Integration Of Information Flow Research Articles

COTTON-YOLO: Enhancing Cotton Boll Detection and Counting in Complex Environmental Conditions Using an Advanced YOLO Model

This study aims to enhance the detection accuracy and efficiency of cotton bolls in complex natural environments. Addressing the limitations of traditional methods, we developed an automated detection system based on computer vision, designed to optimize performance under variable lighting and weather conditions. We introduced COTTON-YOLO, an improved model based on YOLOv8n, incorporating specific algorithmic optimizations and data augmentation techniques. Key innovations include the C2F-CBAM module to boost feature recognition capabilities, the Gold-YOLO neck structure for enhanced information flow and feature integration, and the WIoU loss function to improve bounding box precision. These advancements significantly enhance the model’s environmental adaptability and detection precision. Comparative experiments with the baseline YOLOv8 model demonstrated substantial performance improvements with COTTON-YOLO, particularly a 10.3% increase in the AP50 metric, validating its superiority in accuracy. Additionally, COTTON-YOLO showed efficient real-time processing capabilities and a low false detection rate in field tests. The model’s performance in static and dynamic counting scenarios was assessed, showing high accuracy in static cotton boll counting and effective tracking of cotton bolls in video sequences using the ByteTrack algorithm, maintaining low false detections and ID switch rates even in complex backgrounds.

Conceptual Framework of Information Flow Synchronization Throughout the Building Lifecycle

The construction industry’s reliance on traditional methods and fragmented workflows results in significant information loss, inefficiencies, increased costs, and errors. This study addresses these issues by integrating comprehensive urban planning with building information modeling (BIM) to create a seamless information flow throughout the building lifecycle. We propose a holistic framework that synchronizes data from planning to demolition, incorporating national and municipal digital twins. An imperative literature review and analysis of international best practices were conducted to develop a conceptual framework aimed at improving data accuracy and interoperability. Our findings underscore the importance of adopting open standards such as Industry Foundation Classes (IFC) and CityGML for effective information exchange. By implementing an information model (IM)-based approach in urban planning and public sector permit processes, project timelines can be streamlined, and regulatory compliance enhanced. This study concludes that continuous, integrated information flow facilitates more efficient, cost-effective construction practices and improved decision-making. Furthermore, this research illustrates the potential of digital twin technology to revolutionize the construction industry by enabling real-time data integration and fostering stakeholder collaboration, ultimately offering a robust framework for practitioners, and significantly enhancing the efficiency and accuracy of construction processes.

Rapid construction method of emergency material supply chain based on shared platform covering market resources

In the early stages of emergency response, access to a robust information flow and sufficient materials is the basis for ensuring the efficient operation of the emergency material supply chain. Based on a summary of the structure and operational processes of the emergency material supply chain, this paper analyses the key elements and sources of supply chain information flow and proposes a method for integrating information flow based on shared platform. First, basic disaster information and multiscale population data are combined to determine the distribution of the affected population or shelters, and the demand assessment is completed. Then, the market material supply source information is mined, the national reserve information is integrated, and the quantity and distribution of supply sources are analysed. Finally, traffic information is obtained by calling on the real-time Amap API, the supply and demand of emergency materials are matched, and a reasonable emergency material dispatching plan is proposed. This paper considers both historical and recent cases, with case applications to the Lushan County, Ya 'an City, Sichuan Province MS7.0 earthquake; and the 2023 MW7.8 and 7.5 Turkey earthquakes, among others. The results verify that the proposed method can effectively integrate and construct the information flow of the emergency material supply chain and that the rational use of market resources can compensate for material shortages. The information flow and material flow of the emergency material supply chain can thus be maximized, indirectly reducing the losses caused by disaster emergencies.

