In agriculture, crop yield and quality are critical for global food supply and human survival. Challenges such as plant leaf diseases necessitate a fast, automatic, economical, and accurate method. This paper utilizes deep learning, transfer learning, and specific feature learning modules (CBAM, Inception-ResNet) for their outstanding performance in image processing and classification. The ResNet model, pretrained on ImageNet, serves as the cornerstone, with introduced feature learning modules in our IRCResNet model. Experimental results show our model achieves an average prediction accuracy of 96.8574% on public datasets, thoroughly validating our approach and significantly enhancing plant leaf disease identification.

The article presents the experience of formation and development of economic competences of non-economic specialty students. The modern world is quite complex, diverse, and multidimensional, in order to adapt to it, work effectively, it is necessary to have information about market relations, relations in the sphere of production, consumption, exchange, distribution, and also to be able to connect these areas, navigate the laws operating in these areas. It should be noted that the formation and development of a specialist’s economic competence occurs throughout his or her entire professional life. In our study, the process of forming economic competence is considered as its formation at the stage of mastering economic disciplines, relevant special courses and methodical support. Training in higher education should lead to the acquired knowledge being transferred into the activity of combining elements into an interconnected structure, into the skillful distribution of resources, into the activity that brings profit and has the form of capital investment, in other words, the individual, acquiring knowledge for himself, should be able to transform it into a socially significant value. This requires the search for and implementation of new approaches in the content and organization of the educational process at all levels of education. Research devoted to the role of education in the preparation of future non-economists for economic competence focuses on the preparation of an individual for the economic literacy of an entrepreneur. One of the main tasks of the education system should be preparation for successful socialization in the context of involvement in entrepreneurial relations. It is students and young specialists who have advantages in entrepreneurship in the current conditions: they have the opportunity to obtain specialized knowledge and skills in the field of economics; they can start their own business, relying on economic knowledge. Therefore, the role of higher education is increasing, since it helps to meet the needs of society and implement its socially significant goals. This poses new challenges for universities to transfer the necessary economic knowledge, skills and abilities to students, and to develop their economic competence. The development of basic economic competences in a student is a guarantee of his competitiveness in the labor market and the basis for making reasonable economic decisions in the daily life of every person.

Currently, no academic work examines the history of the legality of roads in Chile during its independent existence as a sovereign country. Addressing this gap in the literature, this paper focuses specially on the period from 1842 to 1969, when different actors articulated a set of guiding ideas about the duties of the state and the legal powers of the administrative authority in terms of planning, construction and management of road infrastructure that would allow connectivity between population centers and across regions, according to the ideas and resources available at their historical time. This historical overview of Chilean “road law” is done in the light of insights and questions of contemporary intellectual history and institutional history. In this regard, it is argued that the evolution of road infrastructure norms and institutions during the period under study can be divided into three historical regimes, based on their fundamental legislative milestones, guiding ideas, institutional settings, and strategies of state action: from 1842 to 1887, a period of a decentralized “minimal road state” with precarious roads characterized by both material and juridical uncertainty; from 1887 to 1920, the emergence of a “proto-developmentalist road state” intent on strengthening its grip on the nationwide road infrastructure; and from 1920 to 1969, a period of a “techno-developmentalist road state” that created a nationwide paved road network for the new technology of mobile vehicles.

The telecommunications services market faces essential challenges in an increasingly flexible and customer-adaptable environment. Research has highlighted that the monopolization of the spectrum by one operator reduces competition and negatively impacts users and the general dynamics of the sector. This article aims to present a proposal to predict the number of users, the level of traffic, and the operators’ income in the telecommunications market using artificial intelligence. Deep Learning (DL) is implemented through a Long-Short Term Memory (LSTM) as a prediction technique. The database used corresponds to the users, revenues, and traffic of 15 network operators obtained from the Communications Regulation Commission of the Republic of Colombia. The ability of LSTMs to handle temporal sequences, long-term dependencies, adaptability to changes, and complex data management makes them an excellent strategy for predicting and forecasting the telecom market. Various works involve LSTM and telecommunications. However, many questions remain in prediction. Various strategies can be proposed, and continued research should focus on providing cognitive engines to address further challenges. MATLAB is used for the design and subsequent implementation. The low Root Mean Squared Error (RMSE) values and the acceptable levels of Mean Absolute Percentage Error (MAPE), especially in an environment characterized by high variability in the number of users, support the conclusion that the implemented model exhibits excellent performance in terms of precision in the prediction process in both open-loop and closed-loop.

Our intention in assembling this special issue of the Journal of Infrastructure, Policy and Development is to offer a state-of-the-art tour through the political economy issues associated with the provision of public infrastructure, and with the use of Public-Private Partnerships (PPPs) in particular. Anyone who is familiar with PPPs cannot fail to be impressed by the diversity of positions and claims regarding their properties. Some scholars maintain that PPPs are an efficient tool to enhance productivity due to their ability to manage demand-side risk. In contrast, other scholars see in PPPs a scheme whereby the public assumes the risk while the private partner takes the profit.