Information Flow Analysis Research Articles

Neuronal wiring diagram of an adult brain

Connections between neurons can be mapped by acquiring and analysing electron microscopic brain images. In recent years, this approach has been applied to chunks of brains to reconstruct local connectivity maps that are highly informative1–6, but nevertheless inadequate for understanding brain function more globally. Here we present a neuronal wiring diagram of a whole brain containing 5 × 107 chemical synapses7 between 139,255 neurons reconstructed from an adult female Drosophila melanogaster8,9. The resource also incorporates annotations of cell classes and types, nerves, hemilineages and predictions of neurotransmitter identities10–12. Data products are available for download, programmatic access and interactive browsing and have been made interoperable with other fly data resources. We derive a projectome—a map of projections between regions—from the connectome and report on tracing of synaptic pathways and the analysis of information flow from inputs (sensory and ascending neurons) to outputs (motor, endocrine and descending neurons) across both hemispheres and between the central brain and the optic lobes. Tracing from a subset of photoreceptors to descending motor pathways illustrates how structure can uncover putative circuit mechanisms underlying sensorimotor behaviours. The technologies and open ecosystem reported here set the stage for future large-scale connectome projects in other species.

Analysis of logistics information flows in the supply chain of Africa Global Logistics Burkina Faso (AGL BF)

Analysis of logistics information flows in the supply chain of Africa Global Logistics Burkina Faso (AGL BF)

Comparison of text information from information sources based on the cosine similarity algorithm

This article presents research aimed at developing an optimal concept of analysis and comparison of information sources based on large volumes of textual information using natural language processing (NLP) methods. The object of the study was Telegram news channels, which are used as sources of text data. Texts were pre-processed, including cleaning, tokenization, and lemmatization, to form a global dictionary consisting of unique words from all information sources. For each source, a vector representation of the texts was built, the dimension of which corresponds to the number of unique words in the global dictionary. The frequency of use of each word in the channel's texts was displayed in the corresponding positions of the vector. By applying the cosine similarity algorithm to pairs of vectors, a square matrix was obtained that demonstrates the degree of similarity between different sources. The results of the study show the effectiveness of the proposed approach for quantitative assessment of the similarity of textual data from different sources. The need for further optimization of the algorithm was identified, in particular by parameterization to achieve a balance between accuracy and computational cost, as well as the separation of words with excessive weight, such as specific terms or channel names. The proposed method can be applied to the analysis of information flows, the identification of relationships between sources and the study of the socio-cultural influence of media content in the conditions of the modern information environment.

Disrupted Human-Dog Interbrain Neural Coupling in Autism-Associated Shank3 Mutant Dogs.

Dogs interact with humans effectively and intimately. However, the neural underpinnings for such interspecies social communication are not understood. It is known that interbrain activity coupling, i.e., the synchronization of neural activity between individuals, represents the neural basis of social interactions. Here, previously unknown cross-species interbrain activity coupling in interacting human-dog dyads is reported. By analyzing electroencephalography signals from both dogs and humans, it is found that mutual gaze and petting induce interbrain synchronization in the frontal and parietal regions of the human-dog dyads, respectively. The strength of the synchronization increases with growing familiarity of the human-dog dyad over five days, and the information flow analysis suggests that the human is the leader while the dog is the follower during human-dog interactions. Furthermore, dogs with Shank3 mutations, which represent a promising complementary animal model of autism spectrum disorders (ASD), show a loss of interbrain coupling and reduced attention during human-dog interactions. Such abnormalities are rescued by the psychedelic lysergic acid diethylamide (LSD). The results reveal previously unknown interbrain synchronizations within an interacting human-dog dyad which may underlie the interspecies communication, and suggest a potential of LSD for the amelioration of social impairment in patients with ASD.

A Comprehensive Accounting of Construction Materials in Belt and Road Initiative Projects.

