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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.
Dynamic blockage of radio propagation paths between the user equipment (UE) and the 5G New Radio (NR) Base Station (BS) induces abrupt rate fluctuations that may lead to sub-optimal performance of the Transmission Control Protocol (TCP) protocol. In this work, we characterize the effects of dynamic human blockage on TCP throughput at the 5G NR air interface. To this aim, we develop an analytical model that expresses the TCP throughput as a function of the round-trip time (RTT), environmental, and radio system parameters. Our results indicate that the blockage affects TCP throughput only when the RTT is comparable to the blocked and non-blocked state durations when the frequency of state changes is high. However, such conditions are not typical for dynamic body blockage environments allowing TCP to benefit from the high bandwidth of 5G NR systems fully.
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.
We considered basic mechanisms of atmospheric particle acceleration and estimated the escape rates of ionospheric ions (H+ and O+) during the geomagnetic field reversal. It is assumed that during the reversal the Earth's magnetic field deviates from the current dipole configuration, and the quadrupole component dominates. The standoff distance of the quadrupole magnetosphere is about of 3 Earth's radii and therefore a magnetic shielding protects the atmosphere from sputtering and ion pickup but not from the polar and auroral winds.
Currently, there is an extensive set of bankruptcy prediction models, but almost all of them are classification based, i.e., they allow to estimate the posterior probability that a particular firm will fail, given its financial characteristics. The expected time to failure is not considered explicitly. On the other hand, there is a survival analysis that deals with the time of the occurrence of the event of interest (while this event may not occur during observation). However, despite its popularity in the medical and technical sciences, survival analysis is relatively rarely used in predicting financial failure. Even when it is applied, most authors use the simplest form of a model. The goal of our work is to evaluate the applicability of survival analysis to bankruptcy prediction. We compare a few state-of-art statistical and machine learning models using a real dataset. Our findings confirm that survival analysis allows (1) to extract from given data valuable information regarding the dynamics of risks and (2) to estimate the impact of features.
The present increase of attention toward blockchain-based systems is currently reaching a tipping point with the corporate focus shifting from exploring the technology potential to creating Distributed Ledger Technology (DLT)-based systems. In light of a significant number of already existing blockchain applications driven by the Internet of Things (IoT) evolution, the developers are still facing a lack of tools and instruments for appropriate and efficient performance evaluation and behavior observation of different blockchain architectures. This paper aims at providing a systematic review of current blockchain evaluation approaches and at identifying the corresponding utilization challenges and limitations. First, we outline the main metrics related to the blockchain evaluation. Second, we propose the blockchain modeling and analysis classification based on the critical literature review. Third, we extend the review with publicly accessible industrial tools. Next, we analyze the selected results for each of the proposed classes and outline the corresponding limitations. Finally, we identify current challenges of the blockchain analysis from the system evaluation perspective, as well as provide future perspectives.
This paper summarizes practices of customer- driven services applied in the leading Russian bank to avoid the impact of financial sanctions (2014–2019). We show how economic sanctions and strict national policies triggered this bank to increase flexibility in customer care to attract more capital from their existing clients. The project comprised three stages: (1) to analyse requirements and to develop ‘‘as-is’’ state of processes; (2) to analyse best practices and to improve processes under the scope of flexibility and customer orientation; (3) to implement the new vision in ‘‘to-be’’ state and final verification. At the third research stage to assess the results of processes improvement in the bank within a year we have applied a set of methods based on data envelopment analysis which provides a multidimensional understanding of processes and new scopes of customer’s value profiles. We have found that process reengineering result could give the contribution already at the first month of implementation and argue the findings could be used to introduce flexible data-driven customer care and improve customer-related processes in organisations worldwide.
This work presents a novel approach to the design of a decision-making system for the cluster-based optimization of an evacuation process using a Parallel bi-objective Real-Coded Genetic Algorithm (P-RCGA). The algorithm is based on the dynamic interaction of distributed processes with individual characteristics that exchange the best potential decisions among themselves through a global population. Such an approach allows the HyperVolume performance metric (HV metric) as reflected in the quality of the subset of the Pareto optimal solutions to be improved. The results of P-RCGA were compared with other well-known multi-objective genetic algorithms (e.g., MOEA, NSGA-II, SPEA2). Moreover, P-RCGA was aggregated with the developed simulation of the behavior of human agent-rescuers in emergency through the objective functions to optimize the main parameters of the evacuation process.
