At present, issues of ecosystem self-organization and the mechanisms for their sustainable development have been insufficiently explored in academic literature. The key idea of our research is that for enterprises interacting in different industries based on a network partnership, a special tool is needed to ensure the openness of interaction between participants in the transfer of knowledge, technology, information, and resources. The authors argue that the development and practical implementation of a cross-sectoral digital ecosystem platform will allow for the synchronizing of the scientific and technological progress of several industries, making the most effective use of the synergistic effect from the interaction of ecosystem actors and ensuring the transparency and openness of the ongoing processes therein. The authors demonstrate their propositions with the example of unmanned aircraft system (UAS) industry. The proposed model and mechanism of cross-sectoral interaction can be replicated in different technological niches, such as robotics, neurotechnology, quantum technologies, etc. The conclusions arising from the conducted research provide scientists, government bodies, and decision-makers with the necessary information for a better understanding of practical mechanisms and tools that allow for the implementation of self-organization and sustainable development in modern innovation ecosystems.
The presented study examines the fundamental prerequisites for the emergence of digital platforms, which would provide a global view of the role that platforms play in creating a new organizational model — an ecosystem of high-tech industries.Aim. In the context of industrial digitalization, the study aims to substantiate the creation of a modern mechanism for coordinating high-tech market participants within a single economic and organizational space — an ecosystem based on a cross-industry digital platform.Tasks. The authors analyze the international experience of implementing digital platforms, identify problems and provide recommendations for solving them in the context of digital platform implementation in the Russian industry.Methods. This study uses general scientific methods of cognition in various aspects to analyze the current vector of industrial development driven by the introduction of ecosystems as a new organizational and economic model; describe the principles of their formation, possible structure, and main differences from traditional cluster and network models; substantiate that an ecosystem model allows its participants to achieve a positive synergistic effect in the implementation of their strategic development goals in the context of digital transformation.Results. The issues of using the tools of an industrial digital platform to facilitate the interaction between participants within an ecosystem are considered. Platform solutions in the industry show great promise in terms of analyzing large amounts of data, reducing transaction costs, and obtaining “perfect information”. The direction for the implementation of cross-industry digital platforms and creation of ecosystems in the near future is characterized.Conclusions. Digital cross-industry interaction within the framework of a common platform will expand external communications and promotion channels, making it possible to introduce digital business models and diversify production, but also requiring compatibility between the systems of industrial enterprises and a functioning digital platform and cloud environment.
Objective - The objective of this paper is to consider using machine learning approaches for in-firm processes prediction and to give an estimation of such values as effective production quantities. Methodology - The research methodology used is a synthesis of a deep-learning model, which is used to predict half of real business data for comparison with the remaining half. The structure of the convolutional neural network (CNN) model is provided, as well as the results of experiments with real orders, procurements, and income data. The key findings in this paper are that convolutional with a long-short-memory approach is better than a single convolutional method of prediction. Findings - This research also considers useof such technologies on business digital platforms. According to the results, there are guidelines formulated for the implementation in the particular ERP systems or web business platforms. Novelty - This paper describes the practical usage of 1-dimensional(1D) convolutional neural networks and a mixed approach with convolutional and long-short memory networks for in-firm planning tasks such as income prediction, procurements, and order demand analysis. Type of Paper - Empirical. Keywords: Business; Neural, Networks; CNN; Platform JEL Classification: C45
This paper examines the creation of the hybrid power control system with dense layout of units for the various vehicles. The relevance of the topic is proved by the several publications of Toyota Motors, Subaru Corporation, Texas Instruments and others, who have implemented similar decisions in their electronic control units. The mathematical model and the developed prototype are suitable for gasoline internal combustion engine, where the main feature of this development is the support and control of energy generation. Due to the dense layout of particular vehicles it is necessary to resolve the issues of electromagnetic compatibility, based on protection and decoupling of various elements among themselves. The controlling device for the internal combustion engine of vehicle carries out a complete decoupling of control and signal circuits, and protection of power for integrated circuits. These constructive decisions include the installation of optically decoupled devices, protective diodes and resistances for bypassing the high voltage surges. The proposed concept includes the control of the engine cooling, ignition, engine rate and throttle, generation current, engine startup and cut-off. The prototype working on this concept has an increased uptime and working distance. These features ensure the stability of the engine and its primary characteristics of rate support and generation current control. Processes of installation prototype modeling, technical decisions and reliability issues have been described. This work can be used to develop the engine control units with high reliability and diverse set of functions.
This paper describes the practical usage of 1D convolutional neural networks in business platforms for such tasks as income prediction, procurements and order demand analysis. The structure of the CNN model is provided, as well as the results of experiments with real orders, procurements and income data. According to the results, there are guidelines formulated for the implementation in the particular ERP systems or web business platforms. Currently web-based platforms featuring advanced business functions are rapidly growing. Their new functions can use classic and modern concepts. The comparison between several approaches, including machine learning and regression are provided. Technologies used in such platforms are provided and analyzed. The structures of a such specific web-platforms frontend and backend systems are observed. Other prospective ideas of usage are formulated. Keywords: Business, Neural, Networks, CNN, Platform
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