The Belt and Road Initiative (BRI) stands as the most ambitious infrastructure project in history, marked by its scale of investment, extensive geographical reach across continents and countries, and a diverse array of projects from roads to digital networks. While the BRI's environmental sustainability has raised concerns, the impacts of construction materials used in these projects have been overlooked, especially in developing countries. Here, we map and account for the materials embodied in the BRI by integrating, for the first time, official governmental project reports, geographical information, and material flow analysis. We pinpoint and analyze the BRI material stocks in each individual project by material types, countries, regions, and sectors. Between 2008 and 2023, 328 million tons of construction materials have accumulated in 540 BRI projects around the world, mostly in Asia and Africa. Aggregates (sand and gravel) constitute the largest share (82%), followed by cement, steel, and other materials. Most of the materials are used in transportation infrastructure. Our work further highlights some limitations in terms of data quality for such sustainability assessments. By shedding light on the significant impact of BRI projects on raw material usage across the globe, this study sets the stage for further investigations into environmental impacts of BRI and material stock-flow-nexus from perspective of an initiative.

Methods and algorithms of optimization in computer engineering: review and comparative analysis

Introduction: The main areas of application of artificial intelligence for algorithmic analysis and optimization of information flows in tasks of multiparametric diagnostics by means of computer engineering are considered. The issues of globalization of all areas of humanitarian, scientific, technical and engineering activities of human society are considered. It is noted that the common denominator of all directions is information flows. The main tools for their management and algorithmic analysis are multi-parametric methods of artificial intelligence. Methods: One of its most relevant areas has been highlighted - the use of evolutionary algorithms in combination with modern diagnostic systems based on computer engineering. The possibility of using intelligent analysis of data from biophysical laser systems in assessing the state of “living matter” - the biological media of the human body - is considered. Results: Through algorithmic optimization, a set of new cancer detection markers was determined: the statistical parameters of optical anisotropy maps wavelet coefficients linear distributions - the differences between these markers lie in the range from 4 to 20 times; the asymmetry of the wavelet coefficients autocorrelation function - the differences between these markers lie within two orders of magnitude; for normal state, the wavelet coefficients distributions are multifractal; for prostate cancer, the distributions of the wavelet amplitude coefficients are multifractal.Conclusions: A comparative study of the algorithmic optimization of differences of cancer through the use of multiparametric statistical, correlational, fractal and wavelet analysis of polarization tomograms of optical anisotropy of blood layers of donors and prostate cancer sicks is presented

Register transfer level hardware design information flow modeling and security verification method

Information flow analysis can effectively model the security behavior and security properties of hardware design. However, the existing gate level information flow analysis methods cannot deal with large-scale designs due to computing power and verification effectiveness, and the register transfer level (RTL) information flow analysis methods require formal languages to rewrite hardware designs. This paper proposes a RTL hardware design information flow modeling and security verification method. Based on the RTL functional model, this method develops an information flow tracking logical model to model security behavior and security properties of RTL hardware designs from the perspective of information flow. This method can be integrated into EDA flows and uses EDA testing and verification tools to capture security property violations and detect security vulnerabilities based on non-interference security policy. The results on experiments with Trust-Hub hardware Trojan benchmarks show that the proposed method can effectively detect hardware Trojans.

Visual evaluation and information improvement method of smart factory layout based on information value added

Abstract The process of Industry 4.0 is constantly promoted. In order to realize the high-quality connotation development of enterprises, manufacturing enterprises need to carry out information transformation and upgrading. The technology of information flow analysis can consistently assess the worth of the factory, overcome existing technical challenges, and achieve intelligent modernization and enhancement of the system. This study presents a visualization and evaluation of the factory’s layout, production lines, and workstation from three perspectives: macro, meso, and micro. To begin, a value added heat map (VAHM) is created by examining the visualization technique for information value added information (IVA). This analysis aims to assess the extent of information wastage in terms of the factory’s physical space, resources, and staff. Additionally, a method for analyzing the information gain of a production line, which is based on the concept of distributed cognition, is introduced. This method evaluates the production line by integrating the economic principle of information processing (EPIP). Furthermore, when assessing complexity, the volume of information in workstations is measured and enhanced by applying the principle of information processing economy. Ultimately, the method’s validity and feasibility are confirmed by applying it to real-world manufacturing industry scenarios.

