In the 1960s, many East Asian Tiger countries, such as Taiwan, Malaysia, China, etc., faced unfavourable socio-economic conditions like those facing many African countries such as Rwanda. For example, in the 1960s, Singapore was classified as an undeveloped country, with its GDP per capita standing at less than $320. However, all these countries managed to reach a development level like that of Western and North American countries (such as the U.S. and Canada), within a space of less than 15 years. Today, Singapore’s GDP per capita has risen to an incredible $84,501, making it the sixth highest GDP per capita in the world. With Vision 2020, President Kagame promised that Rwanda, which he referred to as “the African lion”, would cruise faster to the middle-income country status faster than Singapore and other middle income status countries, most of which are in East-Asia in less than 20 years. Thirteen years after the introduction of Rwanda’s Vision 2020, President Kagame dashed off the Wall Street Journal and other western media which often referred to Rwanda’s economic development as “the economic tiger of Africa” in his 2013 famous statement as follows: “There is a view that development is a marathon, not a sprint. We do not agree. Development is a marathon that must be run at a sprint. In our pursuit of progress, we have, of course, looked to East Asia’s so-called “tiger” economies for inspiration. But Africa’s experience is unique, and we must now define our own destiny. So, while being described as an “African tiger” is a welcome recognition of how far Rwanda has come, perhaps it isn’t quite right. After all, our continent has its own big cat. Step forward, the new lions of Africa.”. To achieve this objective between 2000 and 2020, Rwanda’s GDP per capita needed to increase from 254.94 USD (2000) to $1240 (in 2020), and the GDP growth rate was to grow consistently between 7% and 10% for a period of 20 years (2000–2020). The objective of this article is to critically analyse how the Government of Rwanda failed to achieve its promise of achieving a middle-income status (MICS) between 2000 and 2020. In 2000 the Rwandan president Paul Kagame launched Rwanda’s Vision 2020. This was a long-term framework for Rwanda’s development in 20 (i.e., 2000–2020). The idea behind Rwanda’s leadership was to imitate whatever Singapore did to become a developed country in just 15 years after its independence in 1965. Blatantly stated, President Paul Kagame wanted to turn his country into the Singapore of Africa. The IMF states that “Vision 2020 was the longer-term socio-economic development framework that sought to transform Rwanda into a middle-income country by 2020”. There is no doubt that Rwanda has made some developmental strides through the implementation of Vision 2020 as an instrument for operationalisation for the Singaporean-modelled developmental state over the past 20 years and has succeeded in some areas. However, the Government of Rwanda has failed in many areas and has failed to achieve the prestigious MICS in 2020. Therefore, among the many gaps in the current literature that this research seeks close is to find out what were some of the accomplishments that Rwanda’s Vision 2020 was unable to achieve? Why did the Government of Rwanda fail to achieve middle-income status within 20 years? What insights can other developing countries gain from Rwanda’s Vision 2020 experience? This study is a significant contribution to the current theoretical knowledge as it sheds light on the reasons behind the Government of Rwanda’s failure to achieve middle-income country status in 2020, a topic that has been largely ignored by the Government’s documents and the media. Despite Rwanda’s Vision 2020, there has been no scientific study to date that has attempted to deal with this topic. Therefore, this study, titled “Why the Government of Rwanda failed to achieve middle-income status (MICS) in 2020” is likely the first of its kind. By addressing

In this paper, the YOLOv9 algorithm is used to realize the real-time positioning detection of electrical connectors. Firstly, the camera was used to collect images of the electrical connectors that needed to be detected, and the collected pictures were classified into data sets. Meanwhile, the electrical connectors were classified and labelimg was used to label the category boundary box. Then the labeled data sets were put into the YOLOv9 model for training. According to the trained electrical connector detection model, the detection and identification of electrical connector are realized.

With the rapid development of digital technology, a new social culture has emerged between new media and traditional media. Digital technologies are gradually being integrated into the media industry, significantly enhancing the dissemination efficiency of traditional media programs and providing strong support for the modernization of new media. In this context, media professionals need to deeply understand the impact of social communication on traditional media. This understanding is essential to explore new work models aimed at meeting audience needs, maximizing the advantages of social communication, injecting new vitality into the dissemination of broadcast and television programs, and promoting the sustained and healthy development of traditional media.

In recent years, building collapse accidents have occurred from time to time in China, causing serious casualties and property losses. In order to monitor the abnormal phenomena of buildings in time, Intelligent Building Safety Monitoring System based on computer vision is proposed. Using ZigBee wireless sensor network, the crack images and sensor monitoring data of monitoring points are obtained and transmitted to the local control computer. After the data are processed by the integrated crack image recognition and measurement algorithm based on OpenCV, they are displayed on the terminal monitoring software. If the data is abnormal, the early warning function is triggered, and the intelligent monitoring and danger early warning functions for building safety are realized.