سعیده کریمی

Today, waste management is one of the important pillars of resource management in hospitals. The purpose of this study was to identify the effective factors in the management of special medical wastes at the source of their production, to analyze these factors, and to determine the most effective ones to improve the quality of management of such wastes.

In this research, the basics and dynamic components of systems were used to investigate the performance of hospital waste management in this field. For this purpose, the main indicators of the organization's performance were extracted from the research literature. Then, using the concepts of system dynamics, the effect of these factors was shown in the form of cause and effect diagrams. To explain the function of the proposed model, research variables were examined in the form of different scenarios to determine the effectiveness of each in different conditions.

According to the presented flow diagram and the proposed scenarios, it was found that by using the optimal scenario, the various variables of the research, such as decontamination rate, collection, and unauthorized entry into urban waste reservoirs, through the explained relationships of the model in it improves about 15 to 40 percent.

The results obtained in this research ultimately lead to the reduction of environmental risks as well as the reduction of hazardous waste production, which can be a basis for explaining waste management strategies in medical centers.

Jahiliyat, ignorance, is a Quranic term expressing the behavioral, moral and religious characteristics of the people of Hijaz in the historical period before Islam. Sayyid Qutb, after dividing Jahiliyat into ancient and modern and separating societies into Jahili and Islamic, in the explanation of the term Jahiliyat, sets Jahiliyat and disbelief against each other, and as a result considers the Jahili society as a disbelieving society. But according to Allameh Tabatabai, disbelief and faith have degrees and grades, and each ignorance may not be disbelief. For him, according to the verses of the Holy Quran, disbelief and idolatry are one of the effects and results of Jahiliyat, not the same thing. Based on this, considering the society as disbelievers, Allameh Tabatabai does not agree with their takfir. The view taken in the research, which was carried out with descriptive-analytical method, is that of Allameh Tabatabai's. According to the article ahead, Sayyid Qutb's error is in the semantic confusion between law and sovereignty in the verse "A Fa hukam al-Jahiliyyah Yabghun".

The occurrence of fires is one of the important factors that determine the different characteristics of many terrestrial ecosystems. For a long time, fires have severely affected forest areas, and sometimes their negative effects remain for several years after the occurrence of the fire, so that the state of vegetation does not return to its previous state. The aim of this study is to model the restoration of vegetation in Zagros forests (Ilam province) following fire.

We used various climatic and environmental data as independent variables (vegetation at the time of fire (NDVI+1), burn severity index, temperature and precipitation anomaly, average temperature, annual precipitation, slope, aspect, and elevation) and NDVI +5 as dependent variable for the modeling (using random forest, decision tree and gradient boosting) the vegetation recovery following fire. Landsat satellite images were used to prepare indices indicating vegetation density status and burn severity, and after preprocessing the images, these indices were prepared by spectral ratio. Climatic variables (precipitation, average temperature, minimum temperature and maximum temperature) were also estimated according to the regression relationships between these variables and the elevation in the study area. Finally, three machine learning algorithms, including decision tree, random forest, and gradient boosting, were used for modeling, and also the accuracy of these models were evaluated.

The results showed that among the various variables investigated, the annual precipitation, average annual temperature, normalized vegetation difference index (NDVI) and burn intensity index at the time of fire were the most important factors affecting the vegetation restoration post fire in these forests. The precipitation and temperature were the most important factors affecting the restoration among the mentioned factors. Also, the results showed that among the different models, the gradient boosting algorithm with R2 = 0.66 models vegetation restoration better than other models. In this model, the climatic factors were the most important in the vegetation recovery.

According the relationships between the NDVI and other studied factors and the results of the modeling; it is possible to explain the effective role of climate factors in the vegetation restoration in the study area.