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We analyze the composition of : rocks and provide age estimates for detrital zircon grains extracted from sandstones of the tuff-turbidite sequence in the Dzhida Zone of the Central Asian Fold Belt. The sandstones are classified as feldspathic graywackes based on their mineralogical and petrochemical characteristics and are interpreted as first-cycle sediments. The detrital material consists predominantly of volcanic rock fragments, with plagioclase grains being subordinate. Relatively low values of the chemical index of alteration (CIA = 58-68) indicate moderate to weak chemical weathering of lithic components. This reflects the low maturity of the detrital material, which was derived primarily through mechanical breakdown of source rocks. The U-Pb age distribution for detrital zircons from the tuff-turbidite sandstones is bimodal, with major peaks at 530 and 510 Ma. No Neoproterozoic zircon grains were identified in the samples studied. The graywacke composition, low sediment maturity, and the observed U-Pb age distribution of detrital zircons collectively suggest that these deposits accumulated in a forearc basin setting, with detritus supplied from local sources - namely, Early Cambrian volcanic rocks of the Dzhida ensimatic island arc.

LA-ICP-MS dating of zircons from a tuff bed in the Bolshoi Patom Formation in the Ura Uplift yielded an age of 633 ± 7 Ma. The Vendian reference section in the southern Siberian Platform therefore includes the last Cryogenian (Marinoan) glaciation, whereas the postglacial Vendian succession is entirely Ediacaran. Using the obtained age estimate of the Bolshoi Patom Formation, we considered chronostratigraphy of the Riphean-Vendian deposits in the Ura Uplift and in the adjacent areas. The available geochronological and sequence-stratigraphic indicate the presence of a hidden stratigraphic unconformity above the glacial horizon at the base of the Vendian sequence, caused by glacioisostasy.

Located in the north-west of the Murmansk Region, the sulphide Cu-Ni ore deposits were discovered in the 1930s. Nowadays they provide approximately 8-10% of the nickel mining in Russia. The deposits are spatially associated with serpentinite massifs, which have long been assigned to either the ophiolitic complex or basalt magma differentiates. The magma is regionally represented by wide-spread fields of pillow lavas and gabbro-diabase sills. The ultramafic ferropicritic volcanic rocks, which alternate in cross-section with lavas and basaltic tuffs, were discovered in the 1970s. Initially the ore-bearing massifs were intrusions composed of peridotite (wehrlite), clinopyroxenite, and gabbro, which were tectonically metamorphosed during the Svecofennian orogeny. Based on petrological, geochemical, and isotopic research, scientists have proved that ferropicritic volcanic rocks and ore-bearing intrusions are genetically bonded. This paper concerns the processes of differentiation of all the ferropicritic magma facies, i.e., intrusive (ore and ore-free intrusions), volcanogenic (massive and pillow lavas, stratified flows, sills), and dike ones (dike bodies, cross-cutting intrusions, and dike swarms in the Archean surrounding host rocks). Their relations, time and sequence of formation have been revealed. We also discuss the role that the processes of contamination and assimilation of the Archean basement rocks and hosting sulphide carbonaceous schists have played in sulphide ore formation.

This paper presents an exhibition on a new methodological approach emerging within the practice-oriented philosophy of science, addressing the problem of the relationship between contingency and the inevitability of the dynamics of scientific knowledge. The article analyzes the epistemological and methodological principles of this emerging approach and its significance for classical approaches to the philosophy of science.

The purpose of the article is to record and analyze the change in the ratio of the share of fundamental and applied research in the research institutes of the Siberian Branch of the Russian Academy of Sciences. The study brings us to the most important problem of modern Russian science studies, allowing us to more adequately assess the speed and depth of transformation of Russian research practice in the context of global trends in changing the scientific landscape. The key theoretical framework of the study is the widely discussed thesis of an "epochal break" in the development of world science. A sharp turn towards a practically oriented scientific search in a certain sense contradicts the basic academic values formulated within the walls of a classical university. It is shown that at the empirical level, the first points of tension in the research institutes of the Novosibirsk Akademgorodok were recorded almost from the very beginning of market reforms in science. Further, using the example of the results of a number of large-scale sociological studies, it is proved that the forced transition to an increasingly applied orientation of research was perceived quite painfully by scientists, especially the older generation. Many of them interpreted this as a betrayal of the cause of true science. It is concluded that, unlike foreign science, for which the transition to neoliberal science occurred primarily through evolutionary means, in Russian conditions it was a rapid and painful process of forced adaptation of science to new conditions of activity, which was of a shock nature.

