Railway track support condition assessment—Initial developments on a vehicle-based monitoring solution through modal analysis

Nowadays, there are multiple initiatives showing a renewed interest on railway transport of goods and passengers around the world. Thus, an efficient management of railway infrastructures, both at the operational level and in terms of economic profitability, is not only desirable but also corresponds to an area of ongoing research. In order to contribute to these efforts, an alternative and novel methodology to evaluate railway track support conditions is presented here, based on modal analysis of the characteristic frequencies of the multi-element system composed by a railway infrastructure and an instrumented vehicle moving over it. This methodology belongs to the group of vibration-based structural damage identification methods, and is focused on observing the characteristic frequencies of this multi-element system, which can be correlated with changes in the physical properties of the railway infrastructure under analysis. An important feature of the proposed methodology is that it should enable the collection of information regarding the conditions of the substructure of a railway infrastructure. By performing this assessment of a railway infrastructure over its length, and over time by comparing different rides over the same railway stretch, important information can be gathered regarding the support conditions of the track. This paper presents a complete description on the current stage of development of the proposed methodology, along with the theoretical model that serves as the basis to interpret the collected data. Preliminary verification of this methodology is performed through the analysis of two case studies regarding the passage of an instrumented vehicle over two underpasses. The results obtained so far show that the proposed methodology can provide relevant information regarding the support conditions of railway tracks.

Social Dimensions of Political Risk – SDPR: Concept and Index for Risk Impact on Society

Political risk concerns the profits and investment plans of international business (MNCs, FDI). The Social Dimensions of Political Risk – SDPR is an unchartered territory of political risk. Consequently, on the basis of the analysis of theories of risk, political risk, systems, values and globalization the concept for SDPR is generated. This concept is based on basic assumptions: 1) society is a system whose elements are subsystems; 2) the societal subsystem is at the core of society; 3) the relation between societal subsystem and society is such as the relation element – system; 4) political risk is systemic; 5) values are axial to the system, and their carrier is the societal subsystem; 6) laws are an artificial construct that has only a value function, but is not a value; 7) the incommensurability between values and the above mentioned artificial construct generates SDPRs that are relevant to the risk for society. A formal theoretical and analytical model of SDPR and a value triangle and conceptual index of SDPR based on it are introduced. Key conclusions pertain to the following: the need for reconsider the paradigm of democracy; greater participation of the societal subsystem; need for subsystems’ mutual restraint based on the principle of authorities’ restraint.

Modeling stress-strain state of the section of human dental apparatus with the account of contact interaction between the teeth and their partial absence

Complex interaction of incisors of the multi-element system “jaw – tooth – periodontium – enamel – dentinˮ is considered in the present work. The model was created on the basis of tomographic images taking into account previous studies of the human dental apparatus for a simplified model of three molars. The aim of the work is to determine the stress-strain states of the “jaw – periodontium – tooth – dentinˮ system for a different number of incisors and their location using three-dimensional computer finite-element modeling. The results of the study allow obtaining a more complete and accurate assessment of the state of the human dentofacial apparatus as a whole and each of its elements separately in order to improve the technologies and methods of prosthetics.

DETERMINATION OF EFFICIENCY CRITERIA IN THE DEVELOPMENT OF MEDICAL INFORMATION SYSTEMS

EHR play an important role in the health care system, so the issue of data efficiency is key in the development of the EHR system. One of the key benefits of EHR is an access to medical information anywhere, anytime. While testing this advantage, it was found that it is performed only privately and does not take into account the dynamics of patients. Therefore, a model for calculating the efficiency of EHR on the basis of full coverage was developed. The study presents a model of the “Element” system, which provides for maximization of the coverage ratio of EHR to provide providers with access to medical records in the form required. The Element system is based on medical information packaging methods such as the dictionary method, the direct matching method and the combined method, and also allows healthcare professionals to be available to users’ medical information in this regard when EHR with customer data arises from EHR medical fund. In this case, if the presented system can be reduced both the time of work with medical documents and the time required for the appointment of drugs, which, accordingly, reduces the time to receive medical care. A set-theoretical model of the information system for improving the quality of medical services is proposed. The study of this model showed the need to measure the effectiveness of medical systems, taking into account their coverage. Medstar, Medkit and Helsi systems, which are part of the eHealth system, were used for the analysis. During the study, such concepts as full and partial coverage of systems were identified, the disadvantages of partial coverage were indicated. It has been found that for maximum efficiency it is necessary to minimize the time required to access medical information, which is achieved through full coverage.

Machine learning autonomous identification of magnetic alloys beyond the Slater-Pauling limit

Discovery of new magnets with high magnetization has always been important in human history because it has given birth to powerful motors and memory devices. Currently, the binary alloy Fe3Co1 exhibits the largest magnetization of any stable alloys explained by the Slater-Pauling rule. A multi-element system is expected to include alloys with magnetization beyond that of Fe3Co1, but it has been difficult to identify appropriate elements and compositions because of combinatorial explosion. In this work, we identified an alloy with magnetization beyond that of Fe3Co1 by using an autonomous materials search system combining machine learning and ab-initio calculation. After an autonomous and automated exploration in the large material space of multi-element alloys for six weeks, the system unexpectedly indicated that Ir and Pt impurities would enhance the magnetization of FeCo alloys, despite both impurity elements having small magnetic moments. To confirm this experimentally, we synthesized FexCoyIr1-x-y and FexCoyPt1-x-y alloys and found that some of them have magnetization beyond that of Fe3Co1.

ON THE PROSPECTS OF NEUROCYBERNETIC (STRONG) ARTIFICIAL INTELLIGENCE IN BANKING

In the article “artificial intelligence” (AI) is considered as a multi-element system; in its composition, the author identifies objects of industrial property and, first of all, the neurocomputer interface, which in the future can become a “thinking”, “strong” component of this system. It is proved that the fact of using industrial property in a credit institution is not identical to the use of AI in it (an answering machine and a computer that is supplemented with data by an operator can hardly be called a “robot”, as well as a program that processes information faster than a person to open a Bank account, etc.). in this context, promising directions are described; so, if at the beginning of 2021, as the top managers of Sberbank claim, its Neurocomputers will be able to independently make decisions on issuing small loans without further approval by the Bank’s employees on the basis of a combined analysis of the client’s biometric data, credit history, income and expenses, then it will be possible to state the Genesis of the formation of a «strong» AI in banking.