Breakdown of effective information flow in disorders of consciousness: Insights from TMS-EEG

BackgroundThe complexity of the neurophysiological mechanisms underlying human consciousness is widely acknowledged, with information processing and flow originating in cortex conceived as a core mechanism of consciousness emergence. Combination of transcranial magnetic stimulation and electroencephalography (TMS-EEG) is considered as a promising technique to understand the effective information flow associated with consciousness. ObjectivesTo investigate information flow with TMS-EEG and its relationship to different consciousness states. MethodsWe applied an effective information flow analysis by combining time-varying multivariate adaptive autoregressive model and adaptive directed transfer function on TMS-EEG data of frontal, motor and parietal cortex in patients with disorder of consciousness (DOC), including 14 vegetative state/unresponsive wakefulness syndrome (VS/UWS) patients, 21 minimally conscious state (MCS) patients, and 22 healthy subjects. ResultsTMS in DOC patients, particularly VS/UWS, induced a significantly weaker effective information flow compared to healthy subjects. The bidirectional directed information flow was lost in DOC patients with TMS of frontal, motor and parietal cortex. The interactive ROI rate of the information flow network induced by TMS of frontal and parietal cortex was significantly lower in VS/UWS than in MCS. The interactive ROI rate correlated with DOC clinical scales. ConclusionsTMS-EEG revealed a physiologically relevant correlation between TMS-induced information flow and levels of consciousness. This suggests that breakdown of effective cortical information flow serves as a viable marker of human consciousness. SignificanceFindings offer a unique perspective on the relevance of information flow in DOC, thus providing a novel way of understanding the physiological basis of human consciousness.

Analysis of information flow security using software implementing business logic based on stored database program blocks

Objectives. Verification of software security is typically performed using dynamic and static analysis tools. The corresponding types of analysis do not usually consider the business logic of the software and do not rely on data access control policies. A modern approach to resolving this problem is to implement language-based information flow control. Despite a large amount of research, mechanisms for information flow control in software are not widely used in practice. This is because they are complex and impose increased demands on developers. The aim of the work is to transfer information flow control from the language level to the level of formal verification. This will enable the functions of controlling data integrity and confidentiality in software to be isolated into a separate task, which can be resolved by information security analysts.Methods. The research is based on general formal security methods for computer systems and formal verification methods. The algorithm developed by the author for checking security specifications and resolving security violations uses temporal logic of actions.Results. The technology is presented as a step-by-step approach to resolving specific tasks, including the following: designing a database (DB) for storing and processing sensitive information; analyzing dependencies and identifying relevant sets of program blocks in the DB; generating TLA+ specifications for the identified program blocks; labeling specifications according to global security policy rules and additional constraints; applying the specification verification algorithm, and resolving security violations while providing recommendations for software developers. The procedure also involves analyzing labeled data, in order to control the spread of verified program block output values in external software modules.Conclusions. The technology presented herein does not require developers to include redundant annotations describing security policy rules. The function of analyzing information flows with reference to predefined access restrictions is moved to a separate stage of the software development life cycle.

Graph Theory and Algorithms for Social Network Analysis

Graph theory and algorithms play a crucial role in the analysis of social networks, which are complex systems composed of individuals or entities connected by various types of relationships. This paper provides an overview of graph theory concepts and algorithms commonly used in social network analysis. It discusses how these tools can be applied to understand the structure and dynamics of social networks, identify key actors or communities, and analyze information flow and influence propagation. The paper also highlights some of the challenges and future directions in the field of social network analysis using graph theory. The study of graph theory and algorithms for social network analysis is of significant importance due to its wide-ranging applications in various fields. Understanding the structure and dynamics of social networks can provide valuable insights into human behavior, information diffusion, organizational dynamics, and the spread of diseases or ideas. By applying graph theory concepts such as centrality measures, community detection algorithms, and link prediction techniques, researchers can uncover hidden patterns, identify influential nodes or groups, and predict future interactions in social networks. This knowledge can be used to design more effective strategies for marketing, public health interventions, social media management, and other areas where understanding and influencing human behavior are critical. Versatile with distinctive options. MOORA is a new optimization approach that has been proposed. This objective method signifies a matrix of potential replies, but it also proposes better policies based on the rates employed. Well established. For comparison, the reference point method is another multi-objective optimization technique. After several competitions, this proved to be the best pick among the methods. From the result network models is in first position whereas Graph density is in 5th position

