This study conducted a systematic literature review on current and emerging trends in the use of artificial intelligence (AI) for community surveillance, using the PRISMA methodology and the paifal.ai tool for the selection and analysis of relevant sources. Five main thematic areas were identified: AI technologies, specific applications, societal impact, regulations and public policy. Our findings revealed exponential growth in the development and implementation of AI technologies, with applications ranging from public safety to environmental monitoring. However, this advancement poses significant challenges related to privacy, ethics and governance, driving a debate on the need for appropriate regulations. The analysis also highlighted the disparity in the adoption of these technologies among different communities, suggesting a need for inclusive policies to ensure equitable benefits. This study contributes to the understanding of the current scenario of AI in community policing, providing a solid foundation for future research and developments in the field.

New technologies always have an impact on traditional theories. Finance theories are no exception to that. In this paper, we have concentrated on the traditional investment theories in finance. The study examined five investment theories, their assumptions, and their limitation from different works of literature. The study considered Artificial Intelligence (AI) and Machine Learning (ML) as representative of financial technology (fintech) and tried to find out from the literature how these new technologies help to reduce the limitations of traditional theories. We have found that fintech does not have an equal impact on every conventional finance theory. Fintech outperforms all five traditional theories but on a different scale.

Remote sensing technologies have revolutionized forestry analysis by providing valuable information about forest ecosystems on a large scale. This review article explores the latest advancements in remote sensing tools that leverage optical, thermal, RADAR, and LiDAR data, along with state-of-the-art methods of data processing and analysis. We investigate how these tools, combined with artificial intelligence (AI) techniques and cloud-computing facilities, enhance the analytical outreach and offer new insights in the fields of remote sensing and forestry disciplines. The article aims to provide a comprehensive overview of these advancements, discuss their potential applications, and highlight the challenges and future directions. Through this examination, we demonstrate the immense potential of integrating remote sensing and AI to revolutionize forest management and conservation practices.

In April 2023, the government of Changshu City, in Jiangsu Province, China, announced that it would officially use digital Chinese Yuan (E-CNY) as a method of wage payment to the government and state-owned enterprises staff starting in May. With the gradual improvement and application of E-CNY technologies, such as no electricity, no internet payment (offline payment), and the programmability of smart contracts, E-CNY will be officially used in China. CNN said China is on the verge of a cashless society. The advantages of E-CNY have a positive role in promoting the Chinese government’s implementation of the development goals of a low-carbon and sustainable economy. However, artificial intelligence (AI) trust concerns are the primary bottleneck in the current development based on intelligent algorithms and digital information technology. AI trust concerns are affecting the scope of use of E-CNY, and it may need to achieve effective scale-use, making it promote low-carbon and sustainable development. From the industry perspective, this article selects the housing rental enterprises, which are challenging to develop and energy-intensive, to analyze the theoretical approach and practical impact of E-CNY in promoting the low-carbon and sustainable development of China’s rental housing economy. Meanwhile, from the perspective of Chinese consumers, the impact of AI trust concerns on E-CNY in promoting low-carbon and sustainable development is analyzed in this article.

The low-carbon economy is the major objective of China’s economy, and its goal is to achieve sustainable economic development. The study enriches the literature on the relationship between digital Chinese yuan (E-CNY), low-carbon economy, AI trust concerns, and security intrusion. The rapid growth of Artificial Intelligence (AI) offered more ways to achieve a low-carbon economy. The digital Chinese yuan (E-CNY), based on the AI technique, has shown its nature and valid low-carbon characteristics in pilot cities of China, it will assume important responsibilities and become the key link. However, trust concerns about AI techniques result in a limitation of the scope and extent of E-CNY usage. The study conducts in-depth research from the perspective of AI trust concerns, explores the influence of E-CNY on the low-carbon economy, and discusses the moderating and mediating mechanisms of AI trust concerns in this process. The empirical data results showed that E-CNY positively affects China’s low-carbon economy, and AI trust concerns moderate the positive impact. When consumers with higher AI trust concerns use E-CNY, their feeling of security intrusion is also higher. It affects the growth of trading volume and scope of E-CNY usage. Still, it reduces the utility of China’s low-carbon economy. This study provides valuable management inspiration for China’s low-carbon economy.

The use of artificial intelligence (AI) in the detection and diagnosis of plant diseases has gained significant interest in modern agriculture. The appeal of AI arises from its ability to rapidly and precisely analyze extensive and complex information, allowing farmers and agricultural experts to quickly identify plant diseases. The use of artificial intelligence (AI) in the detection and diagnosis of plant diseases has gained significant attention in the world of agriculture and agronomy. By harnessing the power of AI to identify and diagnose plant diseases, it is expected that farmers and agricultural experts will have improved capabilities to tackle the challenges posed by these diseases. This will lead to increased effectiveness and efficiency, ultimately resulting in higher agricultural productivity and reduced losses caused by plant diseases. The use of artificial intelligence (AI) in the detection and diagnosis of plant diseases has resulted in significant benefits in the field of agriculture. By using AI technology, farmers and agricultural professionals can quickly and accurately identify illnesses affecting their crops. This allows for the prompt adoption of appropriate preventative and corrective actions, therefore reducing losses caused by plant diseases.