.