The development of humanity is inextricably linked with the development of technology, which led to technical revolutions, starting from the Paleolithic and ending with the fourth industrial revolution, characterized, among other things, by the rapid development and large-scale implementation of artificial intelligence technology (hereinafter - AI). The ability of AI to self-learn and produce results comparable to the results of human intellectual activity demonstrates that technology is becoming increasingly autonomous. This trend forces us to rethink the connection initially formed between man and technology, and to formulate an approach to the legal regulation of AI technology that meets the needs of the time. In the article, the authors turn to philosophical views on the essence of technology and, through this prism, analyze existing approaches to the legal regulation of AI in different countries.

This article examines possible foundations for assessing the credibility of various historical narratives. The search for such foundations is conducted by placing history within the context of cognitive science, beginning with the question of whether the specificity of historical knowledge manifests itself at the level of cognitive processes. It is proposed that narrative constructions are the result of the development of the mind's ability to represent processes. Drawing on ideas from evolutionary epistemology, which understands cognition as a means of increasing organisms' adaptability to changing conditions, history is examined as the ability of social actors to represent processes in order to increase the predictability of their actions.

The ratio of appeals to the work of Descartes, on the one hand, Newton and, on the other hand, Voltaire, is considered. Newton highlighted both the attention to Descartes's that he, Newton, assessed positively, and that in which their positions were not only significantly different, but mutually exclusive. Voltaire paid special attention to his comparison of the contributions of Descartes and Newton to the creation of modern science. While appreciating the corresponding achievements of Newton, Voltaire emphasized that it was Descartes who had the merit of directing European science "along the path of truth", that is, the path on which successes were then multiplied by Newton. At the same time, when considering Voltaire's implementation of this comparative assessment of the positions of Descartes and Newton, it was recorded that they paid attention to those components of Descartes' views that Voltaire assessed as erroneous. At the same time, manifestations of inconsistency in the worldview and philosophical positions of Voltaire himself were recorded. He argues that the explanation of the movement of anything in the universe is impossible without recognizing an external source from him. Voltaire himself declared God to be such an external source of movement for everything bodily, calling him the supreme mind having a will. At the same time, Voltaire assesses the same position of Descartes as erroneous. Voltaire also recognizes, definitely in contrast to Descartes, a person with independent activity. It is emphasized that when Voltaire evaluates certain positions of Descartes as erroneous, he does this from the standpoint of taking into account the achievements, first of all, in the science of nature directly by Newton, which provided for the new European philosophy the prospect of establishing a distinctly materialistic component.

A new class of genes, called ontogenes, together with the class of Mendelian genes, form the genetic program of individual development. Functionally, the program is a set of cycles and quasicycles (spirals) executed by genes of both classes. The genetic cyclicity revealed in the living, apparently, is only a variant of the cyclicity inherent in matter in general. It has been suggested that the total cyclicity of matter is due to the way it is formed in the Universe: matter arises in the flow of cosmic energy when its laminar flow changes to a vortex one. The principle of the quasi-cycle, discovered in the work with the genetic system, was the basis for the cyclic protomodel of the Universe. The article presents its main provisions. On the basis of the provisions, the peculiarities of the organization of the living are explained. The example of the cyclic protomodel shows that biology in its development, like physics and chemistry, has reached the level of building models of a metaphysical nature.

This article opens a series of papers devoted to a holistic, comprehensive presentation of the concept of semantic modeling, including its methodological, logical-mathematical, and technological aspects, with an emphasis on practical applications and the concept's relationship with such areas of the IT industry as programming practice and artificial intelligence. For this reason, the central focus of this series of articles will be the description and discussion of the organizational and technological framework for the practical application of the concept's principles. A few clarifications are in order. Research on semantic modeling began in the late 1970s and early 1980s and is actively pursued today. The main focus of this research is the creation and development of a methodologically and theoretically sound technology for automatically solving intelligent problems as a trusted artificial intelligence technology, and the application of this technology and its tools to solving practical problems. The primary methodological and theoretical efforts of this research are aimed at creating, studying, and developing a declarative logical-mathematical formalism for problem description whose procedural semantics would allow for the automatic extraction of a problem-solving algorithm from its declarative specification and subsequent execution of this algorithm by a computer. The description of the methodological and theoretical results obtained here is the subject of this article. Naturally, the results obtained in this area should find practical application, which will be reflected in the second article in the series, dedicated to describing the semantic modeling technology created and being developed by the authors of this series. Since the problem of further improving this technology by, among other things, applying certain tools of modern artificial intelligence (AI), in particular, large-scale language models (LLM) and generative AI, is of undoubted interest, the third article in the series will be devoted to this issue. It should be noted that the first two articles in this series will focus on explaining the potential of semantic modeling as it applies to practical programming in the IT industry. The topic of deeply automating manual programming, particularly the coding stage, has recently been actively discussed in computer science. This topic has become especially popular with the advent of large language models. It turns out that semantic programming can also be successfully used to solve this problem. For this reason, the first two articles in this series will be devoted to discussing this potential. The relationship between semantic modeling and artificial intelligence will be the subject of the third article.