Search

Companies are trying to harness the potential of the current stage of development of the digital industry and make it cost-effective, which requires not only technological innovation but also the transformation of many existing business models. Based on an analysis of examples of digital subscription companies, this article provides additional explanations, including definitions and constituent characteristics of digital subscription business models. The aim of the article was to identify trends in the development of digital subscription services by analysing the number of active subscribers and the impact of COVID-19 on this indicator. Autoregression and analysis methods were used in the study, and the implementation of the selected models was carried out using the Python-3 programming language. The article describes a theoretical basis that determine the feasibility of using the subscription model for the company under non-deterministic critical conditions. The main approaches to forecasting the company’s performance indicators are identified. The type of data to be forecasted is determined, and the necessity of using autoregressive models for further analysis is declared. Factors associated with the COVID-2019 incidence that should be considered as exogenous variables were identified. The multicriteria selection problem was constructed by restricting the family of autoregressive models and determining the criteria for forecasting and data preparation time, forecast accuracy, and the possibility of considering external factors in determining the algorithm efficiency. It is found that moving average autoregression is the most effective. Given the further application of this algorithm to predict the number of subscribers, the expediency of implementing a subscription strategy for the company under non-deterministic critical conditions is declared. It is possible to effectively apply the selected mathematical model to predict the performance of a subscription company. The practical significance of the work lies in the possibility of creating certain benchmarks for the development of the market and a business strategy for product promotion, which will be based on an understanding of the potential benefits and threats of the market for digital subscription products and services

A significant part of new enterprises of various forms of ownership and in different sectors of the economy ceases to exist within the first five years. This tendency to close enterprises indicates the need for strategic planning of enterprise development and the implementation of a risk factor identification, evaluation, and management process in the strategic management of enterprise development. The purpose of the research is to develop a methodological approach that would allow identifying the risk factors that may have the greatest impact at each life cycle stage of analysed project. To achieve the goal set within the framework of the research, structural and logical analysis, methods of systematisation, generalisation, scientific abstraction and hierarchy analysis were used. The article proposes methodological approach to determining the risk factors of enterprise external and internal environment, which makes it possible to identify the risk factors that have the greatest impact at each stage of the project life cycle. Mathematical models have been obtained which allow for the identification of those risk factors that can have the most negative impact (both in terms of frequency of occurrence and potential losses), which will enable the enterprise to increase the efficiency of managing these risks at all stages of project implementation. The impact of risk factors has been assessed in conditions of incomplete certainty and lack of sufficient statistical information. The practical significance of the results obtained lies in the possibility of increasing the efficiency of using available resources in risk management processes. The proposed methodological approach can be used to assess the impact of project risks in a certain industry, which will increase the accuracy of the results obtained

The combination of Liquid Chromatography and Mass Spectrometry (LC-MS) is commonly used to determine and characterize biologically active compounds because of its high resolution and sensitivity. In this work we explore the interpretation of LC-MS data using multivariate statistical analysis algorithms to extract useful chemical information and identify clusters of similar samples. Samples of leaves from 19 plants belonging to the Apiaceae family were analyzed in unified LC conditions by high- and low-resolution mass spectrometry in a wide range scan mode. LC-MS data preprocessing was performed followed by statistical analysis using tensor decomposition in the form of Parallel Factor Analysis (PARAFAC); matrix factorization following tensor unfolding with principal component analysis (PCA), independent component analysis (ICA), non-negative matrix factorization (NMF); or unsupervised feature selection (UFS). The optimal number of components for each of these methods were found and results were compared using four different metrics: silhouette score, Davies-Bouldin index, computational time, number of noisy components. It was found that PCA, ICA and UFS give the best results across the majority of the criteria for both low- and high-resolution data. An algorithm for biomarker signal selection is suggested and 23 potential chemotaxonomic markers were tentatively identified using MS 2 data. Dendrograms constructed by the methods were compared to the molecular phylogenic tree by calculating pixel-wise mean square error (MSE). Therefore, the suggested approach can support chemotaxonomic studies and yield valuable chemical information for biomarker discovery., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Background: Low field magnetic stimulation is a potentially rapid-acting treatment for depression with mood-enhancing effects in as little as one 20-min session. The most convincing data for LFMS has come from treating bipolar depression. We examined whether LFMS also has rapid mood-enhancing effects in treatment-resistant major depressive disorder, and whether these effects are dose-dependent., Objective/hypothesis: We hypothesized that a single 20-min session of LFMS would reduce depressive symptom severity and that the magnitude of this change would be greater after three 20-min sessions than after a single 20-min session., Methods: In a double-blind randomized controlled trial, 30 participants (age 21-65) with treatment-resistant depression were randomized to three 20-min active or sham LFMS treatments with 48 h between treatments. Response was assessed immediately following LFMS treatment using the 6-item Hamilton Depression Rating Scale (HAMD-6), the Positive and Negative Affect Scale (PANAS) and the Visual Analog Scale., Results: Following the 3rd session of LFMS, the effect of LFMS on VAS and HAMD-6 was superior to sham (F (1, 24) = 7.45, p = 0.03, Bonferroni-Holm corrected; F (1, 22) = 6.92, p = 0.03, Bonferroni-Holm corrected, respectively). There were no differences between sham and LFMS following the initial or second session with the effect not becoming significant until after the third session., Conclusions: Three 20-min LFMS sessions were required for active LFMS to have a mood-enhancing effect for individuals with treatment-resistant depression. As this effect may be transient, future work should address dosing schedules of longer treatment courses as well as biomarker-based targeting of LFMS to optimize patient selection and treatment outcomes., (Copyright © 2019. Published by Elsevier B.V.)

Background: Among all malignancies in Ukraine in 2016, the group of non-melanoma skin cancers headed the list in men and made up 21.6%. This group was second in women and made up 17.6%. The diagnostics of skin tumors are becoming increasingly relevant., Methods: The store-and-forward (SAF) method was used. The study included patients that were remotely diagnosed with melanocytic nevi, seborrheic keratoses, skin cancer, and skin melanoma. Patients signed up for remote diagnostics, were examined in person by dermatologists, and had tumors excised with a subsequent histological examination., Results: Using telemedicine, 108 melanocytic nevi, 97 seborrheic keratoses, 62 skin cancers, and 47 skin melanomas were diagnosed and selected. The accuracy of teledermatological examination and teledermoscopy compared to clinical examination and dermoscopy was 90.3 to 100.0%. The accuracy of teledermatological examination and teledermoscopy compared to histopathological diagnoses was 85.1 to 98.9%., Conclusions: Teledermatological diagnosis showed a high ability for detecting skin neoplasms in Ukraine.