Information Flow Model Research Articles

Temperature increase drives critical slowing down of fish ecosystems.

Fish ecosystems perform ecological functions that are critically important for the sustainability of marine ecosystems, such as global food security and carbon stock. During the 21st century, significant global warming caused by climate change has created pressing challenges for fish ecosystems that threaten species existence and global ecosystem health. Here, we study a coastal fish community in Maizuru Bay, Japan, and investigate the relationships between fluctuations of ST, abundance-based species interactions and salient fish biodiversity. Observations show that a local 20% increase in temperature from 2002 to 2014 underpins a long-term reduction in fish diversity (∼25%) played out by some native and invasive species (e.g. Chinese wrasse) becoming exceedingly abundant; this causes a large decay in commercially valuable species (e.g. Japanese anchovy) coupled to an increase in ecological productivity. The fish community is analyzed considering five temperature ranges to understand its atemporal seasonal sensitivity to ST changes, and long-term trends. An optimal information flow model is used to reconstruct species interaction networks that emerge as topologically different for distinct temperature ranges and species dynamics. Networks for low temperatures are more scale-free compared to ones for intermediate (15-20°C) temperatures in which the fish ecosystem experiences a first-order phase transition in interactions from locally stable to metastable and globally unstable for high temperatures states as suggested by abundance-spectrum transitions. The dynamic dominant eigenvalue of species interactions shows increasing instability for competitive species (spiking in summer due to intermediate-season critical transitions) leading to enhanced community variability and critical slowing down despite higher time-point resilience. Native competitive species whose abundance is distributed more exponentially have the highest total directed interactions and are keystone species (e.g. Wrasse and Horse mackerel) for the most salient links with cooperative decaying species. Competitive species, with higher eco-climatic memory and synchronization, are the most affected by temperature and play an important role in maintaining fish ecosystem stability via multitrophic cascades (via cooperative-competitive species imbalance), and as bioindicators of change. More climate-fitted species follow temperature increase causing larger divergence divergence between competitive and cooperative species. Decreasing dominant eigenvalues and lower relative network optimality for warmer oceans indicate fishery more attracted toward persistent oscillatory states, yet unpredictable, with lower cooperation, diversity and fish stock despite the increase in community abundance due to non-commercial and venomous species. We emphasize how changes in species interaction organization, primarily affected by temperature fluctuations, are the backbone of biodiversity dynamics and yet for functional diversity in contrast to taxonomic richness. Abundance and richness manifest gradual shifts while interactions show sudden shift. The work provides data-driven tools for analyzing and monitoring fish ecosystems under the pressure of global warming or other stressors. Abundance and interaction patterns derived by network-based analyses proved useful to assess ecosystem susceptibility and effective change, and formulate predictive dynamic information for science-based fishery policy aimed to maintain marine ecosystems stable and sustainable.

Methodology for managing the flows of target information in the remote sensing space system Part 2. Interrelated mathematical models systems formation

In the second part of the series of articles, the issues of the systemic organization of mathematical models for solving the problem of controlling the flows of target information in the Earth remote sensing space system are considered. A description of the interrelated mathematical models of the orbital constellation as components of the information system, the main task of which is to survey ground objects and the formation of the initial volume of information for its further processing, is presented. To calculate the time of servicing the request by the space segment, the following methods of formation are proposed: a model of the evolution of the Earth remote sensing spacecraft orbit; model for forecasting possible spacecraft correction intervals to maintain nominal orbital parameters; model for forecasting possible time intervals for on / off cycles of observation equipment; model for forecasting possible time intervals for dumping the received information to the information reception points. When calculating the cost of servicing a single request from the orbital complex, both the cost of servicing one spacecraft per unit of time and the cost of processing a single request from the ground complex were taken into account. In conclusion, a generalized form of representation of the target information flow model of the Earth remote sensing space system is proposed as an interconnected sequence of functions for changing the amount of information when an appropriate processing process (traffic change functions) is applied to it. General approaches to solving the optimization problem are considered.

