Uncontrolled economic development often leads to land degradation, a decline in ecosystem services, and negative impacts on community welfare. This study employs water yield (WY) modeling as a method for environmental management, aiming to provide a comprehensive understanding of the relationship between Land Use Land Cover (LULC), Land Use Intensity (LUI), and WY to support sustainable natural resource management in the Cisadane Watershed, Indonesia. The objectives include: (1) analyzing changes in WY for 2010, 2015, and 2021; (2) predicting WY for 2030 and 2050 under two scenarios—Business as Usual (BAU) and Protected Forest Area (PFA); (3) assessing the impacts of LULC and climate change on WY; and (4) exploring the relationship between LUI and WY. The Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model calculates actual and predicted WY conditions, while the Coupling Coordination Degree (CCD) analyzes the LULC-WY relationship. Results indicate that the annual WY in 2021 was 215.8 × 108 m³, reflecting a 30.42% increase from 2010. Predictions show an increasing trend in WY under both scenarios for 2030 and 2050 with different magnitudes. Rainfall contributes 88.99% more dominantly to WY than LULC. Additionally, around 50% of districts exhibited unbalanced coordination between LUI and WY in 2010 and 2020. This study reveals the importance of ESs in sustainable watershed management amidst increasing demand for natural resources due to population growth.

Increasing levels of everyday cycling has many benefits for both individuals and for cities. Reduced traffic congestion, improved air quality and safer spaces for all vulnerable road users are among the significant benefits for urban developments. Despite this, public opposition to cycling infrastructure is common, particularly when it involves reprioritising road space for cycles instead of vehicles. The purpose of the research was to examine various stakeholders’ perspectives on proposed cycle infrastructure projects. This study utilised an innovative data collection approach through detailed content analysis of 322 public consultation submissions on a proposed active travel scheme in Limerick City, Ireland. By categorising submissions into support, opposition, and proposals, the study reveals the nuanced public perceptions that influence behavioural adaptation and acceptance of sustainable transport infrastructure. Supportive submissions, which outnumbered opposition-related submissions by approximately 2:1, emphasised the need for dedicated cycling infrastructure, enhanced cyclist safety, and potential improvements in environmental conditions. In contrast, opposition submissions focused on concerns over car parking removal, decreased accessibility for residents, and safety issues for vulnerable populations, particularly the elderly. Proposal submissions suggested design modifications, including enhanced safety features, provisions for convenient car parking, and alternative cycle routes. This paper highlights the value of structured public consultation data in uncovering behavioural determinants and barriers to cycling infrastructure adoption, offering policymakers essential insights into managing public opposition and fostering support. The methodology demonstrates how qualitative data from consultations can be effectively used to inform policy by capturing community-specific needs and enhancing the design of sustainable urban mobility systems. These findings underscore the need for innovative, inclusive data collection methods that reveal public sentiment, facilitating evidence-based transport policies that support climate-neutral mobility.

This paper proposes a framework for highlighting the significance of cultural communication in Islamic thought, sociology, and law, areas deeply intertwined with human values. It examines how Islam presents a clear vision and noble conduct in establishing peace values and fostering principles of tolerance among all. Additionally, it explores the potential of virtual communication networks to promote a culture of peace.

A comprehensive survey was conducted in 2012 and 2020 to assess the financial culture of Hungarian higher education students. The findings revealed that financial training effectiveness had not improved over time. To address this, a conative examination of financial personality was initiated by the Financial Compass Foundation, which gathered over 40,000 responses from three distinct age groups: Children, high school students, and adults. The study identified key behavioral patterns, such as excessive spending and financial fragility, which were prominent across all age groups. These results informed Hungary’s seven-year strategy to enhance financial literacy and integrate economic education into the National Core Curriculum. The research is now expanding internationally with the aim of building a comparative database. The study’s main findings highlight the widespread need for improved financial education, with more than 80% of adults demonstrating risky financial behaviors. The implications of these findings suggest the importance of early financial education and tailored interventions to foster long-term financial stability. The international expansion of this research will allow for the examination of country-specific financial behaviors and provide data-driven recommendations for policy development.

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.

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.