Detection the precursors of critical transitions in complex systems is one of the most difficult and still unsolved problems. This problem has not received a final solution, not only for real complex systems, but also for model systems capable to self-organize into the critical state. The presented paper is devoted to early detection of time moments of self-organized critical transitions in cellular automata as a result of the analysis of the time series they generate for a number of grains falling from the grid. It was found that cumulative moments of probability distribution and cumulative scaling exponents are quite informative indicators for early detection of critical transitions. General features of the behavior of indicators when approaching a critical point are established for the time series generated by cellular automata with different rules.
This book gathers the papers on digitalization of society, economics and management in post-pandemic period. 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.
The purpose of this paper is to address issues related to better identification of strategic orientation of the firm and the impact of strategic orientation on sustainable development of the firm. The paper presents an overview of the existing literature on strategic orientation of the firm, reexamines the major findings and fills the discovered gaps in theoretical constructs and models by new models. In this paper a new model of strategic orientation is proposed based on the type of relationship of a firm with its stakeholders who are considered as suppliers of key strategic resources. Relationship between the firm and its particular stakeholder is presented on an input-output like scheme and the variants of the position of the firm towards all its stakeholders serve as foundation for determining strategic orientation types. Next we present orientation of firms of different strategic types towards sustainability. The paper outlines several novels problems for strategic management and organizational design theory. The paper provides a novel treatment of strategic orientation and particular strategic types.
Strategic transformation and logistic integration in supply chain management requires systematic strategic supply chain modeling, and modern simulation provides such opportunities for analysis and synthesis of efficient and integrated supply chains. Authors suggest a method of constructing and analysis of conceptual supply chain models. The following base levels of the supply chain representation are considered: object-based, configuration/network-based, process-based, and logistics coordination levels. A general simulation model of integrative supply chain is proposed based on technologies of hybrid process-and-agent-based simulation modeling. Literature review on simulation modeling application for integrative and collaborative supply chains is presented. Iterative simulation and optimization procedures for complex analysis and optimization of supply chains are proposed. The suggested approaches and techniques were tested in the case of strategic transformation of supply chain. Authors present and interpret the results of supply chain optimization и simulation modeling for a set of scenarios of logistic processes transformation and inventory management policies, interorganizational coordination mechanisms and related technological solutions.
properly built risk assessment process could help to significantly reduce the overall level of a project uncertainty, which in turn will have a positive impact on the project outcome. Based on recommendation given in BABOK® Guide, a combined procedure for analysis of risks is built up, which allows performing risk assessment within the framework of the overall risk management process. The main groups of risks classified by their main origin are identified related to a reference IT-project applied in financial sphere. This makes the proposed procedure to be aimed at detailed risk analysiswhere not only the qualitative but also the quantitative measure of the risks, i.e., their probability and gravity of their consequences can be implemented. The process of risk assessment is chosen for the project consisting in creating and deploying a modern corporate data warehouse in a big Russian private bank. The procedure is extended by decision tree, concisely illustrating risk decomposition, which open the way to highly predictable further risk management process. The short feedback replies given by main stakeholders at the end of first stage are also presented.
The issue contains abstracts of the 32nd World Congress of the International Project Management Association (IPMA). Authors from 25 countries submitted papers in five thematic areas — Project management perspective, Project management practice, Project management people, Project management methodology and approaches, Agility and projects..
With technology evolving rapidly and proliferating, it is imperative to pay attention to mobile devices’ security being currently responsible for various sensitive data processing. This phase is essential as an intermediate before the cloud or distributed ledger storage delivery and should be considered additional care due to its inevitability. This paper analyzes the security mechanisms applied for internal use in the Android OS and the communication between the Android OS and the remote server. Presented work aims to examine these mechanisms and evaluate which cryptographic methods and procedures are most advantageous in terms of energy efficiency derived from execution time. Nonetheless, the dataset with the measurements collected from 17 mobile devices and the code for reproducibility is also provided. After analyzing the collected data, specific cryptographic algorithms are recommended to implement an application that utilizes native cryptographic operations on modern Android devices. In particular, selected algorithms for symmetric encryption are AES256 / GCM / No Padding; for digital signature – SHA512 with RSA2048 / PSS, and for asymmetric encryption – RSA3072 / OAEP with SHA512 and MGF1 Padding.