In this paper we propose an approach for compact storage of big graphs. We
propose the preprocessing algorithms of a certain type of graphs which can
signi cantly increase the data density on the disc and increase performance
of fundamental operations with graphs.
Healthcare services are tightly connected with complex data analysis techniques to enable optimal resource allocation in medical institutions. This paper proposes a detailed analysis of incoming patient flow to local polyclinic by integrating clustering techniques, process mining and a concept of self-organizing systems. The study takes into account concepts based on models of managing social networks, the participants of which today can be both people and intelligent software. How could patient flow model be developed using a clinical pathways approach that combines clinical pathways tool, social media analysis, hierarchical agglomerative clustering method and probabilistic topic modeling to investigate the optimal resource utilization of medical facility? The methodology to answer this research question was demonstrated using a time- series clustering (kmedoids, Ward's method, Latent Dirichlet Allocation, Additive Regularization of Topic Models), Naive Bayes classifier based on public real data of 64668 depersonalized patient- doctor of 32 specialties conversions. In this paper, a modeling methodology for heterogeneous patient flow segmentation is proposed. The presented approaches serve as the foundation for the further development of a queuing system model of a medical institution. In addition, the shared economy principles are applied by the development of such service that would reduce the workload of appointments to therapists by matching patients to needed doctors.
This paper presents a correlation method for processing data on end devices and reducing
the amount of data transmitted over the network. Instead of expensive and complex network
devices, developers can use cheap and proven low-speed Internet of Things (ZigBee, NB IoT,
BLE) solutions for data transfer. The novelty lies in one of the features of this approach: the use
of components for analysis, rather than a complete copy of the signals, as well as processing
directly on the sensor. The advantage of this approach allows you to reduce the number of
operations and complexity of implementation, in contrast to other methods focused on the
cloud computing paradigm. We provide results for correlation values and the number of logical
elements (LE) when implemented on the FPGA, depending on the number of elements in the
correlator. This allows to maintain a balance between the required calculation accuracy and
spent hardware resources, as well as to simplify the end device.
The rapid development of information technologies and their widespread use in various sectors of the economy, including transportation, have led to a massive growth in data flows. However, the paradox of our time is that this data is not used to its full potential to provide companies with powerful impetus for their development. This is especially true at the strategic level, where company executives still mostly make decisions without relying on the recommendations of business intelligence systems. In the management of road transport, this phenomenon can be attributed to the fragmentation of data sources and the vague understanding of the relationships between them. This is also partially due to the diversity in opinions in the expert community on the efficiency of the transport processes. Thus, it is obvious that, without deciding on how to measure efficiency, it is hard to say where the data for calculations should be acquired from and what should be the architecture of a decision support system. Note that the diversity of data sources in the digital age creates prerequisites for various metrics that highlight different aspects of the transportation process.
With the wide variety of information systems and applications for motor transport and transport logistics control we have today, one may think we are already living in the digital era of general welfare, and digital tools would easily ensure sustainable development and prosperity of businesses. However, the experience of deployment and introduction of such solutions shows that their value for transport business is significantly lower than expected. Moreover, in some projects, business performance of transport companies had no correlation with introduction of information systems. In the best-case scenario, they provided for a slight decrease in document flow transaction costs. The change of the strategic status of a company in the transportation service market is a fairly complicated task, which, as analysis of literary sources shows, is achievable for few enterprises, primarily small and medium-sized businesses. Such situations show that information solutions were introduced without analyzing or assessing the business models of certain companies which could be used a basis for digital landscape of business as a whole. In recent years, the basic concept of forming a single information space of an enterprise has been the enterprise architecture. It provided for coordination between all the business processes in order to achieve a company’s strategic goals. The fundamentals of the concept were developed by J. Zachman in his famous Zachman Framework, and it was later developed with numerous models of enterprise architecture (e.g., TOGAF (Department of Defense Architecture Framework), GERAM (Generalised Enterprise Reference Architecture and Methodology), DoDAF (Department of Defense Architecture Framework)). However, currently some researchers note that sustainable corporate development should stem not only from a “correct assembly” of all its business elements, which was the purpose of enterprise architecture, but also from the interaction of these elements when reaching the emergence effect. In this context, one should pay attention to comprehensive activity analysis of a transport and logistics business using ontological and architecture approaches.
