Over the past few decades, democratic procedures have been raised as main core of most urban planning methods. In this regard, the present research adopts Descriptive-Evaluation methods and uses meta-analysis method so as to review the democratic procedures at the heart of planning theories and democracy and to explore their paradigmatic interaction. The main challenge is the emergence of planning Practices based on public interest and in alignment with democratic nature of it, so the results of present research suggest that transition to post-modern era of planning perspective is accompanied by association between democratic procedures and certain subjects such as public interest, consensus, pluralism, uncertainty and agonistic arenas. In this regard, the theory of planning has experienced a paradigmatic shift towards agonistic planning which is a democratic accountability mechanism premised on intellectual support of agonistic democracy. So this article tries to recognize and set up a platform for emergence of new democratic theories, especially appearance the agonistic planning theory in Iran, and consequently the formation of social discourse due to turning hostility into agnostic and replacing enmity by disagreement or aggression by competition.

In his book "The God delusion" Richard Dawkins has denied the existence of the God of Abrahamic religions, including Islam. In order to prove his point of view, in addition to presenting the reasons he tried to remove everything that is considered as an obstacle. The point of view of agnosticism is one of these obstacles that Dawkins, by accepting its principle, believes that the probability of the non-existence of God is greater than his existence; therefore, one should not be agnostic about the existence of God. He has based his belief on the basis of the division of " agnosticism " into temporary and permanent, Knowing the existence of God as a scientific hypothesis, the principle of the impossibility of definitively proving the absence of a thing, criticism of Bayesian reasoning and improbable reasoning. In this article, Dawkins' point of view and reasons are first introduced in a descriptive way, and then the success rate of the reasons presented in proving his claim is analyzed by a critical method. The result of the research shows that these reasons cannot prove his claim and their ultimate efficiency is to prove theoretical agnosticism.

SUBJECT AND OBJECTIVES: The difference between how people view atheism varies in the world, in the west, Atheism is seen to be a normalised topic which is accepted by many people whereas in the East, heavily criticised. The openness in western societies with the spread of free secular materialistic thought occurred after the Enlightenment period, has pushed people towards the concept of atheism, especially in the younger generation. METHOD AND FINDING: The East has much harsher rules when dealing with the influx of atheism where some countries believe death by execution is the solution. This pushed them to westernised way of thought where they began to fight Islamic beliefs, and of course under the support and protection of western regimes that they used as tools against Islam, many managed to flee their countries and go to the west as they consider that religion restricts and prevent them from achieving their goals, while atheism makes them free from all values and allows the achievement of freedom, without any restrictions. CONCLUSION: The Spread of Atheism in the Muslim countries stems from western atheism. It is thought that the increase of East atheism is due to cultural, intellectual, educational, psychological, political, social, and religious reasons and it is our duty as Muslims to find out why this is and how to solve it from the concept of Islamic beliefs the same way Imam Sadiq would by adopting a logical concept to convince the atheists during their theological debates and arguments. Due to their lack of evidence, it was not long before their theories were being disproved with one example being from the famous British philosopher Anthony flew was a strong advocator for atheism wrote a book called “ There is a God” in the later days of his life.

Big data is an important achievement of the ICT revolution. In this article, big data is considered in terms of its impact on scientific research and the epistemology of knowledge. There are two main trends in this matter: one is that such a phenomenon leads to the abandonment of theory from research processes, and the other considers the role of theories to be still effective. What we consider regarding the epistemological transformation caused by big data is somewhat different from what is stated in the relevant literature. Of course, it was presented in the critical analysis of researchers' points of view. Now we summarize it as follows: 1-The traditional division of disciplines has lost its importance or at least has been weakened due to the ocean of data. Data is independent of the field, and therefore interdisciplinary cooperation in the “agnostic” situation of science might be irrelevant. Of course, we are not of the agnostic belief that theory has lost its importance in the big-data age, as Chris Anderson claims in his article “The End of Theory: The Data Deluge Makes the Scientific Method Obsolete”. But without this agnostic approach, we can also accept that the role of theories, at least at the level of description, is greatly reduced, and therefore we can accept that data analysis is possible without disciplinary affiliation, and as a result, interdisciplinary collaborations are also shaky. 2-Data-driven science increases the role of correlation compared to causal theories and increases the role of researcher decision-making in the application of knowledge.   Application means that a correlation is functionally important without depending on a specific discipline, and it can be used independently of any discipline,only to solve problems. The problems are not within the disciplines, but are located in the real world and impose themselves on us independently of the theories and disciplines. To solve a problem, numerous disciplines can be considered. These different perspectives mean that “a problem” is not dependent on any of these perspectives (disciplines). The problem of inflation can be seen from the perspective of economics, social psychology, political science, etc. 3-Weakening the role of theory and discipline in scientific development opens another field for scientific activities. New actors enter into scientific activities to the extent possible to play a competitive role with traditional researchers in scientific development. While the methodological changes produced by big data do not seem to be sufficient to invoke an entirely new paradigm in knowledge discovery, the emergence of big data has profoundly shaped the actors involved and their relationships. Moreover, even, the transition of scientific authority is predictable. In the age of big data, the shift of scientific authority from researchers and scientists (the followers and holders of grand theorists) to technicians, digital businessmen, and even ordinary users of big data can be expected. According to Chang et al., categories of mass data have emerged, which are caused by interactions, economies, interactions of societies or nations, and interactions of individuals. Public access to these data and especially facilitating the use of information technology tools can enable a large number of non-professional users in the field of research even with basic knowledge of statistics and data analysis in the field of science production. It is useful to remember that before the appearance of statistical software such as SPSS and SAS, performing a statistical analysis requires familiarity with programming skills. With the development of this software, more researchers were able to perform statistical operations because programming skills were removed from this process. Similarly, increased access to big data and analytical technologies resulting from new tools enable more "people" to analyze data and discover correlations between data for personal or business purposes or socio-cultural interactions. The production of public knowledge by people who are considered unprofessional according to "academic standards" is an important aspect in the evolution of scientific epistemology in the age of big data and analytical tools of information technology. The aggregative and generalized structure of scientific activity (if such a structure is important anymore) which was done by professional researchers and scientists through the structures of knowledge sharing and reproduction, will be performed by the machine. Knowledge activity will be displaced from its traditional institutional context. This is why I say that the implicit structure of authority that has been created over time by scientists and researchers is disrupted by "new revolutionaries" in the world of science. The institution of power in the production of science becomes shaky, scattered, multipolar, and rather rebellious. The problem is that in the previous paradigms, a kind of hierarchical structure of power or authority or scientific authority had emerged. The new paradigm targets exactly this hierarchical structure. Everyone can observe the big data and make inferences from it: No prior expertise,not just without prior theory,in an "agnostic platform" and without dependence on any field or discipline,without aristocracy of epistemological knowledge or any other theory and with a disruption from previous scholars and scientists. 4-Massification of actors raises the question of whether the accumulative, aggregative, and evolutionary characteristics of science, as seen in the history of science, do not undergo a radical transformation. Doesn't it go out of its institutional context, i. e. the field of "academy"? Microsoft, Apple, Facebook, Telegram, WhatsApp and many others have come to the field of innovation by people whose original innovators were often not "scientists" in the conventional sense, and sometimes even dropped out of university. These initiatives and innovations have been the source of the growth of science. The result is that scientific evolution has come from the academic environment to the business environment. Science has become more dependent on the market than before, and the "dynamism" of the market has become the source of the "dynamism" of science. Information technology has provided such a platform by facilitating communication and combining various technologies to provide a context for mutual leap. Science is neither interdisciplinary nor disciplinary but non-disciplinary. This can disrupt the entire structure of science that has dominated since the Renaissance.

