This study applies machine learning methods such as Decision Tree (CART) and Random Forest to classify drought intensity based on meteorological data. The goal of the study was to evaluate the effectiveness of these methods for drought classification and their use in water resource management and agriculture. The methodology involved using two machine learning models that analyzed temperature and humidity indicators, as well as wind speed indicators. The models were trained and tested on real meteorological data to assess their accuracy and identify key factors affecting predictions. Results showed that the Random Forest model achieved the highest accuracy of 94.4% when analyzing temperature and humidity indicators, while the Decision Tree (CART) achieved an accuracy of 93.2%. When analyzing wind speed indicators, the models’ accuracies were 91.3% and 93.0%, respectively. Feature importance revealed that atmospheric pressure, temperature at 2 m, and wind speed are key factors influencing drought intensity. One of the study’s limitations was the insufficient amount of data for high drought levels (classes 4 and 5), indicating the need for further data collection. The innovation of this study lies in the integration of various meteorological parameters to build drought classification models, achieving high prediction accuracy. Unlike previous studies, our approach demonstrates that using a wide range of meteorological data can significantly improve drought classification accuracy. Significant findings include the necessity to expand the dataset and integrate additional climatic parameters to improve models and enhance their reliability.

To address the escalating online romance scams within telecom fraud, we developed an Adaptive Random Forest Light Gradient Boosting (ARFLGB)-XGBoost early warning system. Our method involves compiling detailed Online Romance Scams (ORS) incident data into a 24-variable dataset, categorized to analyze feature importance with Random Forest and LightGBM models. An innovative adaptive algorithm, the Adaptive Random Forest Light Gradient Boosting, optimizes these features for integration with XGBoost, enhancing early Online romance scams threat detection. Our model showed significant performance improvements over traditional models, with accuracy gains of 3.9%, a 12.5% increase in precision, recall improvement by 5%, an F1 score increase by 5.6%, and a 5.2% increase in Area Under the Curve (AUC). This research highlights the essential role of advanced fraud detection in preserving communication network integrity, contributing to a stable economy and public safety, with implications for policymakers and industry in advancing secure communication infrastructure.

In the face of growing competition, industrial and commercial firms need more effective strategies to gain competitive advantages. This study investigates the role of enterprise risk management (ERM) as a mediator in highlighting the significance of innovation capability on profitability in industrial and commercial firms listed on the Amman Stock Exchange (ASE). Data were collected from 244 respondents using a standardized questionnaire and analyzed with SPSS software. The results indicate that the innovation capability has an impact on profitability in industrial and commercial firms, as well as their ERM practices. Additionally, ERM mediates the relationship between innovation capability and profitability. Firms that adopt distinctive innovation strategies tend to maintain formal ERM strategies, which in turn enhance market superiority and profitability. This research offers some significant managerial ramifications that may be essential for business owners, executives, and decision-makers involved in the development of firms.

This paper foresees a critical analysis and development of a legislative proposal for the effective implementation of blockchain technology in Civil Mediation in conflicts in condominiums. This paper provides a legal analysis of personal, property rights and condominium disputes, applying blockchain technology for the purpose of self-executing civil mediation. This paper provides several solutions for conflicts in condominiums: Condominium Statute in blockchain, telematic attendance and voting systems, the self-execution of civil mediation agreements in conflicts in condominiums and Tokenization and IoT for property remote control in condominiums. The novelty of this research lies in the fact that, based on the experience of civil mediation in conflicts in condominiums, foreseen in US States and in other States such as Canada, Spain, the regulation is adapted for the correct application of blockchain technology for mediation in conflicts in condominiums.

Since the systematic approach of the processes and their interactions, the aim is to establish the configuration of a construction project for the housing of the Weenhayek indigenous people. Applied from the theoretical research of various authors on a group of methodologies, phases and tools for project management, through rational scientific methods, such as descriptive, analytical, comparative, analytical-synthetic, inductive-deductive, historical-logical, analogies, modeling, systemic-structural-functional, systematization; and empirical methods, such as interpretivism that involves inductive, qualitative, phenomenological and transversal research, and the interview technique; the way in which the implementation processes are organized, interacted and structured is established. This reveals an alternative for the detailed configuration of a construction project for Weenhayek houses, based on phases, activities, actions and work tasks with characteristics in accordance with the needs of the project.

In order to explore how hygiene factors and motivational factors indirectly affect job satisfaction through teacher self-efficacy. Based on the two factor theory and Teacher Job Satisfaction Survey (TJS), this study analyzes how hygiene factors and motivational factors indirectly affect job satisfaction through teacher self-efficacy. The study collects valid questionnaires from 120 teachers and conducts mediation analysis using structural equation modeling. From the results, teacher self-efficacy had obvious mediating effects between hygiene factors and job satisfaction (β > 0.6, P < 0.001), as well as between motivational factors and job satisfaction (β > 0.6, P < 0.001). This discovery not only provides new perspectives and strategies for improving teacher job satisfaction, but also emphasizes the importance of enhancing teacher self-efficacy in improving job satisfaction. In addition, the study provides strong empirical evidence for education management departments and school leaders to formulate more effective teacher development policies and management measures, which has positive theoretical and practical significance for improving education quality and promoting education reform.