Secure Instruction and Data-Level Information Flow Tracking Model for RISC-V

With the proliferation of electronic devices, third-party intellectual property (3PIP) integration in the supply chain of the semiconductor industry and untrusted actors/fields have raised hardware security concerns that enable potential attacks, such as unauthorized access to data, fault injection and privacy invasion. Different security techniques have been proposed to provide resilience to secure devices from potential vulnerabilities; however, no one technique can be applied as an overarching solution. We propose an integrated Information Flow Tracking (IFT) technique to enable runtime security to protect system integrity by tracking the flow of data from untrusted communication channels. Existing hardware-based IFT schemes are either fine-, which are resource-intensive, or coarse-grained models, which have minimal precision logic, providing either control-flow or data-flow integrity. No current security model provides multi-granularity due to the difficulty in balancing both the flexibility and hardware overheads at the same time. This study proposes a multi-level granularity IFT model that integrates a hardware-based IFT technique with a gate-level-based IFT (GLIFT) technique, along with flexibility, for better precision and assessments. Translation from the instruction level to the data level is based on module instantiation with security-critical data for accurate information flow behaviors without any false conservative flows. A simulation-based IFT model is demonstrated, which translates the architecture-specific extensions into a compiler-specific simulation model with toolchain extensions for Reduced Instruction Set Architecture (RISC-V) to verify the security extensions. This approach provides better precision logic by enhancing the tagged mechanism with 1-bit tags and implementing an optimized shadow logic that eliminates the area overhead by tracking the data for only security-critical modules.

Effects of visual-electrotactile stimulation feedback on brain functional connectivity during motor imagery practice

The use of neurofeedback is an important aspect of effective motor rehabilitation as it offers real-time sensory information to promote neuroplasticity. However, there is still limited knowledge about how the brain’s functional networks reorganize in response to such feedback. To address this gap, this study investigates the reorganization of the brain network during motor imagery tasks when subject to visual stimulation or visual-electrotactile stimulation feedback. This study can provide healthcare professionals with a deeper understanding of the changes in the brain network and help develop successful treatment approaches for brain–computer interface-based motor rehabilitation applications. We examine individual edges, nodes, and the entire network, and use the minimum spanning tree algorithm to construct a brain network representation using a functional connectivity matrix. Furthermore, graph analysis is used to detect significant features in the brain network that might arise in response to the feedback. Additionally, we investigate the power distribution of brain activation patterns using power spectral analysis and evaluate the motor imagery performance based on the classification accuracy. The results showed that the visual and visual-electrotactile stimulation feedback induced subject-specific changes in brain activation patterns and network reorganization in the alpha band. Thus, the visual-electrotactile stimulation feedback significantly improved the integration of information flow between brain regions associated with motor-related commands and higher-level cognitive functions, while reducing cognitive workload in the sensory areas of the brain and promoting positive emotions. Despite these promising results, neither neurofeedback modality resulted in a significant improvement in classification accuracy, compared with the absence of feedback. These findings indicate that multimodal neurofeedback can modulate imagery-mediated rehabilitation by enhancing motor-cognitive communication and reducing cognitive effort. In future interventions, incorporating this technique to ease cognitive demands for participants could be crucial for maintaining their motivation to engage in rehabilitation.

Design and evaluation of a Mobile-Based decision support system to enhance lung transplant candidate assessment and management: knowledge translation integrated with clinical workflow

BackgroundAccurate and timely decision-making in lung transplantation (LTx) programs is critical. The main objective of this study was to develop a mobile-based evidence-based clinical decision support system (CDSS) to enhance the management of lung transplant candidates.MethodAn iterative participatory software development process was employed to develop the ImamLTx CDSS. This study was accomplished in three phases. First, required data and standard clinical workflow were identified according to the literature review and expert consensus. Second, a rule-based knowledge-based CDSS application was developed. In the third phase, this CDSS was evaluated. The evaluation was done using the standard Post-Study System Usability Questionnaire (PSSUQ 18.3) and ten usability heuristics factors for user interface design.ResultsAccording to expert consensus, fifty-five data items were identified as essential data sets using the Content Validity Ratio (CVR) formula. By integrating information flow in clinical practices with clinical protocols, more than 450 rules and 500 knowledge statements were extracted. This CDSS provides clinical decision support on an Android platform regarding inclusion and exclusion referral criteria, optimum transplant time based on the type of lung disease, findings of initial assessment, and the overall evaluation of lung transplant candidates. Evaluation results showed high usability ratings due to the fact provided accuracy and sensitivity of this lung transplant CDSS with the information quality domain receiving the highest score (6.305 from 7).ConclusionThrough a stepwise approach, the ImamLTx CDSS was developed to provide LTx programs with timely patient data access via a mobile platform. Our results suggest integration with existing workflow to support clinical decision-making and provide patient-specific recommendations.