The objective of this paper is to reveal the main directions of changes in Russian agribusiness caused by the food embargo through the lens of interfirm relationships.
Qualitative research in the form of focus group was conducted. The focus group consisted of 9 participants, representatives of the senior management of Russian agribusinesses.
Findings and implications
The study reveals that the Russian food embargo contributes to the development of interfirm relationships in the Russian agribusiness. Both retailers and manufacturers tend to use a relational approach and to develop interfirm relationships in order to build sustainable value chains and long-term relationships with partners. The resulting conclusions represent important changes in interfirm relationships between different actors of agribusiness: (1) retailers tend to interact with providers toward building sustainable value chains; (2) at the same time the criteria imposed by retailers to their suppliers are gradually changing. The possibility to change the supply conditions plays a significant role as does as the ability to operate in a turbulent environment.
Despite the large number of empirical studies exploring the impact of the embargo from different angles, there is still a lack of research concerning the consequences of embargoes in terms of interfirm relationships. This study extends the literature on the impact of embargoes and fulfills an identified need to study the consequences of embargoes in terms of developing interfirm relationships. This is the first Russian study to empirically examine the impact of the embargo on Russian firms’ interfirm relationships.
−Albert Abubakirovich Galeev is a Soviet and Russian expert in plasma physics who actively contributed to fusion research. In the early 1970s, he became a head of department at the Space Research Institute of the Academy of Sciences of USSR and began devoting most of his time to the problems of the physics of space plasma and made a very important contribution to the solution of many of them, such as physics of collisionless shock waves, the phenomenon of anomalous ionization, processes in the plasma envelopes of comets, and many others. This paper is devoted to only one of the many directions of his work: studies of current sheets and the magnetic reconnection processes that occur therein. Studies of thin current structures is space plasma, whose thickness is about the proton gyroradius, began with the pioneering works of S.I. Syrovatskii, T. Speiser, and other outstanding scientists who proposed that in space plasma, thin boundary current sheets exist, which play the key role in the dynamics of Earth’s magnetosphere and Sun’s corona. The development of these works was dictated by the necessity to explain the solar flares and magnetospheric perturbations during which phases of evolutionary development are replaced by explosive spontaneous processes that release free energy. One of the key physical processes is the magnetic field reconnection, which is realized in nature as a part of the general problem of generation and evolution of current sheets. In a series of works that started in 1975 by the publication (together with L.M. Zelenyi) of the article entitled “Metastable states of a diffuse neutral layer” in JETP letters, A.A. Galeev studied the stability of current sheets to the tearing mode and the dynamics of magnetic reconnection at the boundary of planetary magnetospheres and explained the processes of generation of fast ion flows with energies of several MeV in Earth’s magnetotail. In this paper, we discuss further development of these works that were once initiated by A.A. Galeev. A new model of embedded current sheets is presented, which consists of an internal electron sheet and two external current sheets formed by proton and oxygen ion currents. It is shown that the free energy of such embedded structure in the corresponding range of parameters substantially exceeds the free energy of the well-known Harris’s configuration. This allows one to simultaneously explain their stability (up to a certain limit) and destabilization when the current sheet parameters reach certain critical values, which leads to the change of topology of magnetic field and start of magnetic reconnection.
