Cultural specification in translation: study of "The conference of the birds"

http://www.ester.ee/record=b4428921~S1*es

Using the functional resonance analysis method (FRAN) to model and analyze lifeboat training in a simulator

Lifeboat operation is a complex procedure in which safety and rescue are of utmost importance. Training coxswain to perform these operations and acquiring sufficient skills and competencies to face unforeseen risks in harsh weather conditions is challenging. However, lifeboat simulators facilitate the training by removing the risk of training in real environments and improving training courses and trainees' skills. In this study, a Functional Resonance Analysis Method (FRAM) model for launching a lifeboat and on-water tasks was created based on the approved lifeboat training course materials, rubric grading, and lifeboat course scenarios. Two scenarios were used to identify some essential functions in a lifeboat operation. Launch a lifeboat, get away from the platform and drive to a safe zone, pick up Person In Waters (PIWs), recover people from the life raft, tow a life raft, stop by a vessel and transfer the PIW are some tasks covered in this FRAM model. The model was tested with the simulator to identify variabilities in terms of accuracy and time. Five volunteers were asked to perform these scenarios. FRAM signatures of different performances were created to visualize various ways of doing an operation. Successful and unsuccessful operations were monitored using the FRAM, and key elements to having successful and unsuccessful outcomes were determined. Identifying functions and their variations helped to discover where and how trainees act differently in the lifeboat operation. The results of building the FRAM model showed that four categories of functions contributed to lifeboat operation, including action, assessment, decision-making, and skill. The comprehensive model presented in this study enables the researcher to better understand lifeboat operations and helps identify the variations that can affect an operation. Effective processes and key features to diagnose acceptable vs. unacceptable performance extracted by FRAM can be considered a perfect source of observational learning to inform trainees. The FRAM approach used in this study can be employed to determine work practices that are more or less effective, allowing for the adaptation of processes and techniques to steer lifeboat training in the direction of routes that would provide better results

Dataset Creation and Imbalance Mitigation in Big Data: Enhancing Machine Learning Models for Forest Fire Prediction

Historically, forest fire prediction methods have leaned on heuristics, local insights, and basic statistical models, often neglecting the complex interplay of variables such as temperature, humidity, wind speed, and vegetation type. The lack of real-time prediction capabilities, paired with unpredictable weather patterns attributed to climate change, underscores the shortcomings of traditional methods, especially in geographically varied regions like Canada. In contrast, machine learning provides the adaptability needed for real-time responses, effectively harnessing updated data and addressing region-specific forest fire risks. The shift towards machine learning is both a timely and revolutionary approach. This research addresses the urgent need for effective forest fire prediction and management strategies, specifically in the Canadian context, by harnessing machine learning methodologies. Using Copernicus’s reanalysis data, this study establishes a comprehensive predictive framework employing four cutting-edge machine learning algorithms. Random Forest, XGBoost, LightGBM, and CatBoost. The study features a robust data pre-processing pipeline, class imbalance correction, and rigorous model evaluation measures. Key contributions include the creation of a feature-rich dataset, comprehensive methods for addressing the class imbalance in large scale datasets, and the development of a machine learning framework tailored for forest fire classification. The findings have significant implications for data-driven forest management strategies, with the aim of facilitating proactive fire prevention measures on a large scale. One primary challenge encountered was the inherent class imbalance in fire classification datasets, with a striking 158:1 ratio between "non-fire" and "fire" events. To address this, the study utilized various re-sampling strategies, encompassing under-sampling, over-sampling, and hybrid techniques. Specific methods employed included NearMiss, SMOTE, and SMOTE-ENN. The NearMiss method with a 0.09 sampling ratio was found to be particularly effective in addressing this imbalance. When combined with NearMiss version 3 at a 0.09 ratio, the XGBoost model outperformed its peers, showcasing an accuracy of 98.08%, a sensitivity of 86.06%, and a specificity of 93.03%. The findings indicate that while high recall from NearMiss Version 3 optimized sensitivity, there was sometimes a trade-off with precision

The effects of explicit teaching of context clues at undergraduate level in EFL and ESL context