Purpose: It tends not only to shed lights on an emerging classification framework of disease according to the shared molecular pathogenesis across various organs/tissues, but also to inspire more efficient paradigms of pharmaceutic innovation in a broader medical perspective. Methods: Literature review and re-thinking. Results: This article has sorted out an updated profile of the outstanding targeted medications with an extending list of clinical indications in oncology and beyond. Conclusion: Pharmaceutic development can be processed in a less risky and more affordable manner through drug repurpose or tissue agnostic approval.

Background and Objectives: Identifying pregnant women who are at risk of premature birth and determining its risk factors is essential because it affects their health. This study aimed to use an interpretable machine-learning model to predict premature birth. Methods: In this study, data from 149,350 births in Tehran in 2019 were utilized from the Iranian Mothers and Babies Network (IMaN) dataset. Various factors related to the mother and the fetus, such as the mother's demographic variables and health status, medical history, pregnancy conditions, childbirth, and associated risks, were considered. The machine learning models, including multilayer neural networks, random forest, and XGBoost, were employed to predict the occurrence of preterm birth after data preprocessing. The models were evaluated based on accuracy, sensitivity, specificity, and area under the ROC curve. The Python programming language version 3.10.0 was applied to analyze the data. Results: About 8.67% of births were premature. The XGBoost algorithm achieved the highest prediction accuracy (90%). According to the model output, multiple births, which account for 46% of pregnant women's births, had the highest importance score. Delivery risk factors had a score of 41%, and other variables, including neurological and mental illness, preeclampsia, and cardiovascular disease, were subsequently ranked in order of importance for this particular individual. Conclusion: Using an interpretable machine learning method could predict the occurrence of premature birth. Based on risk factors, the interpretable machine learning method can provide personalized preventive recommendations for every pregnant woman, aiming to reduce the risk of preterm birth.

One of the debates concerning Hume’s Philosophy is whether he believes in God. Through investigating his own works and extraction of his assertions concerning God, we try in this paper to examine the internal consistency of his system. In order to do this, we shall analytically examine whether the current tags attached to him are consistent with implications of his works. This research shows that it is problematic to call him an “atheist” or an “agnostic”. Furthermore, the findings of this paper shows that his minimal belief in God can be found in all of his works. Finally, according to the results, it is suggested to call him a weak or non-dogmatic agnostic.

With the increasing interconnectedness of communications and social networks, graph-based learning techniques offer valuable information extraction from data. Traditional centralized learning methods faced challenges, including data privacy violations and costly maintenance in a centralized environment. To address these, decentralized learning approaches like Federated Learning have emerged. This study explores the significant attention Federated Learning has gained in graph classification and investigates how Model Agnostic Meta-Learning (MAML) can improve its performance, especially concerning non-IID (Non-Independent Identically Distributed) data distributions.In real-world scenarios, deploying Federated Learning poses challenges, particularly in tuning client parameters and structures due to data isolation and diversity. To address this issue, this study proposes an innovative approach using Genetic Algorithms (GA) for automatic tuning of structures and parameters. By integrating GA with MAML-based clients in Federated Learning, various aspects, such as graph classification structure, learning rate, and optimization function type, can be automatically adjusted. This novel approach yields improved accuracy in decentralized learning at both the client and server levels.