Anomaly detection in pedestrian walkways is an important research topic, commonly used to improve the safety of pedestrians. Due to the wide utilization of video surveillance systems and the increased quantity of captured videos, the traditional manual examination of labeling abnormal events is a tiresome task. So, an automated surveillance system that detects anomalies becomes essential among computer vision researchers. Presently, the development of deep learning (DL) models has gained significant interest in different computer vision processes namely object classification and object detection, and these applications were depending on supervised learning that required labels. Therefore, this paper develops an automated deep learning based anomaly detection technique in pedestrian walkways (DLADT-PW) for vulnerable road user's safety. The goal of the DLADT-PW model is to detect and classify the various anomalies that exist in the pedestrian walkways such as cars, skating, jeep, etc. The DLADT-PW model involves preprocessing as the primary step, which is applied for removing the noise and raise the quality of the image. In addition, mask region convolutional neural network (Mask-RCNN) with densely connected networks (DenseNet) model is employed for the detection process. To ensure the better anomaly detection performance of the DLADT-PW technique, an extensive set of simulations were performed and the outcomes are investigated under distinct aspects. The obtained experimental values confirmed the superior characteristics of the DLADT-PW technique by achieving a maximum detection accuracy.
Financial risk assessment (FRA) is an essential process in financial institutions determining a company’s creditworthiness. This paper introduces a new wrapper feature selection with a clustering-based FRA model to assess the financial status. This study involves three different phases of operations such as feature selection, clustering, and classification. The proposed model initially designs an Information Gain Directed Feature Selection algorithm that offers to rank to the features utilising the information gain. In addition, the proposed model also involves an improved K-means clustering technique to cluster the data. Finally, the gradient boosting tree classifier model is executed to perform the classification process. The proposed model tested using two benchmark datasets. The simulation results indicate that the projected FRA model obtains maximum accuracy values of 95.68% and 94.76% on the applied datasets.
In recent times, the utilization of autonomous vehicles (AVs) has been significantly increased over the globe. It is because of the tremendous rise in familiarity and the usage of artificial intelligence approaches in distinct application areas. Though AVs offer several benefits like congestion control, accident prevention, and so on, energy management and traffic flow prediction (TFP) remain a challenging issue. This paper concentrates on the design of intelligent energy management and TFP (IEMTFP) technique for AVs using multi-objective reinforced whale optimization algorithm (RWOA) and deep learning (DL). The proposed model involves an energy management module using fuzzy logic system to reach the specified engine torque with respect to different measures. For optimal tuning of the variables involved in the fuzzy logic membership functions (MFs), RWOA is employed to further reduce the energy utilization. Besides, the proposed model uses a DL-based bidirectional long short-term memory (Bi-LSTM) technique to perform TFP. For validating the efficacy of the IEMTFP technique, an extensive experimental validation is carried out. The resultant values ensured the goodness of the IEMTFP model in terms of energy management and TFP.
We analyze how artificial intelligence changes a significant part of the energy sector, the oil and gas industry. We focus on the upstream segment as the most capital-intensive part of oil and gas and the segment of enormous uncertainties to tackle. Basing on the analysis of AI application possibilities and the review of existing applications, we outline the most recent trends in developing AI-based tools and identify their effects on accelerating and de-risking processes in the industry. We investigate AI approaches and algorithms, as well as the role and availability of data in the segment. Further, we discuss the main non-technical challenges that prevent the intensive application of artificial intelligence in the oil and gas industry, related to data, people, and new forms of collaboration. We also outline three possible scenarios of how artificial intelligence will develop in the oil and gas industry and how it may change it in the future (in 5, 10, and 20 years).
Currently, wireless body area networks (WBANs) are developing rapidly. Miniature wearable devices which are light weight and low power consumption can be developed using modern technology. However, the WBANs research challenge is power consumption. This paper provides the two intrabody communication (IBC) methods review. The equivalent body channel models and modeling methods are analyzed. The conventional IBC WBANs architecture is reviewed and novel energy-efficient architecture and an IBC WBANs data transmission technique are proposed. The experiment was conducted in order to measure the BodyCom mobile module output power. The comparative analysis showed that the power consumption of the IBC proposed technique is 7 times lower than Bluetooth LE, and 14 times lower than ZigBee. The proposed technique can be applied in areas such as home and industrial automation, medical appliances, wearable devices, security, and internet of things.