The effects of a 6-session intervention targeting contextual analysis on reading comprehension were investigated in undergraduate university classes, assigned randomly to treatment and control conditions. According to the quantitative analysis of the study, in comparison to control group, using context clues strategy caused an effect on reading comprehension of the EFL and ESL students in experimental group who were taught in how to use different context clues while reading, without considering the role of proficiency level and gender as a variable because there was no interaction between them and strategy use in this study. Thus, implementing context clue strategy as a learning tool deserves more attention by college English instructors in both EFL and ESL context. On the basis of the major findings in this research, college English teachers should keep the students better informed of the significance and specific functions of context clues in contextual guessing and should encourage the students to guess word meanings from context instead of inhibiting it when there are adequate context clues offered

The effects of explicit teaching of context clues at undergraduate level in EFL and ESL context

The effects of a 6-session intervention targeting contextual analysis on reading comprehension were investigated in undergraduate university classes, assigned randomly to treatment and control conditions. According to the quantitative analysis of the study, in comparison to control group, using context clues strategy caused an effect on reading comprehension of the EFL and ESL students in experimental group who were taught in how to use different context clues while reading, without considering the role of proficiency level and gender as a variable because there was no interaction between them and strategy use in this study. Thus, implementing context clue strategy as a learning tool deserves more attention by college English instructors in both EFL and ESL context. On the basis of the major findings in this research, college English teachers should keep the students better informed of the significance and specific functions of context clues in contextual guessing and should encourage the students to guess word meanings from context instead of inhibiting it when there are adequate context clues offered

On solving operator equations by Galerkin\u27s method with Gabor frame

This paper deals with solving boundary value problems by Galerkin\u27s method in which we use Gabor frames as trial and test functions‎. ‎We show that‎, ‎the preconditioned stiffness matrix resulted by discretization is compressible and its sparsity‎ ‎pattern involves a bounded polyhedron structure‎. ‎Moreover‎, ‎we introduce an adaptive Richardson iterative method to‎ ‎solve the resulting system and we also show that by choosing a suitable smoothing parameter‎, ‎the method would be convergent‎

A Two Stage Hierarchical Control Approach for the Optimal Energy Management in Commercial Building Microgrids Based on Local Wind Power and PEVs

The inclusion of plug-in electrical vehicles (PEVs) in microgrids not only could bring benefits by reducing the on-peak demand, but could also improve the economic efficiency and increase the environmental sustainability. Therefore, in this paper a two stage energy management strategy for the contribution of PEVs in demand response (DR) programs of commercial building microgrids is addressed. The main contribution of this work is the incorporation of the uncertainty of electricity prices in a model predictive control (MPC) based plan for energy management optimization. First, the optimization problem considers the operation of PEVs and wind power in order to optimize the energy management in the commercial building. Second, the total charged power reference which is computed for PEVs in this stage is sent to the PEVs control section so that it could be allocated to each PEV. Therefore, the power balance can be achieved between the power supply and the load in the proposed microgrid building while the operational cost is minimized. The predicted values for load demand, wind power, and electricity price are forecasted by a seasonal autoregressive integrated moving average (SARIMA) model. In addition, the conditional value at risk (CVaR) is used for the uncertainty in the electricity prices. In the end, the results confirm that the PEVs can effectively contribute in the DR programs for the proposed microgrid model

Assessment of the Relation of Mandibular Cortical Index and Gonial Angle Size in an Adult Iranian Population Using Digital Panoramic Radiography

Objectives: This study sought to assess the relation of mandibular cortical index (MCI) with age, gender and gonial angle size in an Iranian adult population using digital panoramic radiography.Methods: We evaluated 370 digital panoramic radiographs of patients and divided them into five 10-year age groups. Each radiograph was assigned to low (≤120°) or high (≥125°) angle groups in terms of the gonial angle size. The MCI class was also determined for each individual. The multinomial logistic regression was used to assess statistical differences.Results: The MCI class was significantly different between males and females and MCI class 3 had higher prevalence among older individuals. There was no statistically significant difference in distribution of MCI classes between individuals with high and low gonial angles.Conclusion: Age-related changes in MCI support its potential use for detection of skeletal osteopenia