Promoting the Patriotic Agenda on Social Media among Russian Students

The study aims to compare patriotic attitudes among the social group of student youth, as reflected in public opinion dynamics and social media information flows. The research methodology was based on cognitive and network approaches to examining social processes. The methods of open sociological and statistical data analysis, cognitive content mapping and automated social media analysis were used to determine the content and intensity of the impact of opposing patriotic and anti-patriotic information flows in the Russian segment of social media on the social values and attitudes of student youth. The results of the study revealed a high intensity of information pressure of anti-patriotic information flow in the digital communications environment on the audience of student youth. The sociological data indicates a strong prevalence of patriotic attitudes among the youth, however, analysis of information flows reveals the widespread presence of anti-patriotic narratives and discourses on social networks. The student communities of supporters of the patriotic idea differ from the opponents in that they are much more numerous, but much less active in producing and promoting semantic content in the social media space. The social media segment in Russia lacks a well-developed network infrastructure, including opinion leaders and effective platforms for broadcasting patriotic content. The empirical study of information flows has shown that universities are not on the list of platforms for promoting the patriotic agenda. Russian universities face a significant challenge in implementing patriotic education for the youth due to their technological and ideological detachment from students who are immersed in the digital communication environment. These circumstances reveal institutional issues with implementing the state policy of youth development in the face of existential challenges.

Relative importance of global warming, the IPO, and the AMO in surface air temperature and terrestrial precipitation

AbstractThe relative importance of oceanic modes to global surface air temperature (SAT) and terrestrial precipitation during 1934–2020 was investigated using a variety of statistical and dynamical system methods. Through singular spectrum analysis, we present the distribution of decadal (10–20‐year), multidecadal (20–50‐year), and secular (>50‐year) variabilities of global SAT and terrestrial precipitation anomalies. Three sea surface temperature modes were identified by singular value decomposition that affect the low‐frequency variabilities of global SAT and terrestrial precipitation anomalies—namely, global warming (GW), the Interdecadal Pacific Oscillation (IPO), and the Atlantic Multidecadal Oscillation (AMO). The relative importance GW, the IPO, and the AMO in SAT and terrestrial precipitation was obtained through a normalized multivariate information flow analysis. Besides a high information flow percentage (60%) from GW to global SAT, especially over the northern Indian Ocean and tropical West Pacific, information flow from the AMO to the northwestern Pacific was also found. In terms of terrestrial precipitation, a large area with a wet trend was found over Eurasia at mid‐to‐high latitudes, and this trend was especially remarkable in the boreal winter half‐year (November–April), as compared with that in the boreal summer half‐year (May–October). By employing artificial neutral networks with a self‐organized map to cluster the key patterns of vertically integrated water vapour flux, we found that the synoptic circulation related to the wet trend is characterized by westerly flow that transports water vapour from the northeastern Atlantic to Eurasia, which is favourable for precipitation there both in the boreal winter and summer half‐year.

Mapping clinical interactions in an Australian tertiary hospital emergency department for patients presenting with risk of suicide or self-harm: Network modeling from observational data.

