Through three development routes of authentication, communication, and computing, the Internet of Things (IoT) has become a variety of innovative integrated solutions for specific applications. However, due to the openness, extensiveness and resource constraints of IoT, each layer of the three-tier IoT architecture suffers from a variety of security threats. In this work, we systematically review the particularity and complexity of IoT security protection, and then find that Artificial Intelligence (AI) methods such as Machine Learning (ML) and Deep Learning (DL) can provide new powerful capabilities to meet the security requirements of IoT. We analyze the technical feasibility of AI in solving IoT security problems and summarize a general process of AI solutions for IoT security. For four serious IoT security threats: device authentication, Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks defense, intrusion detection and malware detection, we summarize representative AI solutions and compare the different algorithms and technologies used by various solutions. It should be noted that although AI provides many new capabilities for the security protection of IoT, it also brings new potential challenges and possible negative effects to IoT in terms of data, algorithm and architecture. In the future, how to solve these challenges can serve as potential research directions.

This article investigates the potential for carbon reduction in urban parks in Shangqiu City using high-resolution remote sensing imagery. The aim is to guide modern urban carbon neutrality strategies. The carbon-saving potential is estimated based on the mitigation of the urban heat island effect by park greenery, which reduces energy consumption. Then, the sample parks were divided into different categories, and 16 landscape metrics were selected to analyze their relationship with carbon-saving potential and driving factors. We found that a total of 300.57 t CO₂ could be reduced in Shangqiu City parks, and on average, a park could reduce 2.55 ± 0.31 t CO₂ (1.79 ± 0.29 t CO₂ ha⁻¹) per summer day. The significant effect of landscape patterns on park carbon-saving differs between park categories, which means that park carbon-saving enhancement strategies need to be different for different park categories. Meanwhile, this study implies that the landscape pattern can be designed to enhance the carbon-saving potential of urban parks, which can play a great role in promoting the process of carbon neutrality and mitigating climate change in China.

This article investigates the potential for carbon reduction in urban parks in Shangqiu City using high-resolution remote sensing imagery. The aim is to guide modern urban carbon neutrality strategies. The carbon reduction potential is estimated based on the mitigation of the urban heat island effect by park greenery, which reduces energy consumption. The parks are regarded as cool island centers, and 100 cooling gradients are gradually formed outward until the temperature reaches that of impermeable urban surfaces. The energy saved by the parks in mitigating the heat island effect is statistically calculated as the carbon reduction potential of urban park greenery. Additionally, this article classifies the sample parks into different categories and selects 26 landscape metrics to analyze their relationship with carbon-saving potential and driving factors.

Grounded in the cognition–emotion–intention theoretical framework, this study develops a model to examine the interplay among perceived value, risk perception, and visit intention. Utilizing survey data from 314 Chinese silver tourists aged 50 and above, we employ an Ordered Probit model and a moderating effect model to analyze whether and how perceived value influences the intention to visit R country, and to assess the moderating role of risk perception. The findings reveal that perceived information value, emotional value, and social value significantly enhance the intention of silver tourists to visit R country. Furthermore, risk perception not only directly diminishes visit intention but also negatively moderates the positive effects of perceived value on intention. These results suggest that tapping into the market potential of silver tourism, enhancing perceived value, and mitigating risk perception are critical strategies for fostering the growth and vitality of R country's tourism economy.