Application Research of Computer Artificial Intelligence Technology in Urban Smart Logistics System

This paper proposes the application objective and path of intelligent logistics. Urban smart logistics system is constructed by digitization of underlying elements, modeling of intermediate operations and intelligent management decision-making system. The system in the standardized logistics mode to promote the supply side and the demand side of the maximum degree of fit. Then this paper takes e-commerce platform, e-logistics platform and e-government platform as the basic framework to build an intelligent logistics system composed of supply subsystem, demand subsystem and supervision subsystem. Finally, the system realizes the effective integration of information flow, business flow, logistics and capital flow in the operation process of intelligent logistics mode based on big data cloud computing. Finally, this paper analyzes the practical application of intelligent logistics in the field of intelligent management and decision making. Experimental simulation shows that the logistics system can improve the efficiency of scheduling, transportation and other processes.

The effects of digital transformation on supply chain resilience: a moderated and mediated model

PurposeThis study aims to investigate the impact of digital transformation on supply chain resilience. Additionally, the paper examines the mediating effect of supply chain process integration as well as the moderating effect of environmental uncertainty in the relationship between digital transformation and supply chain resilience.Design/methodology/approachDrawing on digital empowerment theory, this study proposes a theoretical model. Using survey data collected from 216 enterprises in China, the study employs structural equation modeling to validate the theoretical model.FindingsThe results reveal that digital transformation has a significant impact on supply chain resilience. Three dimensions of supply chain process integration, namely, information flow integration, physical flow integration, and financial flow integration mediate the relationship between digital transformation and supply chain resilience. In addition, environmental uncertainty including market uncertainty and technology uncertainty positively moderates the relationship between digital transformation and supply chain resilience.Originality/valueFirst, this paper provides empirical evidence on both the direct and indirect effects of digital transformation on supply chain resilience. Second, this paper enriches the understanding of how supply chain integration impacts supply chain resilience in the digital transformation era by adopting a more granular perspective of process integration rather than broad external and internal integrations. Furthermore, this paper extends the knowledge of the role of external environment in digital transformation and supply chain risk management by examining the moderating effects of market uncertainty and technology uncertainty.

Role of reinforcement learning for risk-based robust control of cyber-physical energy systems.

Critical infrastructures such as cyber-physical energy systems (CPS-E) integrate information flow and physical operations that are vulnerable to natural and targeted failures. Safe, secure, and reliable operation and control of CPS-E is critical to ensure societal well-being and economic prosperity. Automated control is key for real-time operations and may be mathematically cast as a sequential decision-making problem under uncertainty. Emergence of data-driven techniques for decision making under uncertainty, such as reinforcement learning (RL), have led to promising advances for addressing sequential decision-making problems for risk-based robust CPS-E control. However, existing research challenges include understanding the applicability of RL methods across diverse CPS-E applications, addressing the effect of risk preferences across multiple RL methods, and development of open-source domain-aware simulation environments for RL experimentation within a CPS-E context. This article systematically analyzes the applicability of four types of RL methods (model-free, model-based, hybrid model-free and model-based, and hierarchical) for risk-based robust CPS-E control. Problem features and solution stability for the RL methods are also discussed. We demonstrate and compare the performance of multiple RL methods under different risk specifications (risk-averse, risk-neutral, and risk-seeking) through the development and application of an open-source simulation environment. Motivating numerical simulation examples include representative single-zone and multizone building control use cases. Finally, six key insights for future research and broader adoption of RL methods are identified, with specific emphasis on problem features, algorithmic explainability, and solution stability.

Research on the Intelligent manufacturing Path of Equipment Manufacturing Enterprises Based on Factor Load Matrix Model

To explore measures for promoting the competitive power of the Liaoning equipment manufacturing industry and identifying development opportunities for realizing intelligent manufacturing, this study focuses on Liaoning equipment manufacturing enterprises. Ten investigation variables are designed across six dimensions, including the foundations of intelligent manufacturing, vertical information flow integration, enterprise-manufacturer relationships, partner value stream, industrial standardization system, and government-scientific research institution collaboration. A comprehensive survey and data collection using questionnaires are conducted. The survey data is analyzed using logical analysis to construct a membership function model, and the confidence and reliability of the data are tested using the KMO and Bartlett coefficient models. Principal component analysis is then used to construct a factor load matrix model to identify key influencing factors for realizing intelligent manufacturing. Based on the findings, countermeasures and suggestions are proposed to promote the development of Liaoning equipment manufacturing enterprises towards information, digital, and intelligent dimensions.