This book gathers the best papers presented at the first conference held by the Russian chapter of the Association for Information Systems (AIS). It shares the latest insights into various aspects of the digitalization of the economy and the consequences of transformation in public administration, business and public life. Integrating a broad range of analytical perspectives, including economic, social and, technological, this interdisciplinary book is particularly relevant for scientists, digital technology users, companies and public institutions.
We investigate the evolutionary model with recombination and random switches in the fitness function due to change in a special gene. The dynamical behaviour of the fitness landscape induced by the specific mutations is closely related to the mutator phenomenon, which, together with recombination, plays an important role in modern evolutionary studies. It is of great interest to develop classical quasispecies models towards better compliance with the observation. However, these properties significantly increase the complexity of the mathematical models. In this paper, we consider symmetric fitness landscapes for several different environments, using the Hamilton-Jacobi equation (HJE) method to solve the system of equations at a large genome length limit. The mean fitness and surplus are calculated explicitly for the steady-state, and the relevance of the analytical results is supported by numerical simulation. We consider the most general case of two landscapes with any values of mutation and recombination rates (three independent parameters). The exact solution of evolutionary dynamics is done via a solution of a fourth-order algebraic equation. For the more straightforward case with two independent parameters, we derive the solution using a quadratic algebraic equation. For the simplest case, when there are two landscapes with the same mutation and recombination rates, we derive some effective fitness landscape, mapping the model with recombination to the Crow-Kimura model.
The importance of functional differential equations of pointwise type is determined by the fact that their solutions are used to construct traveling-wave solutions for induced infinite-dimensional ordinary differential equations, and vice versa. Solutions of such equations exhibit bifurcation. A theorem on branching bifurcation is obtained for the solution to a linear homogeneous functional differential equation of pointwise type.
This paper proposes a model of the impact of technology on the standard of living based on fuzzy linear regression. The Human Development Index (HDI) was chosen as a dependent variable as an indicator of the health and well-being of the population. The explanatory variables are the Network Readiness Index (NRI), which measures the impact of information and communication technologies (ICT) on society and the development of the nation, and the Global Innovation Index (GII), which measures the driving forces of economic growth. The analysis is based on data for 2019 for four groups of countries with different levels of GDP per capita. For developed countries, the positive and balanced impact of innovation and ICT on living standards has been confirmed. For two groups of developing countries (upper and lower middle income), the GII coefficient was found to be negative. A more in-depth analysis showed that this is due to the state of political and social institutions. This fact means that without a simultaneous increase in the maturity of institutions, stimulation of other areas of innovative development (education, knowledge and technology, infrastructure) leads to a decrease in the quality of life.
Due to the tremendous increase in the number of wearable devices and proximity-based services, the need for improved indoor localization techniques becomes more significant. The evolution of the positioning from a hardware perspective is pacing its way along with various software-based approaches also powered by Machine Learning (ML). In this paper, we apply ML algorithms to the real-life collected signal parameters in an indoor localization system based on Ultra-Wideband (UWB) technology to make an analysis of the signal and classify it accordingly. The contribution aims to answer the question of whether an indoor positioning system could benefit from utilizing ML for signal parameter analysis in order to increase its location accuracy, reliability, and robustness across various environments. To this end, we compare different applications of ML approaches and detail the trade-off between computational speed and accuracy
Abstract: This article is devoted to econometric analysis of the results of experiments conducted with two
agent-based models, which describe the movement of ground vehicles. There are two types of road users in
these models: manned ground vehicles (MGV) and unmanned ground vehicles (UGV). In the first model, the
main difference between UGV and MGV is an ability to exchange massages between UGV for transmitting
information about extreme situations, which allows them to adjust speed and direction of movement. In the
second model, in addition to the above differences, UGV have an additional advantage, namely, the ability to
intelligently assess density of traffic flow for efficient maneuvering. In these models, at a given roundabout,
traffic characteristics such as output stream traffic and the number of traffic accidents are analyzed. The main
task of the econometric analysis is to study dependence of these traffic characteristics on the model parameters
such as average vehicle speed, input flow rate, message exchange rate between UGV, and the impact of the
effect obtained from the implementation into UGV ability of intelligent estimation of traffic flow density.