An exhaustive analysis and evaluation of fertility indicators in a society including many ethnic groups might provide valuable insights into any discrepancies. This study aims to systematically analyse the fertility rates over specific periods and investigate the differences in levels and patterns between local and expatriate women in Saudi Arabia using the existing data. This analysis used data from credible sources published by the General Authority for Statistics in the Saudi census 2022. The calculation of period fertility indicators started with the most straightforward rates and advanced to more complex ones, followed by a comprehensive description of the advantages and disadvantages of each. The aim was to ascertain fluctuations in fertility rates and analyse temporal patterns. Multiple studies consistently show that the fertility rate among expats in Saudi Arabia is lower than that among Saudi native women. However, the reason for this discrepancy still needs to be discovered since the definitive effect of contraceptive techniques has yet to be confirmed. Moreover, the reproductive trends that have occurred since the early 1980s will persist, although with additional precautions in place.

One crucial metric for estimating a reservoirs and dam’s lifespan is sedimentation. It is dependent upon sediment output, which in turn is dependent upon soil erosion. The study area, the Aguat Wuha Dam, was located in Simada woreda, of northwestern parts of Ethiopia. And the study's goal was to use Arc GIS and RUSLE adjusted to Ethiopian conditions to assess potential soil erosion and sediment output from the watershed and identify hotspot locations for appropriate planning for erosion and sedimentation problem management techniques to make the outputs of the dam project more productive and effective for the proposed and suggested purpose of the dam. To predict the geographical patterns of soil erosion in the watershed, the Geographic Information System (GIS) was combined with the revised universal soil loss equation (RUSLE). A soil erosion map was produced using ArcGIS by utilizing all of the model's parameters, including Erosivity, erodibility, steepness, land use, land cover, and supportive practice factors. The watershed's yearly soil loss varies from 0 to 413.86 tons/ha. In order to determine the erosion hotspot area, the average annual soil loss value was discovered to be 9.24 tons/ha/year and was categorized into six erosion severity classes: low, moderate, high, very high, severe, and very severe. These findings indicated that 162.57 ha and 699.17 ha of the watershed were considered to be extremely and severely vulnerable to soil erosion, respectively. It was discovered that the anticipated sediment yield supplied to the outlet varied from 0 to 104.94 tons/ha/year. By standing from the implications of the assessments of the geological, geotechnical, topographical, and socioenvironmental considerations Watershed management is the most effective way to reduce the amount of sediment produced and the amount that enters the reservoir among the several reservoir sedimentation control options that are available.

Hazards are the primary cause of occupational accidents, as well as occupational safety and health issues. Therefore, identifying potential hazards is critical to reducing the consequences of accidents. Risk assessment is a widely employed hazard analysis method that mitigates and monitors potential hazards in our everyday lives and occupational environments. Risk assessment and hazard analysis are observing, collecting data, and generating a written report. During this process, safety engineers manually and periodically control, identify, and assess potential hazards and risks. Utilizing a mobile application as a tool might significantly decrease the time and paperwork involved in this process. This paper explains the sequential processes involved in developing a mobile application designed for hazard analysis for safety engineers. This study comprehensively discusses creating and integrating mobile application features for hazard analysis, adhering to the Unified Modeling Language (UML) approach. The mobile application was developed by implementing a 10-step approach. Safety engineers from the region were interviewed to extract the knowledge and opinions of experts regarding the application’s effectiveness, requirements, and features. These interview results are used during the requirement gathering phase of the mobile application design and development. Data collection was facilitated by utilizing voice notes, photos, and videos, enabling users to engage in a more convenient alternative to manual note-taking with this mobile application. The mobile application will automatically generate a report once the safety engineer completes the risk assessment.

The goal of this work was to create and assess machine-learning models for estimating the risk of budget overruns in developed projects. Finding the best model for risk forecasting required evaluating the performance of several models. Using a dataset of 177 projects took into account variables like environmental risks employee skill level safety incidents and project complexity. In our experiments, we analyzed the application of different machine learning models to analyze the risk for the management decision policies of developed organizations. The performance of the chosen model Neural Network (MLP) was improved after applying the tuning process which increased the Test R² from −0.37686 before tuning to 0.195637 after tuning. The Support Vector Machine (SVM), Ridge Regression, Lasso Regression, and Random Forest (Tuned) models did not improve, as seen when Test R² is compared to the experiments. No changes in Test R²’s were observed on GBM and XGBoost, which retained same Test R² across different tuning attempts. Stacking Regressor was used only during the hyperparameter tuning phase and brought a Test R² of 0. 022219.Decision Tree was again the worst model among all throughout the experiments, with no signs of improvement in its Test R²; it was −1.4669 for Decision Tree in all experiments arranged on the basis of Gender. These results indicate that although, models such as the Neural Network (MLP) sees improvements due to hyperparameter tuning, there are minimal improvements for most models. This works does highlight some of the weaknesses in specific types of models, as well as identifies areas where additional work can be expected to deliver incremental benefits to the structured applied process of risk assessment in organizational policies.

Ecological environment damage events will destroy or damage the balance between animal and plant habitats and ecosystems, and even pose a threat to China’s ecological security. However, at present, there are some problems in the identification and evaluation of forest ecosystem damage, such as imperfect evaluation system, insufficient quantitative evaluation methods, imperfect damage compensation management system, and lack of analysis of the overall damage of the interaction between human activities and forest ecosystem. Based on the damaged object, the system involves a total of four first-class indicators, including physical damage, mental damage, economic forest fruit loss, forest by-products loss, processing and manufacturing loss, forest tourism loss, scientific research literature and history loss, soil conservation loss, water conservation loss, wind prevention and sand fixation loss, carbon fixation and oxygen release loss, atmospheric purification loss. There are 14 secondary indicators of emergency treatment fee and investigation and evaluation fee, as well as 22 tertiary indicators, and the value quantification method of each indicator is clarified by using market value method, alternative cost method, shadow engineering method, recovery cost method and other methods. The article also discusses the management system of forest ecosystem damage from the two aspects of forestry technology department and judicial administration department. The purpose is to provide reference for the quantification and standardization of forest ecosystem damage assessment technology and the improvement of management system.