Lung Cancer Patients' Conceptualization of Care Coordination in Selected Public Health Facilities of KwaZulu-Natal, South Africa.

Cancer patients commonly receive care, including comprehensive treatment options, from multiple specialists within and across facilities offering varying levels of care. Given this multi-layered approach to cancer care, there is a need for coordinated care enhanced through integrated information flow for optimal patient care and improved health outcomes. This study aimed to explore how patients conceptualized cancer care coordination in an integrated health care system in KwaZulu-Natal. The study employed a grounded theory design to qualitatively explore the patients' experiences and views on cancer care coordination using in-depth interviews. Guided by the grounded theory principles, data generation and analysis were conducted iteratively, followed by systematic thematic analysis to organize data, and review and interpret comprehensive findings. This process culminated in the development of themes relating to barriers to cancer care coordination and the interface between the primary and tertiary settings. Theoretical saturation was achieved at 21 in-depth interviews with consenting respondents. This study revealed that care coordination was affected by multilevel challenges, including pertinent health system-level factors, such as difficulty accessing specialty care timeously, weak communication between patients and healthcare providers, and unmet needs concerning supportive care. We found that negative experiences with cancer care erode patient trust and receptiveness to cancer care, and patients advocated for better and proactive coordination amongst different care facilities, services, and providers. An integrated care coordination setup is essential to create and sustain a high-performance health care system. These findings make a case for developing, implementing, and evaluating interventions to enhance the quality of cancer care for patients and ultimately improve health outcomes for patients in KwaZulu-Natal. This study will provide comprehensive data to inform professionals, policymakers, and related decisionmakers to manage and improve cancer care coordination.

Failure Case Studies and Challenges in ERP Integration

The 21st century recognizes the importance of information as a vital resource, so it is studied, researched, and improved to accomplish the performance objectives set by an organization. To sustain cohesive business reliability and growth, information sharing and communication are essential. enterprise resource planning (ERP) software integration is seen by many organizations as a way to manage their integrated information flow more effectively under increased competitive pressure. The implementation of such a project for a company is very beneficial because it revolves around performance and formality, both of which are highly important and complex for organizations. However, due to the ERP project implementation budget, time, and resources, this type of project is tricky. In this context, and based on the principle that people learn more from failures than from successes, the authors are interested in this article about the difficulties, the stakes, and the causes of ERPs integration failures reported in the literature to make recommendations and learn from these case studies.

Managing information flows about material resources in the context of the development of intelligent agriculture

model of material resources management, taking into account the importance of the hierarchy of structuring information flows, in which the conditions for the gradual development of intelligent agriculture are laid down. The proposals include: multi­module software for internal and external integrated information flows of data on material resources and costs; a single digital platform of opera tional data with an expanded list of information on suppliers, the ability to search for seeds, agrochemical products, spare parts with specified parameters; data structuring schemes in the context of improving the efficiency of material resources management in the agroindustrial complex; the structure of the creation of the Republican Statistical and Analytical Center, etc.

A blockchain and IoT-based lightweight framework for enabling information transparency in supply chain finance

Supply Chain Finance (SCF) refers to the financial service in which banks rely on core enterprises to manage the capital flow and logistics of upstream and downstream enterprises. SCF adopts a self-testing and closed-loop credit model to control funds and risks. The key factor in a successful SCF service is the deployment of SCF business-oriented information systems that allow businesses to form partnerships efficiently and expedite cash flows throughout the supply chain. Blockchain Technology (BCT), featuring decentralization, tamper-proofing, traceability, which is usually paired with the Internet of Things (IoT) in real-world contexts, has been widely adopted in the field of finance and is perfectly positioned to facilitate innovative collaborations among participants in supply chain networks. In this paper, we propose a BCT and IoT-based information management framework (named BC4Regu), which works as the regulatory to improve the information transparency in the business process of SCF. With BC4Regu, the operation cost of the whole supply chain can be significantly reduced through the coordination and integration of capital flow, information flow, logistics and trade flow in the supply chain. The contributions in this paper include: (1) proposing a novel information management framework which leverages Blockchain and IoT to solve the problem of information asymmetry in the trade of SCF; (2) proposing the technical design of BC4Regu, including the Blockchain infrastructure, distributed ledger-based integrated data flow service, and reshaped SCF process; and (3) applying BC4Regu to a group of scenarios and conducting theoretical analysis by introducing the principal-agent model to validate the BC4Regu.

