The area of lake surface water is shrinking rapidly in Central Asia. We explore anthropogenic and climate factors driving this trend in Shalkar Lake, located in the Aral Sea region in Kazakhstan, Central Asia. We employ the Landsat satellite archive to map interannual changes in surface water between 1986 and 2021. The high temporal resolution of our dataset allows us to analyze the water surface data to investigate the time series of surface water change, economic and agricultural activities, and climate drivers like precipitation, evaporation, and air temperature. Toward this end, we utilize dynamic linear models (DLM). Our findings suggest that the shrinking of Shalkar Lake does not exhibit a systemic trend that could be associated with climate factors. Our empirical analysis, adopted to address local conditions, reveals that water reduction in the area is related to human interventions, particularly agricultural activities during the research period. On the other hand, the retrospectively fitted values indicate a semi-regular periodicity despite anthropogenic factors. Our results demonstrate that climate factors still play an essential role and should not be disregarded. Additionally, considering long-term climate projections in environmental impact assessment is crucial. The projected increase in temperatures and the corresponding decline in lake size highlights the need for proactive measures in managing water resources under changing climatic conditions.

This research focuses on the construction of the competency of “Double-qualified” teachers in higher vocational colleges. Through comprehensive literature analysis, in-depth interviews and questionnaire surveys, a competency model covering three dimensions, namely personality charm, teaching literacy and practical skills, has been successfully established. This model provides a scientific basis for higher vocational colleges in teacher selection, performance evaluation and professional training, and particularly emphasizes the importance of teachers’ cultivation of students’ practical abilities and professional qualities in the context of vocational education. The research reveals that these three competency dimensions are interdependent and jointly influence teachers’ educational and teaching achievements as well as students’ career development.

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.

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.

This paper aims to show the crisis of contemporary criminal systems, however legislative excess of stipulating the penalty of imprisonment, as a penalty depriving freedom, while sometimes stipulating the penalty of imprisonment is mandatory, rather combining it with other penalties, and more than that, depriving the judge of his discretionary power in determining the punishment, this threatens the theory of individualized punishment in a fatal way, so as a result, prisons are overcrowded with inmates, which places a heavy burden on the state from an economic perspective that exhausts and drains its budget, while there is also a social cost of the prison sentence, paid by the prisoner’s family and close circle, moreover the greatest cost is the failure of the penal system to perform its role towards the prisoner by reforming and rehabilitating, therefore, this paper focuses on presenting the causes of the problem and its negative repercussions, trying to find some solutions, by presenting alternatives to the prison sentence, while expanding the view to include some criminal systems, such as the Islamic criminal system and its decision on the penalty of exile.