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The growing interest and expectations from the blockchain applica-tions attract many analysts to this issue. In what spheres of logistics and supply chain management blockchain is appropriate? What blockchain software solutions are available to companies now? This paper investigates the basic function-ality of the existing software solutions on the market, the comparative analysis of blockchain platforms used for developing the solutions for logistics is also carried out. The main trends of blockchain applications are identified, based on the analysis of the project experience on the use of blockchain, in logistics and supply chain management, in different countries. The problems, limitations and conditions of blockchain implementation are also determined.
Extract-transform-load (ETL) processes play a crucial role in data analysis in real-time datawarehouse environments which demand lowlatency and high availability features for functionality. In essence, ETL- processes are becoming bottlenecks in such environments due to complexity growth, number of steps in data transformations, number of machines used for data processing and finally, increasing impact of human factors on development of new ETL-processes. In order to mitigate this impact and provide resilience of the ETL process, a special Metadata Framework is needed that can manage the design of new data pipelines and processes. In this work, we focus on ETL metadata and its use in driving process execution and present a proprietary approach to the design of the metadata-based process control that can reduce complexity, enhance resilience of ETL processes and allowtheir adaptive self-reorganization.We present a metadata framework implementation which is based on open-source Big Data technologies, describing its architecture and interconnections with external systems, data model, functions, quality metrics, and templates. A test execution of an experimental Airflow Directed Acyclic Graph (DAG) with randomly selected data is performed to evaluate the proposed framework.
Our interest here lies in supporting important, but routine and time-consuming activities that underpin success in highly distributed, collaborative design and manufacturing environments; and how information structuring can facilitate this. To that end, we present a simple, yet powerful approach to team formation, partner selection, scheduling and communication that employs a different approach to the task of matching candidates to opportunities or partners to requirements (matchmaking): traditionally, this is approached using either an idea of ‘nearness’ or ‘best fit’ (metric-based paradigms); or by finding a subtree within a tree (data structure) (tree traversal). Instead, we prefer concept lattices to establish notions of ‘inclusion’ or ‘membership’: essentially, a topological paradigm. While our approach is substantive, it can be used alongside traditional approaches and in this way one could harness the strengths of multiple paradigms.
Recently, transfer learning from pre-trained language models has proven to be effective in a variety of natural language processing tasks, including sentiment analysis. This paper aims at identifying deep transfer learning baselines for sentiment analysis in Russian. Firstly, we identified the most used publicly available sentiment analysis datasets in Russian and recent language models which officially support the Russian language. Secondly, we fine-tuned Multilingual Bidirectional Encoder Representations from Transformers (BERT), RuBERT, and two versions of the Multilingual Universal Sentence Encoder and obtained strong, or even new, stateof-the-art results on seven sentiment datasets in Russian: SentRuEval-2016, SentiRuEval-2015, RuTweetCorp, RuSentiment, LINIS Crowd, and Kaggle Russian News Dataset, and RuReviews. Lastly, we made fine-tuned models publicly available for the research community.
Acceleration of single‐ and multi‐charged oxygen ions in the perturbed Earth's magnetotail is investigated as the possible source of energetic heavy ions in the ring current. The numerical model is developed that allows evaluating the acceleration of oxygen ions O+‐O+8 in two possible scenarios of characteristic perturbations: (A) passage of multiple dipolarization fronts in the magnetotail; (B) passage of fronts followed by electromagnetic turbulence. It is shown that acceleration processes depend on particle charges as well as characteristic time scales of induced electric field variations. Maximum energies gained by oxygen ions correlate with values of their charges. Our simulations show that all kinds of single‐ and multiply charged heavy particles can be efficiently accelerated during multiple dipolarizations processes of the type (A) from initial energies 12 keV to maximum energies about several MeV. The gain of energies of heavy ions under the (B) scenario of magnetospheric perturbations is about 10% higher than in (A) scenario. The shapes of obtained in the model energy spectra were shown to be in agreement with experimental spectra in the range of L‐shells corresponding to ring/radiation belts. Therefore we conclude that the Earth's magnetotail can play the role of the depot where oxygen ions of both ionospheric and solar wind origin can be effectively accelerated during magnetic substorms to energies about several MeV and then populate the ring current and radiation belts of the Earth.