Breathing coordinates cortico-hippocampal dynamics in mice during offline states

Network dynamics have been proposed as a mechanistic substrate for the information transfer across cortical and hippocampal circuits. However, little is known about the mechanisms that synchronize and coordinate these processes across widespread brain regions during offline states. Here we address the hypothesis that breathing acts as an oscillatory pacemaker, persistently coupling distributed brain circuit dynamics. Using large-scale recordings from a number of cortical and subcortical brain regions in behaving mice, we uncover the presence of an intracerebral respiratory corollary discharge, that modulates neural activity across these circuits. During offline states, the respiratory modulation underlies the coupling of hippocampal sharp-wave ripples and cortical DOWN/UP state transitions, which mediates systems memory consolidation. These results highlight breathing, a perennial brain rhythm, as an oscillatory scaffold for the functional coordination of the limbic circuit that supports the segregation and integration of information flow across neuronal networks during offline states.

Integration of Data Flows of the Construction Project Life Cycle to Create a Digital Enterprise Based on Building Information Modeling

This study is devoted to the problem of digital transformation in the construction industry. To solve the proposed emerging approach for integrating information flows based on Building information modeling (BIM). This research describes the advanced methodology for creating a unified information model in construction, which combines architectural, design, engineering, cost and depreciation models of a construction object, raises a number of problems of reorganizing business processes at enterprises. The novelty of the research lies in the model of adapted common data environment based on life cycle of a construction project by stages of implementation and emergence of information. The proposed architecture is a platform for intelligent parametric modeling as intelligent CAD, which allows linking all elements of the model "Digital Enterprise".The result of the study is a conceptual mechanism for improving the efficiency of enterprise management processes based on the architectural approach in terms of digitalization of construction. Emerging technology in the study is the architecture of the intelligent decision support system in construction based on 5D BIMtechnology and Digital Enterprise. The main advantage of the proposed approach is the possibility of multiple reuse of information by all participants in the development and implementation of an investment and construction project without changes and distortions of data about the construction object. Keywords — Building Information Modeling, BIM, Digital Enterprise, Common Data Environment, life cycle of construction project, digitalization of construction

Intelligent perception of power grid condition

In face of increasingly severe challenges in energy development, a secure, stable, green, and sustainable Energy Internet has become a development tendency, as a major technological innovation that promotes the transformation of the world's power and energy systems. The essence of Energy Internet is the deep integration of energy flow and information flow, which is the in-depth perception and control of the energy network situation using advanced information technology. Electric energy equipment and its grid structure are the core of Energy Internet. Energy Internet needs to integrate all the information that can be collected to perceive the situation of power equipment and the entire grid deeply and accurately, so as to prevent sudden power outages caused by equipment failure or system instability, and to improve the reliability and economy of the entire Energy Internet. In the past few decades, a large number of fruitful research studies have been carried out on the electric energy equipment and grid state perception, which has effectively promoted the progress of grid situation perception technology. However, it still cannot meet the requirements of the future Energy Internet for in-depth and extensive perception of information. With the rapid development of latest material devices, sensing chips, microelectronics, 5G communications, artificial intelligence and digital twin technologies for situation perception, new and powerful momentum has been injected into the development of grid situation perception and the Energy Internet. This issue focuses on ‘Intelligent Perception of Power Grid Condition’, which was completed by well-known experts in the field of grid status monitoring for nearly a year. Four review papers summarise the latest research progress from the perspectives of intelligent perception of large-scale interconnected grid situation, intelligent perception of distribution network, intelligent perception of high-voltage and high-power electronic equipment and gas-insulated equipment condition, especially the deep application of artificial intelligence and new sensing technology in these fields. The future power grid will have the ‘double high’ characteristics of ‘a high proportion of grid-connected renewable energy’ and ‘a high proportion of grid-connected power electronic equipment’. Coupled with the strategic orientation of ‘carbon neutrality’, as well as the rapid progress of sensing technology, communication technology and intelligent electric equipment, it will bring huge opportunities for the improvement of the observability, controllability and intelligence of the grid. Intelligent perception of the grid situation, as an inevitable trend of development of power grid and Energy Internet in the future, is believed to receive more extensive attention and continuous investment. We hope that the topic of this issue can produce an intense spark of thought among the electric science and technology workers and promote the vigorous development of intelligent perception of grid situation. Finally, we would like to express our sincere thanks to Dr. Zheng Xiao, Ms. Yangyang Yu and Ms. Muchen Ran from ‘High Voltage’ editorial department for their full support in the planning, manuscript invitation, review and editing of this topic. It is their efficient and enthusiastic work that makes the topic available to readers as scheduled. Ju Tang, Professor of Wuhan University. He is involved in the online monitoring and fault diagnosis of HV gas insulated electric equipment insulation. Fuping Zeng, Associate Professor of Wuhan University. He is involved in the online monitoring and fault diagnosis of HV gas insulated electric equipment insulation.