Reliable assessment of suicide and self-harm risk in emergency medicine is critical for effective intervention and treatment of patients affected by mental health disorders. Teams of clinicians face the challenge of rapidly integrating medical history, wide-ranging psychosocial factors, and real-time patient observations to inform diagnosis, treatment, and referral decisions. Patient outcomes therefore depend on the reliable flow of information through networks of clinical staff and information systems. This study aimed to develop a quantitative data-driven research framework for the analysis of information flow in emergency healthcare settings to evaluate clinical practice and operational models for emergency psychiatric care. We deployed 2 observers in a tertiary hospital emergency department during 2018 for a total of 118.5 h to record clinical interactions along patient trajectories for presentations with risk of self-harm or suicide (n = 272 interactions for n = 43 patient trajectories). The study population was reflective of a naturalistic sample of patients presenting to a tertiary emergency department in a metropolitan Australian city. Using the observational data, we constructed a clinical interaction network to model the flow of clinical information at a systems level. Community detection via modularity maximization revealed communities in the network closely aligned with the underlying clinical team structure. The Psychiatric Liaison Nurse (PLN) was identified as the most important agent in the network as quantified by node degree, closeness centrality, and betweenness centrality. Betweenness centrality of the PLN was significantly higher than expected by chance (>95th percentile compared with randomly shuffled networks) and removing the PLN from the network reduced both the global efficiency of the model and the closeness centrality of all doctors. This indicated a potential vulnerability in the system that could negatively impact patient care if the function of the PLN was compromised. We developed an algorithmic strategy to mitigate this risk by targeted strengthening of links between clinical teams using greedy cumulative addition of network edges in the model. Finally, we identified specific interactions along patient trajectories which were most likely to precipitate a psychiatric referral using a machine learning model trained on features from dynamically constructed clinical interaction networks. The main limitation of this study is the use of nonclinical information only (i.e., modeling is based on timing of interactions and agents involved, but not the content or quantity of information transferred during interactions). This study demonstrates a data-driven research framework, new to the best of our knowledge, to assess and reinforce important information pathways that guide clinical decision processes and provide complementary insights for improving clinical practice and operational models in emergency medicine for patients at risk of suicide or self-harm. Our findings suggest that PLNs can play a crucial role in clinical communication, but overreliance on PLNs may pose risks to reliable information flow. Operational models that utilize PLNs may be made more robust to these risks by improving interdisciplinary communication between doctors. Our research framework could also be applied more broadly to investigate service delivery in different healthcare settings or for other medical specialties, patient groups, or demographics.

Information flow and data-driven analysis of cross-cultural communication in the Chinese cultural community

Abstract The rapid development of the Internet also provides the possibility of information flow for cross-cultural communication, and the network not only breaks through the boundaries of time and space but also breaks through the limitations of cultural communication barriers. The study analyzes the influence mechanism of cross-cultural communication through the medium of social networks. A series of characteristics are proposed to characterize the flow of information, and a simulation experiment is carried out on the influencing factors of cross-cultural communication using the infectious disease model (SEIR). We utilize the Twitter dataset of real social networks to examine the effectiveness of the proposed SEIR model of information dissemination and verify its accuracy. The simulation experiment analyzes the influence of the containment mechanism, the time lag mechanism, and the forgetting mechanism on the model of information dissemination in cross-cultural communication, and information dissemination does not occur when R 0 <1. When R 0 >1, the cross-cultural communication information will be continuously and stably spread in the social network. This paper’s SEIR model has a positive effect on the dissemination of cross-cultural communication information, as evidenced by the results.

Characterizing barriers to closing cross-institutional referral loops: Workflow and information flow analysis

The specialty referral process consists of primary care clinicians referring patients to specialty consultants. This care transition requires effective care coordination and health information exchange between care teams; however, breakdowns in workflow and information flow impede “closing the referral loop” and delay or prevent referrers from receiving the consultant's “visit notes,” particularly in cross-institutional referrals. This study aimed to describe and map the referral process as it occurs in clinics and identify and characterize work system barriers affecting its performance. Referrers and consultants were interviewed about their perceived workflows, barriers, and clinical outcomes to inform a workflow analysis.

A regulatory pathway model of neuropsychological disruption in Havana syndrome.

In 2016 diplomatic personnel serving in Havana, Cuba, began reporting audible sensory phenomena paired with onset of complex and persistent neurological symptoms consistent with brain injury. The etiology of these Anomalous Health Incidents (AHI) and subsequent symptoms remains unknown. This report investigates putative exposure-symptom pathology by assembling a network model of published bio-behavioral pathways and assessing how dysregulation of such pathways might explain loss of function in these subjects using data available in the published literature. Given similarities in presentation with mild traumatic brain injury (mTBI), we used the latter as a clinically relevant means of evaluating if the neuropsychological profiles observed in Havana Syndrome Havana Syndrome might be explained at least in part by a dysregulation of neurotransmission, neuro-inflammation, or both. Automated text-mining of >9,000 publications produced a network consisting of 273 documented regulatory interactions linking 29 neuro-chemical markers with 9 neuropsychological constructs from the Brief Mood Survey, PTSD Checklist, and the Frontal Systems Behavior Scale. Analysis of information flow through this network produced a set of regulatory rules reconciling to within a 6% departure known mechanistic pathways with neuropsychological profiles in N = 6 subjects. Predicted expression of neuro-chemical markers that jointly satisfy documented pathways and observed symptom profiles display characteristically elevated IL-1B, IL-10, NGF, and norepinephrine levels in the context of depressed BDNF, GDNF, IGF1, and glutamate expression (FDR < 5%). Elevations in CRH and IL-6 were also predicted unanimously across all subjects. Furthermore, simulations of neurological regulatory dynamics reveal subjects do not appear to be "locked in" persistent illness but rather appear to be engaged in a slow recovery trajectory. This computational analysis of measured neuropsychological symptoms in Havana-based diplomats proposes that these AHI symptoms may be supported in part by disruption of known neuroimmune and neurotransmission regulatory mechanisms also associated with mTBI.