In this paper, we discuss fitness landscape evolution of permanent replicator systems applying the hypothesis that the specific time of evolutionary adaptation of system parameters is much slower than the time of internal evolutionary dynamics. In other words, we suppose that the extremal principle of Darwinian evolution based on Fisher’s fundamental theorem of natural selection is valid for the steady-states of permanent replicator systems. Various cases illustrating this concept are considered.
The introduction of digital solutions in the automotive industry is often accompanied by a significant gap between the expectations and needs of the leaders of transport and logistics companies and the real business results achieved through the Transportation Management System. The causes of this phenomenon largely depend on the human factor, i.e. irrational actions of personnel who inefficiently use digital technology. The prerequisites for such actions are low competencies of employees, unfair attitude to work, sabotage. Accordingly, this leads to deviations in the transportation process, which can lead to violations of the conditions for the transportation of goods. From the point of view of the concept of reliability of road freight transport, this can be defined as a failure. Failures caused by the varied actions of managers when working with the Transportation Management System were extremely difficult to diagnose. Direct monitoring of managers during the working day is often fraught with various organizational difficulties, and is difficult to implement for a long time to identify ineffective staff activities. The concept of Process Mining, which has been known among scientists and researchers of information systems for more than 10 years, is currently of great interest in business. The paper examined the issues of improving and reorganizing the activities of managers of a transport company based on the mining of the TMS Autobase event log. Based on the results of the study, recommendations were made aimed at developing the digitalization of the transportation process, suggesting an increase in transportation reliability.
This article is dedicated to the problem of business communication; examples of communicative acts and dialogues show the meaning of cultural tradition in verbal and non-verbal informational interaction between different persons.
The purpose of the work is to develop methodological tools for determining the potentialities of alternative options for efficient organization of road transport for a metallurgical company. Research methods include review of specialized scientific literature, systematic analysis of production processes, economic and mathematical modeling, SWOT analysis, technical and economic analysis. Based on the research, following results were obtained: - the options for organizing the transport support of metallurgical company in modern economic conditions, including the conduct of motor transport activities by the forces of its own motor vehicle workshop, internal and external outsourcing of motor transport services were identified; -the features of organization of road transport for metallurgical company were identified due to the presence of organizational and technological interconnection of customers and contractors of motor transport services; - the essence of the concept and the mechanism for ensuring the balance of interests of metallurgical company, customers of motor transport services, and road carriers, performers of these services, are mathematically interpreted; - the results of comparative analysis of potentialities of outsourcing and its
alternatives for organizing transportation activities in metallurgical industry are presented. The results obtained are highly relevant. The use of recommendations proposed in the work will allow balancing the interests of customers and contractors of motor transportation services, making profitable motor transport activities effective for metallurgical production.
The digital coordination of work in emerging organizational landscapes is at a critical moment of evolution. New challenges are emerging as the modern workplace of enterprises has widened from static partnerships, to open ecosystems and digital communities with highly distributed resources, teams, and activities. To address the emerging need for future work coordination models, we provided a modernization of coordination theory and proposed situational dimensions and facets of work management contexts. As the first exposition of this, we focused on team management covering formation, resourcing, structuring, and operations. The multidimensional team coordination framework was validated through an open manufacturing case study.
The purpose of this work is to study the evolution of lean thinking and optimize the value stream in the transport provision of enterprises. An overview of the creation and development of lean thinking is presented, a historical and genetic research method, methods of comparison, analysis and synthesis are implemented, and a mathematical model of the optimization criterion for implementing lean transportation is proposed. It is established that the creation of lean thinking should be associated with the development of scientific management methods, which were implemented in Ford Motor Company. Adapting the principles of the American automobile industry to the specifics of enterprises in Japan in the late 1940s allowed Toyota to create a production system. It was significantly different from the American experience, although it was based on it. The peculiarity of technological processes of Japanese enterprises provided high efficiency. When describing the Japanese experience, American researchers in the late 1980s used the term lean. Under this name, experts from the United States have studied and popularized the achievements of Japanese managers. Analysis of the value stream has shown the difference between this concept and the concepts of the value chain and use value. The analysis of the value stream has a separate specificity when considering the transport support of the company’s activities. For these purposes, an optimization criterion is proposed that requires coordinated management of cargo flows and reserves.
This article is dedicated to the problem of communication; on the process example is considered the part of documentation support with analysis that specify some mistakes and context risks that can decrease process performance.
