Self-learning System Using Lecture Information and Biological Data

With the popularity of English across the world, demands grow for English of daily use. By reviewing the English learning methods and its patterns at home and abroad, this paper conducted a major analysis on the status of English self-learning in China. On this basis, a computer network platform based English learning system was designed and realized by using software engineering approaches, whose development techniques are discussed subsequently. Finally, the performance and safety of the online English learning platform are tested. The test results show that the system is effective in improving students' ability to learn English by themselves.

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Data-driven Discovery of Mathematical and Physical Relations in Oncology Data using Human-understandable Machine Learning

10.1101/2021.08.13.456200 ◽

2021 ◽

Author(s):

Daria Kurz ◽

Carlos Salort S&aacutenchez ◽

Cristian Axenie

Keyword(s):

Machine Learning ◽

Cancer Progression ◽

Clinical Data ◽

Clinical Decision Making ◽

Clinical Decision ◽

Biological Data ◽

Rate Of Change ◽

Learning System ◽

Data Driven ◽

Computational Oncology

For decades, researchers have used the concepts of rate of change and differential equations to model and forecast neoplastic processes. This expressive mathematical apparatus brought significant insights in oncology by describing the unregulated proliferation and host interactions of cancer cells, as well as their response to treatments. Now, these theories have been given a new life and found new applications. With the advent of routine cancer genome sequencing and the resulting abundance of data, oncology now builds an "arsenal" of new modeling and analysis tools. Models describing the governing physical laws of tumor-host-drug interactions can be now challenged with biological data to make predictions about cancer progression. Our study joins the efforts of the mathematical and computational oncology community by introducing a novel machine learning system for data-driven discovery of mathematical and physical relations in oncology. The system utilizes computational mechanisms such as competition, cooperation, and adaptation in neural networks to simultaneously learn the statistics and the governing relations between multiple clinical data covariates. Targeting an easy adoption in clinical oncology, the solutions of our system reveal human-understandable properties and features hidden in the data. As our experiments demonstrate, our system can describe nonlinear conservation laws in cancer kinetics and growth curves, symmetries in tumor's phenotypic staging transitions, the pre-operative spatial tumor distribution, and up to the nonlinear intracellular and extracellular pharmacokinetics of neoadjuvant therapies. The primary goal of our work is to enhance or improve the mechanistic understanding of cancer dynamics by exploiting heterogeneous clinical data. We demonstrate through multiple instantiations that our system is extracting an accurate human-understandable representation of the underlying dynamics of physical interactions central to typical oncology problems. Our results and evaluation demonstrate that using simple - yet powerful - computational mechanisms, such a machine learning system can support clinical decision making. To this end, our system is a representative tool of the field of mathematical and computational oncology and offers a bridge between the data, the modeler, the data scientist, and the practising clinician.

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Assessment of accumulated personnel training as an indicator of professional development in the context of the formation of the knowledge economy

E3S Web of Conferences ◽

10.1051/e3sconf/202129608007 ◽

2021 ◽

Vol 296 ◽

pp. 08007

Author(s):

A.F. Stepus ◽

O.B. Glavatskikh ◽

N.N. Pushina ◽

A.I. Troyanskaya ◽

N.N. Kharitonova

Keyword(s):

Professional Development ◽

Learning Organization ◽

Knowledge Economy ◽

Learning Processes ◽

Learning System ◽

Third Party ◽

Economic Management ◽

Party Organizations ◽

Quantitative Calculation ◽

Self Learning

A variant of the assessment of training and the increment of qualifications is proposed with the aim of further assigning a categoiy for working specialties or a categoiy for specialists and professions of a long training period. The scientific foundations of the increase in the qualifications of domestic and foreign researchers have been worked out for the development of a quantitative calculation of this increase. The practical use of this gain has also been analyzed in learning processes in its various types. This system allows the employee to influence the process of improving their qualifications. It is important that this technique will allow a self-learning organization to create a self-learning system, the personnel of which, among other things, are able to train in third-party organizations, which increases the competitiveness of both the enterprise and employees in the constantly changing modem conditions and the instability of the global economic management system.

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