Paul Ricoeur, French thinker, gives priority to the text itself in his literary hermeneutics and is categorized in the text-centered group. He puts forward this key question in his theory if a literary text has semantic autonomy. He believes that the text has semantic autonomy based on three dimensions of 1) Author's intention, 2) Primary reader and 3) Social-historical circumstances of the text production.According to this interpretation, the text is an array of words having meaning and the consciousness and intent of author and its social historical conditions haven't any role in formation of its meaning. It should be mentioned that there has been too controversies over this matter, that is, semantic autonomy of text, that each of controversies has noted to special dimension of it. One of these controversies is the subject of this essay that is going to see to Paul Ricoeur's literary and text-centered hermeneutics from the view of its function for interpretation of political novel. Therefore, in this essay, the existing political concepts in the political novel are important. The concepts like power, authority, and etc, that can make Ricoeur's text model fragile, because Ricoeur's work doesn't provide a framework for interpretation of the political texts, in general, and political novel, in particular, by virtue of it's focus over semantic conditions (Autonomy of the text from author and it's social-historical circumstances).

For the construction of railway embankments, geotechnical engineers pay special attention to slope stability studies. The factor of safety values plays a crucial part in assessing the safe design of slopes. The factor of safety values is used to determine how close or far slopes are from failing due to natural or man-made causes. The factor of safety is a numeric value to indicate the relative stability, it doesn’t tell about the actual risk level of any structure, but the reliability index and probability of failure quantify the risk level. The present study discusses the findings of a study to determine the factor of safety of an embankment of height 12.3 m by using Geo-studio 2012 software. In this article, the Fragility curve for six different types of cross-sections was also developed i.e. the graph between the probability of failure ( ) and horizontal seismic coefficient ( ), for various values of  (i.e. 0.1, 0.12, 0.144, 0.18, 0.2, 0.3, 0.4 and 0.5). It is observed from the developed Fragility curve, as the  value increases  value decreases. A Fragility curve can be used to calculate failure probability over a range of seismic zones, and for design purposes, a given seismic zone and probability of failure a unique reliable side slope is selected. Further, two machine learning (ML) models namely, Deep Neural Network (DNN) and Support Vector Regression (SVR) have been developed for the prediction of the factor of safety for different sides slope. Obtained correlation values (R) for SVR and DNN are approximately 0.95 and 0.82 respectively. From the help of the predicted factor of safety Fragility curve against horizontal seismic coefficient is drawn for both SVR and DNN models, that for reducing the time of calculation and ease in working best result giving model will be suggested for further analysis of railway embankment.

This study aims to develop seismic Fragility curves for a typical pilesupported wharf. Fragility curve is one of the popular tools in seismic performance evaluation of a structure. The software FLAC2D was used to simulate the seismic performance of the wharf structure. Using eight time history records, occurred in past, as seismic loading, incremental dynamic analysis (IDA) was applied for seismic demand estimation. Based on the resulted seismic response matrix the analytical Fragility curves were developed. As a prevailing tool, adopted Fragility curves are useful for seismic risk assessment. They can also be used to optimize wharf-retrofit methods.

Financial crises can rapidly affect the real sector of the economy, causing significant damage. The existing literature highlights the crucial role of a country's vulnerability in undermining the stability of the financial sector. This study investigates the effect of country risk on financial Fragility in selected countries in the Middle East and North Africa (MENA) from 2000 to 2021 using the vector autoregression (VAR) method for panel data. The findings reveal that economic growth, economic structure, and economic freedom negatively impact financial Fragility, whereas inflation rates, real interest rates, and country risk positively impact it. Country risk generates uncertainty, diminishes the security of the political, economic, and financial environment, and consequently reduces investment, leading to decreased economic growth and increased financial Fragility. Despite its significance, the effect of country risk on financial Fragility remains largely understudied. The results of this study have important implications: policymakers should focus on reducing country risk to foster a more stable political, economic, and financial environment.

The financial Fragility of banks has always been one of the main concerns of supervisors and decision makers in the banking system. In recent decades, financial stability as a goal of the economic system has been increasingly taken into account in policy making. Many of researches has focused on the role of government financing in the monetary system. But so far, the impact of government debt on commercial banks has not been investigated on financial frailty. The purpose of this study is to investigate the effect of government debt on commercial banks on the financial Fragility index in Iran. For this purpose, data from 1979 to 2018 and ARDL method have been used. Results shows that the research hypothesis that the bank debt had a significant effect on the financial system's Fragility was confirmed. The results also show a positive and significant relationship between inflation and financial system Fragility. The regression coefficients shows that the development of the financial market and, more precisely, the increase in value of the stock market and the tax share of production, have a negative and significant relation with the financial Fragility index.