This article presents a model of the ground autonomous vehicles (AVs) motion in the Artificial Road Network (ARN) belonging to the "Manhattan Lattice" type with the implementation of the large-scale agent-based modeling framework FLAME GPU. The most important scenarios of the traffic situation development are investigated, in particular, which are associated with reducing visibility on the roads, especially in conditions of unusual behaviour of some agents of the traffic system, e.g. the unexpected appearance of obstacles such as agent-pedestrians and chaotic maneuvering of usual (i.e. manned) vehicles (MVs) having abnormal characteristics. A new approach to designing large-scale agent-based transportation simulations based on ARNs with a complex configuration and implementation using supercomputer technologies is proposed.
Artificial intelligence and machine learning helps to improve the quality of customer service and change the methods of companies’ activities. For this reason, enterprises should consider integrating these technologies into digital transformation plans to remain competitive. Low-code machine learning platforms allow companies and business professionals with minimal coding experience to create applications and fill in the gaps of the personnel in their organization. Automated machine leaning (AutoML) technology represents the next step in the evolution of machine learning, providing non-technical companies with the ability to create machine learning applications quickly and cheaply
The intellectual structure of scientific discipline consists of a set of interacting topics. The evolution of these topics is the subject of special attention because it reflects the actual interest of researchers and stakeholders. This paper analyzes issues of High-Performance Computing (HPC) on the base of the formal topic modeling technique. Analyzing the abstracts of 7661 publications referenced in Web of Science in 2005–2019, we identified seven topics that concern different aspects of HPC science. The central theme is the Large Scale Applicationsfocused on practical and scientific problems solved using HPC. It is closely linked with Parallel Algorithms that should effectively exploit the thousands of processing cores, Parallel Softwarefor heterogeneous distributed systems, and Interconnected systems that study the integration of HPC facilities in systems of larger size. These topics are relatively stable both in terms of popularity (number of publications) and impact (number of citations). The single topic, which popularity and impact continuously grow in the last 15 years, is Energy efficiency since power consumption is a critical issue of exascale systems. We also found that the topic of Heterogeneous systems dedicated mainly to GPU usage declines after the peak of interest in 2010–2015. The results obtained shed light on the structure of HPC science and supplement the known publications that declare research direction towards exascale performance.
В статье представлены результаты исследования нескольких эффективных моделей прогнозирования динамики котировок акций. Данное исследование основано на анализе прогнозирующей способности пяти методов прогнозирования с использованием выборки, включающей 30 компаний из двух сегментов рынка: автомобильного и информационного.
The blockchain technology is currently penetrating different areas of the modern Information and Communications Technology community. Most of the devices involved in blockchain-related processes are specially designed targeting only the mining aspect, i.e., solving the computational puzzle task. At the same time, the use of wearable and mobile devices may also become a part of eCommerce blockchain operation, especially during the on-charge time. The paper considers the possibility of using a large number of constrained devices to support the operation of the blockchain with a low impact on battery consumption. The utilization of such devices is expected to improve the system efficiency as well as to attract a more substantial number of users. This paper contributes to the body of knowledge with a survey of the main applications of blockchain for smartphones along with existing mobile blockchain projects. It also proposes a novel consensus protocol based on a combination of Proof-of-Work (PoW), Proof-of-Activity (PoA), and Proof-of-Stake (PoS) algorithms for efficient and on-the-fly utilization on resource-constrained devices. The system was deployed in a worldwide testnet with more than two thousand smartphones and compared with other projects from the user-experienced metrics perspective. The results prove that the utilization of PoA systems on a smartphone does not significantly affect the lifetime of the smartphone battery while existing methods based on PoW have a tremendous negative impact. Finally, the main open challenges and future investigation directions are outlined.
This article describes the issues of analysis and assessment of the human factor for predicting the violation committed by the locomotive driver when driving the electric rolling stock. An intelligent system overview for assessing the likelihood of a violation by a locomotive driver is given. Such a system can generate recommendations depending on previously committed violations. One of the tasks is to reduce the risk of locomotive safety devices malfunctions, which are part of the locomotive electrical equipment. The solution to the problem of predicting the occurrence of possible violations is solved using tools and machine learning algorithms. A model has been built that generates recommendations for the driver based on information about previously committed violations and several static characteristics of the locomotive driver.