The reference urban plan is an urban planning tool often used to orient the development of Chadian cities. However, expanding Chadian urban centers, such as Sarh, face challenges in implementing urban planning orientations of their urban plans within the set deadlines. The objective of this study is to identify the factors impeding the effective implementation of the reference urban plan for Sarh town. The methodology employed encompasses a literature review, individual interviews with urban planning experts, geographic information system (GIS) data, household surveys and statistical analysis. The results revealed that less than a quarter (19.72%) of the households surveyed were aware of the reference urban plan. The applied logistic regression model identified age, occupation and level of education as the main factors influencing public participation in the preparation of the reference urban plan. On average, 33.33% of the urban planning guidelines and 21.74% of the projected urban projects were implemented, with a difference of 1631.28 hectares (ha) between the projected plan and the actual plan for the town. Five factors were identified as contributing to the failure to implement the reference urban plan for Sarh town, including low funding, inadequate land management, a lack of political will, weak governance and poor communication. Consequently, participatory and inclusive planning approaches, effective financial mobilisation, strong governance, and the use of modern technologies such as GIS tools are recommended to enhance the implementation of urban planning tools.

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.

Recognizing the importance of competition analysis in telecommunications markets is essential to improve conditions for users and companies. Several indices in the literature assess competition in these markets, mainly through company concentration. Artificial Intelligence (AI) emerges as an effective solution to process large volumes of data and manually detect patterns that are difficult to identify. This article presents an AI model based on the LINDA indicator to predict whether oligopolies exist. The objective is to offer a valuable tool for analysts and professionals in the sector. The model uses the traffic produced, the reported revenues, and the number of users as input variables. As output parameters of the model, the LINDA index is obtained according to the information reported by the operators, the prediction using Long-Short Term Memory (LSTM) for the input variables, and finally, the prediction of the LINDA index according to the prediction obtained by the LSTM model. The obtained Mean Absolute Percentage Error (MAPE) levels indicate that the proposed strategy can be an effective tool for forecasting the dynamic fluctuations of the communications market.

The growth of buildings in big cities necessitates Design Review (DR) to ensure good urban planning. Design Review involves the city community in various forms; however, community participation remains very limited or even non-existent. There are indications that the community has not been involved in the Design Review process. Currently, DR tends to involve only experts and local government, without including the community. Therefore, this research aimed to analyze the extent of opportunities for community participation by exploring DR analysis in developed countries and related policies. In-depth interviews were also carried out with experts and Jakarta was selected as a case study since the city possessed the most intensive development. The results showed that the implementation of DR did not consider community participation. A constructivist paradigm was also applied with qualitative interpretive method by interpreting DR data and community participation. The strategy selected was a case study and library research adopted by examining theories from related literature. Additionally, the data was collected by reconstructing different sources such as books, journals, existing research, and secondary data from related agencies. Content and descriptive analysis methods were also used, where literature obtained from various references was analyzed to support research propositions and ideas.

The idea of emotions that is concealed in human language gives rise to metaphor. It is challenging to compute and develop a framework for emotions in people because of its detachment and diversity. Nonetheless, machine translation heavily relies on the modeling and computation of emotions. When emotion metaphors are calculated into machine translation, the language is significantly more colorful and satisfies translating criteria such as truthfulness, creativity and beauty. Emotional metaphor computation often uses artificial intelligence (AI) and the detection of patterns and it needs massive, superior samples in the emotion metaphor collection. To facilitate data-driven emotion metaphor processing through machine translation, the study constructs a bi-lingual database in both Chinese and English that contains extensive emotion metaphors. The fundamental steps involved in generating the emotion metaphor collection are demonstrated, comprising the basis of theory, design concepts, acquiring data, annotating information and index management. This study examines how well the emotion metaphor corpus functions in machine translation by proposing and testing a novel earthworm swarm-tunsed recurrent network (ES-RN) architecture in a Python tool. Additionally, the comparison study is carried out using machine translation datasets that already exist. The findings of this study demonstrated that emotion metaphors might be expressed in machine translation using the emotion metaphor database developed in this research.