Partial information decomposition reveals that synergistic neural integration is greater downstream of recurrent information flow in organotypic cortical cultures.

The directionality of network information flow dictates how networks process information. A central component of information processing in both biological and artificial neural networks is their ability to perform synergistic integration–a type of computation. We established previously that synergistic integration varies directly with the strength of feedforward information flow. However, the relationships between both recurrent and feedback information flow and synergistic integration remain unknown. To address this, we analyzed the spiking activity of hundreds of neurons in organotypic cultures of mouse cortex. We asked how empirically observed synergistic integration–determined from partial information decomposition–varied with local functional network structure that was categorized into motifs with varying recurrent and feedback information flow. We found that synergistic integration was elevated in motifs with greater recurrent information flow beyond that expected from the local feedforward information flow. Feedback information flow was interrelated with feedforward information flow and was associated with decreased synergistic integration. Our results indicate that synergistic integration is distinctly influenced by the directionality of local information flow.

Controlling in the management of a regional power grid company

The tasks of improving the methods and tools of strategic enterprise management determine the relevance of including the controlling mechanism in the process of managing the enterprise's activities. The purpose of the paper is to substantiate the need to use controlling in the process of strategic enterprise management. The authors use general logical methods and research techniques: a systematic approach, generalization, analysis and synthesis. The paper notes the plurality of views of management theorists and practitioners about the essence of controlling and discrepancies in its functional purpose, which entail the problems of organizing and effectively using the controlling mechanism in enterprise management. In this regard, the authors emphasize the view of controlling as a function of strategic management, systematize the list of implemented controlling tasks and factors that predetermine them, indicate the special role and place of controlling as a kind of tool in the analytical mechanism of self-assessment of the effectiveness of the goal-setting and goal-implementing function of enterprise management, the action of which (i.e. tool) is possible with an adjusted feedback model in the control loop. The prerequisites have been identified and a model for the consistent development of controlling in enterprise management has been tested in relation to the object of research - the regional power grid company PJSC “IDGC of North-West”. The result of the first stage of mastering the controlling functionality is the ability to partially relieve the top management of the company from operational and technical issues in favor of preparing and implementing management decisions of a strategic focus and content. The spectrum of orientation of the controlling functional in the management of PJSC “IDGC of North-West” towards achieving tactical and strategic goals for the long-term existence and development of the company is shown. Prospects for further research into the problem of organizing effective controlling are in the development of methodological support for the process of integrating information flows using digital technologies. Scientific novelty lies in the presentation of controlling as a management function that ensures the company’s activities in accordance with plans in the interests of achieving established goals.

Цифровой менеджмент в деятельности российских политических партий: проблемное поле управления информационными потоками в социальных медиа

The article is devoted to the analysis of digital management of political parties and proto-party network actors in modern Russia. The author implements an analysis of the Internet practice of eight Russian parliamentary and non-parliamentary political parties, as well as proto-party network actors («United Russia», KPRF, LDPR, «Fair Russia», «Yabloko», Party of Growth, Libertarian Party Russia, «Russia of the Future») in April – June 2020, i.e. at a time when communications with electoral groups in the real environment were extremely limited. The calculation of the Digital Management Index of Russian political parties and pro-party network actors was carried out on the basis of comparing the following indicators: the development of the digital infrastructure; the intersection of offline and online networks; management of information flow in social media; personcentricity of information flow in social media; informality of information flow in social media; integration of information flow in social media; correspondence of the localization of the information flow to the target audience. A high level of potential to scale up electoral support through online communication with target audiences demonstrates the party «Russia of the Future». Most of the units of analysis («United Russia», KPRF, LDPR, LPR) fell into the second segment with an average level of adaptation to limiting communications in the real environment. «Fair Russia», «Yabloko» and Party of Growth demonstrated a low level of adaptation.