Any language. relying on its speakers beliefs and worldviews, has cliche forms and constructions, which remain frozen in the nomenclature of its culture, Idioms, similes, proverbs, meaning referents and superordinations from the essence and nature of language. They are ingredients of the culture of a specific language and are, thus, untranslatable between languages. Knowingthefeatures and the interpretations attributed to them will lead us to these cultural principles. In the process of time, idioms lose their flexibility and gradually become less versatile. In fact, as idioms get seated in the lexicon of the language, they lose their original flexibility and become syntactically inseparable. The degree of the flexibility of idioms depends on the degree of their distribution environment. Flexibility and interpretation are interrelated, each determining the other. Whether idioms and idiomatic expressions are formed spontaneously or gradually is an issue that required a thorough investigation. Nevertheless, there is a unanimous agreement that idioms do exist in all languages with relative application and use. The problem becomes more severe when one intends to render from one language to another. Variations in collocations and the nature of idioms and their meanings all indicate the cliche constructions. Word by word translation of idioms is rather difficult if not impossible. In this paper, we will concentrate and elaborate the nature of idioms in Persian and English with the intention to evaluate and measure the degree of flexibility in the hvo languages concerned. We hypothesize that some idioms are analyzable to their components, whereas the constituents of other idioms are inseparable. Moreover, we hypothesize that idioms react differently to transformational rules.

The manner of using language in Ferdowsi’s Shahnameh, like the works of other great figures of Persian literature, is a creative and unique one. An instance of such creativity is revealed I using the word “Soxan”. Without being bounded in the common usages of this word in his time, Ferdowsi has found out its special capacity and conferred some great interpretive capacity to it in his artistic book of verse.Using descriptive-analytical method, this research aims to propose thirty six readings of this word in order to show such characteristic of Ferdowsi’s poetry and draw the attention of readers to the conceptual scope of one of the words of Iranian national epic and its capacities.Foregrounding the status of this word in the linguistic structure of this work which paves the way for the interpretability of the text and also representing his personal style in using such word which asks the reader to decode its meaning and interprets it himself are other aims of this research which is unprecedented among researches about Shahnameh.

"Faith", as one of the most important companions in human history, is a factor in creating homogeneous religious communities, but at the same time, with the emergence of interpretive disagreements, it becomes a cause of conflict among individuals. Even some of the greatest battles in history are the result of people rejecting their counterpart religious interpretations. By introducing an understanding of faith that considers it to be interpretable and states that different social, cultural, and even natural and historical circumstances and situations influence individual perceptions of faith, we can help increase tolerance and flexibility in society. The religious teachings of Islam, despite differences in individuals' capacities for accepting and expressing faith, have always encouraged brotherhood and forbade individuals from imposing heavier burdens on those with less capacity. By using Scriptural evidence, this article attempts to prove that faith is interpretable. Consequently, the article clarifies that, on the one hand, due to the "gradation of faith", there are multiple interpretations of it in society and, on the other hand, achieving the important principle of brotherhood will depend on understanding this aspect of faith and promoting individuals' flexibility and tolerance. It will allow individuals to live peacefully and empathically, free from violence.

Introduction: The ability to interpret the image is one of the key and important features in the ability of imaging systems, which is usually used in the concept of the ability to extract information from the image. Quantifying this feature is one of the main challenges in the fields of information content and image integration in remote sensing, which is needed and important in various applications such as target detection. By evaluating the interpretability of remote sensing images, it is possible to estimate the appropriate image characteristics for various target detection applications. In general, the interpretability of remote sensing images depends on the conditions of imaging, environment and target. Polarimetric radar images are a very useful source of data in target detection applications with more resolution than single-channel radar images. So far, various methods and models have been presented to evaluate the performance of detection in polarimetric radar images,most of them are based on the theory of signal detection or based on the probability of target detection and false alarm. National Image interpretability Rating Scale (NIIRS) models, the automatic target detection (ATD) and simultaneous tracking and tracking and recognition (STAR) models are among the common and conventional methods of evaluating the interpretability of radar images in target detection applications. The NIIRS radar is based on the general image quality equation (GIQE) and takes into account the parameters of resolution, landing angle, squint and background type. The ATD model is designed based on false alarm analysis and estimates the measure of performance (MoP) according to available ground information. The STAR model is also based on the modelling of parameters related to the environment, target and sensor and calculates the probability of success in detecting, detecting and identifying the target in the radar image for a target with specific dimensions and physical conditions. Proposed method: In this study, a unified method has been presented to evaluate the interpretability of polarimetric radar images in the application of target detection. In this method, the performance of the radar detector is first calculated based on the parameters of the detector in the STAR model. After that, for each of the images related to the channels and polarimetric parameters, the target detection performance is calculated by calculating the performance measure (MoP), which relates the detection probability to the false alarm rate is determined. Finally, by combining the information obtained from the MoP criterion and the STAR model, the interpretability of polarimetric radar images is calculated. Experimental results: In this study, the L-band polarimetric radar dataset related to the Flevoland region of the Netherlands, which was obtained by the AIRSAR sensor in 2009, is used to evaluate the effectiveness of the proposed method. Based on the obtained results, the proposed method increases the probability of detecting, detecting and identifying the target by 30% compared to the STAR model. For the development of this research, it is suggested to improve the performance of the radar sensor by using the techniques of combining radar data with optical data and auxiliary data. This case, especially in tracking targets in complex environments, can significantly improve continuous target tracking.

Accurate predictions of air pollutant PM10 concentrations are essential for crafting effective air quality management strategies. This study compares three decision tree ensemble models—Random Forest (RF), Extra Trees, and Extreme Gradient Boosting (XGBoost)—to forecast daily PM10 levels in Thiruvananthapuram, India. By integrating meteorological data and air pollutant variables, this study aims to enhance both the accuracy and interpretability of urban air pollution dynamics. Spearman correlation analysis is employed to analyse the relationships between PM10 and the various input features. The predictive performance of the ensemble models is evaluated using Root Mean Squared Error (RMSE) and Coefficient of Determination (R²). The Extra Trees model demonstrates superior predictive performance, achieving an R² of 0.945 and an RMSE of 8.174 μg/m³. The model-agnostic interpretability method SHapley Additive exPlanations (SHAP) demonstrates that PM2.5, NH3, NO2, and O3 have a major impact on PM10 forecasts. Additionally, it reveals that meteorological conditions, particularly rainfall and relative humidity, play a crucial role in determining PM10 concentrations. This research highlights the potential of machine learning techniques, especially when combining the Extra Trees model with SHAP, to assist local governments in strategic planning and air quality management efforts. Although temporal coverage limits are acknowledged, this study offers useful information to environmental agencies and policymakers looking for data-driven strategies to reduce air pollution.