This study systemically examines the numerous impacts of climate change on agriculture in Tunisia. In this study, we establish an empirical and comprehensive methodology to assess the effects of climate changes on Tunisian agriculture by investigating current climatic patterns using crop yields and socioeconomic variables. The study also assesses the types of adaptation strategies agriculture uses in Tunisia and explores their effectiveness in coping with climate-related adversities. We also consider some resilience factors, namely the ecological aspect and economic and social camouflage pursued by the (very) men in Tunisian agriculture. We also extensively discuss the complex interconnected relationship between policy interventions and community-based adaptations, a crucial part of the ongoing debate on climate change adaptation and resilience in agriculture. The findings of this study contribute to this important conversation, particularly for areas facing similar challenges.

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.

The target area of the survey is the rehabilitated flat area behind the capital cities of Vienna and Bratislava, which lies in the tourist area of Győr. Wetlands provide a backdrop for tourism products such as kite flying, cycling and walking. The city centre offers tourists an easy sightseeing tour behind the natural scenery of the Danube tributary (Szigetköz). Objective: The demographic characteristics of demand and preferences for active tourism product types and the extent of the scope of supply were analyzed. The present research also analyses the cycling routes in the region with regard to the EUROVELO 6 road network. The primary research was a quantitative (questionnaire) survey conducted between 10 September 2023 and 30 October 2023. The survey sample of 666 respondents is not representative and was selected by random sampling. The results of the research include an analysis of the demand for participation in cycling tourism and tour programs as activities requiring activity. The findings of the research provide a basis for demand-supply segmentation of sustainable active tourism product development based on physical experience according to demographic characteristics (e.g. age, education). The landscape of the wetland can be positioned for the bicycle tourists. Especially for the target group of people over 40 and for people with higher education. The scope of the guided tours, linked to the central offer, extends over an area of more than 50 km. Activating the target group helps the rehabilitated natural scenery to connect to sustainable tourism.

This research evaluates the regionalization of tourism in Hungary, revealing the breakdown of the national gross domestic product (GDP) of tourism. It also explores the density, spatial variations, and features of these indicators. A multimodal approach is used to evaluate the competitiveness of Hungarian counties, and the distribution of these tourism regions is analyzed using the tourism penetration index. Furthermore, regional GDP is calculated for the whole territory of Hungary. The study identifies significant regional disparities in tourism competitiveness, highlighting Budapest-Central Danube as the most competitive region and Lake Balaton as underperforming despite its potential. The research contributes by providing a detailed regional GDP analysis and emphasizing the need for targeted policy interventions to enhance tourism development across all regions.

This study explored the competencies required for informal community leaders to effectively promote health within Thai communities, employing an exploratory sequential mixed-methods design. The qualitative phase, comprising in-depth interviews with thirteen community leaders, identified four critical domains of competency: basic health knowledge, communication skills, network building, and cultural awareness. These domains were subsequently validated through second-order confirmatory factor analysis, which confirmed their reliability and construct validity. The findings highlighted the pivotal role of these competencies in enabling community-led health promotion initiatives. This research provides a robust, evidence-based framework to inform the development of training programs, policy strategies, and targeted interventions aimed at enhancing health outcomes within Thai communities.

This paper analyzes the characteristics and influence mechanisms of financial support for China’s strategic emerging industries. Using a sample of 356 listed companies across nine major industries, we conduct an in-depth analysis of the efficiency of financial support and its influencing factors. In addition, this paper analyzes the influence mechanism of financial support for strategic emerging industries based on the relevant theory of financial support for industry development. It clarifies the internal and external influencing factors. Based on the theoretical analysis, a two-stage empirical investigation was conducted: The data of 356 listed companies in strategic emerging industries from 2010 to 2022 were selected as a sample, and the data envelopment analysis (DEA) method was applied to measure efficiency. The influencing factors were then analyzed using a Tobit regression and an intermediate effects test.