The latest advancements in highway research domain and increase in the number of vehicles everyday led to wider exposure and attention towards the development of efficient Intelligent Transportation System (ITS). One of the popular research areas i.e.,Vehicle License PlateRecognition (VLPR) aims at determining the characters that exist in the license plate of the vehicles. The VLPR process is a difficult one due to the differences in viewpoint, shapes, colors, patterns, and non-uniformillumination at the time of capturing images.The current study develops a robustDeep Learning (DL)-basedVLPR model using Squirrel Search Algorithm (SSA)-based Convolutional Neural Network (CNN), called the SSA-CNN model. The presented technique has a total of four major processes namely preprocessing, License Plate (LP) localization and detection, character segmentation, and recognition. Hough Transform (HT) is applied as a feature extractor and SSA-CNN algorithm is applied for character recognition in LP. The SSA-CNN method effectively recognizes the characters that exist in the segmented image by optimal tuning of CNN parameters. The HT-SSA-CNN model was experimentally validated using the Stanford Car, FZU Car, and HumAIn 2019 Challenge datasets. The experimentation outcome verified that the presented method was better under several aspects. The projected HT-SSA-CNN model implied the best performance with optimal overall accuracy of 0.983%.
The goal of the paper is to develop a new algorithm for predicting whether the company will go bankrupt on the base of unbalanced data. To do it, we propose to consider the classification as a multi-objective optimization problem and construct a prediction model as an ensemble while minimizing the parameters FPR (False Positive Rate) and FNR (False Negative Rate) at the same time. To create the ensemble, the proposed algorithm of a Multi-Objective Classifier Selection (MOCS) selects only classifiers that belong to the Pareto-optimal set in FPR/FNR space; that is, there is no dominance between them, and they satisfy some additional conditions. In the general case, MOCS is determined by three parameters: two threshold values that limit false rates (FNR and FPR), and the crowding distance, which defines the uniqueness of the classifier's results. We tested the proposed algorithm on data collected from 2457 Russian companies, 456 of which went bankrupt, and 5910 Polish companies, 410 of which received bankruptcy status. Datasets contain features such as financial ratios and business environment factors. In the testing, we used more than 70 combinations of under-sampling, over-sampling, and no sampling methods with static and dynamic classification models. Final ensembles include seven classifiers for the Russian dataset and four classifiers for the Polish dataset combined by soft voting rule. In both cases, the proposed algorithm produces a significant improvement of prediction results as in terms of standard metrics (geometric mean, the area under the ROC curve) and in the visual representation in the FNR/FPR space, namely in the shift from a Pareto-optimal set of classifiers.
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.
This book highlights interdisciplinary insights, latest research results, and technological trends in Business Intelligence and Modelling in fields such as: Business Intelligence, Business Transformation, Knowledge Dissemination & Implementation, Modeling for Logistics, Business Informatics, Business Model Innovation, Simulation Modelling, E-Business, Enterprise & Conceptual Modelling, etc. The book is divided into eight sections, grouping emerging marketing technologies together in a close examination of practices, problems and trends. The chapters have been written by researchers and practitioners that demonstrate a special orientation in Strategic Marketing and Business Intelligence. This volume shares their recent contributions to the field and showcases their exchange of insights.
n the spring of 2020, the COVID-19 pandemic created a new reality. Each country has implemented different measures to contain the pandemic, which has had many consequences for society and businesses. The purpose of this paper is to improve understanding of how the COVID-19 pandemic has changed consumer behavior in the BRICS countries and discuss the role of consumer trust and anxiety. A systematic literature review with a bibliometric analysis was carried out to identify research directions and reveal the role of trust and anxiety in consumer behavior. Differences in consumer responses to the COVID-19 pandemic challenges in Brazil, Russia, India, and South Africa were identified based on an analysis of an international database of online surveys. An empirical study of Russian consumers was conducted in the spring of 2020. Cluster and factor analyses were applied to reveal different consumer strategies of coping with the crisis. The study revealed differences in consumer trust and the level of anxiety in the BRICS countries. In the empirical study of Russian consumers, anxiety was identified as one of the factors in changing consumer behavior in response to the COVID-19 pandemic.
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.