Hardware/software security co-verification and vulnerability detection: An information flow perspective

Security vulnerabilities provide attackers unauthorized access to critical resources and effective attack surfaces to compromise a system. Security verification is an emerging technique for detecting and locating such threats. However, existing security verification methods are typically restricted within the hardware or software boundary and incapable of meeting cross-layer verification requirements due to the differences in design semantics and the lack of a security model that fits both hardware and software. We attempt to address such a limitation from the perspective of information flow analysis and propose a hardware/software security co-verification method, which can check information flow security properties on fine-grained hardware information flow models. The proposed method can pinpoint security vulnerabilities by capturing information flow security property violations under clues of malicious information flows. Our information flow security model and properties are described using standard hardware design and verification languages, which allows our method to be seamlessly integrated with electronics design automation flows. Experimental results using RISC-V hardware/software designs show that the proposed method detects software, hardware and system-level security vulnerabilities, effectively.

Mapping a Set of Tools to Ensure Cloud and Distributed Computing, Virtualization Tools and Data Storage Systems in the Work of the Transport and Logistics Center

The existing gaps in approaches to the deployment of transport and logistics centers (TLC) within the edges of the backbone network lead to errors in the implementation of the spatial development strategy. Information support solutions for the implementation of terminal, transportation, and warehousing technologies are the least elaborated. As a result, errors have to be corrected in the process of operating the information architecture. There is a need to complement the existing TLC deployment management system with new tools that enhance the validity of TLC location assessment and eliminate the randomness factor in the choice of information architecture for TLC backbone network objects. This research aims to develop a flexible solution for network architecture design using cloud, fog, and edge layers. The main requirement for a flexible solution is that it can be rapidly deployed when the technology architecture changes. The proposed tool visualizes the structure of the network architecture and allows the analysis of information flows by capturing data on the movement of material cargo within the center and between TLC network facilities. The mapping tool considers the network computational load evaluation factor for the cloud, fog, and edge layers. The scientific novelty of the research results is achieved by the principle of system management of the components of complex systems. The practical significance of the results of the study lies in the possibility of using the mapping tool in the process of information architecture design at the stage of making decisions about the deployment of TLC network objects.

Information flow-based fuzzy cognitive maps with enhanced interpretability

AbstractFuzzy Cognitive Maps (FCMs) are a graph-based methodology successfully applied for knowledge representation of complex systems modelled through an interactive structure of nodes connected with causal relationships. Due to their flexibility and inherent interpretability, FCMs have been used in various modelling and prediction tasks to support human decisions. However, a notable limitation of FCMs is their susceptibility to inadvertently capturing spurious correlations from data, undermining their prediction accuracy and interpretability. In addressing this challenge, our primary contribution is the introduction of a novel framework for constructing FCMs using the Liang-Kleeman Information Flow (L-K IF) analysis, a quantitative causality analysis rigorously derived from first principles. The novelty of the proposed approach lies in the identification of actual causal relationships from the data using an automatic causal search algorithm. These relationships are subsequently imposed as constraints in the FCM learning procedure to rule out spurious correlations and improve the aggregate predictive and explanatory power of the model. Numerical simulations validate the superiority of our method against state-of-the-art FCM-based models, thereby bolstering the reliability, accuracy, and interpretability of FCMs.