Системний аналіз та побудова моделі інформаційних потоків тепличного комплексу

The analysis of existing methods and systems of management of the greenhouse complex is carried out. It has been determined that the most expedient is the development of new methods and control systems that would take into account the mutual influence of microclimate parameters, predicted changes in external natural disturbances and determine the most energy efficient modes of functioning of the greenhouse economy. A systematic analysis of the greenhouse complex at the stages of structural, functional, informational and parametric analysis was carried out. A system of goals and a tree of goals for the energy efficient functioning of the greenhouse complex has been built using the decomposition of goals and objectives and synthesis with the definition of the components of the greenhouse economy. The quantitative and qualitative characteristics of information flows between subsystems have been determined and a model of information flows has been built. For the purpose of carrying out functional analysis and synthesis, the method of mathematical description is used - the theory of graphs. The quantitative and qualitative characteristics of information flows between subsystems have been determined using the construction of a model of information flows. A parametric analysis and synthesis were carried out to determine the main controlled and regulated parameters of the greenhouse complex in order to ensure the maximum efficiency of the control system. Structural analysis and synthesis were carried out with the definition of the components of the greenhouse economy, the interrelationships of the subsystems of the greenhouse complex were analyzed and it was proved that the object under study is a complex system. The directions of further research have been formed for the processes of self-organization and management of information flows of the greenhouse complex, which provide for a plurality of scenarios for further development. Key words: greenhouse complex, system analysis, information flows, goal tree, energy efficient management

Managing non‐functional requirements in agile software development

Non-functional requirements define qualities of the software system that ensure effectiveness while embedding any constraints and restrictions on the design. A challenge rises with agile implementation in handling non-function requirements in regulated environments. Thus, a practitioner's perceptions of agile method tailoring are described in relation to inter-team boundaries and non-functional requirements. The research comprises 18 practitioner interviews from two multinational agile software development companies. Interviews were recorded, transcribed, and analysed using an approach informed by grounded theory and information flow models were used to compare and contrast interactions of processes. It was discovered that one of the case study companies managed non-functional requirements as artefacts in their agile methodology, while the other company reverts to conventional plan-based software development practices of documentation, timeline estimations, and safety critical requirements. This research creates a detailed comparison of these contrasting approaches. The main contribution of this study is a set of proposed recommendations to deal with non-functional requirements in a regulated environment using agile techniques. The introduction of two new artefacts, Documentation Work Item and Safety Critical Work Item, is recommended and it is accompanied with an illustrative example, to transform the handling of documentation and safety critical requirements in a more agile way.

Spatial Spillover Effect and Influencing Factors of Information Flow in Urban Agglomerations—Case Study of China Based on Baidu Search Index

Cities in an urban agglomeration closely interact with each other through various flows. Information flow, as one of the important forms of urban interactions, is now increasingly indispensable with the fast development of informatics technology. Thanks to its timely, convenient, and spatially unconstrained transmission ability, information flow has obvious spillover effects, which may strengthen urban interaction and further promote urban coordinated development. Therefore, it is crucial to quantify the spatial spillover effect and influencing factors of information flows, especially at the urban agglomeration scale. However, the academic research on this topic is insufficient. We, therefore, developed a spatial interaction model of information flow (SIM-IF) based on the Baidu Search Index and used it to analyze the spillover effects and influencing factors of information flow in the three major urban agglomerations in China, namely Beijing–Tianjin–Hebei (BTH), the Yangtze River Delta (YRD), and the Pearl River Delta (PRD) in the period of 2014–2019. The results showed that the SIM-IF performed well in all three agglomerations. Quantitative analysis indicated that the BTH had the strongest spillover effect of information flow, followed by the YRD and the PRD. It was also found that the hierarchy of cities had the greatest impact on the spillover effects of information flow. This study may provide scientific basis for the information flow construction in urban agglomerations and benefit the coordinated development of cities.

Modeling of Time-Delayed Distributed Cyber-Physical Power Systems for Small-Signal Stability Analysis

Compared with a power system under centralized control, a power system under fully distributed control has stronger interdependence between its physical side and cyber side. Such a system is a typical cyber-physical system (CPS). The time delay problem is one of the most critical issues in the application of distributed control for power systems. First, a distributed information flow model is established considering multiple sources of time delay, especially the communication delay between neighboring agents. Second, combining the dynamics of physical power systems, a general model of time-delayed CPS under fully distributed control is proposed, especially for inverter-based microgrids. Third, the stability of the time-delayed CPS is analyzed based on the proposed model with a critical eigenvalue tracking algorithm. A real-world microgrid with DGs under fully distributed frequency control is tested for small-signal stability analysis. The findings show that the stability of the distributed CPS with multiple sources of time delay depends on not only the absolute value of time delays but also the coordination of time delays and that the delay merging property in centralized mode is not relevant in a distributed CPS. The accuracy of the model and the obtained stability margin are verified by Simulink simulations.

A Field-Sensitive Security Monitor for Object-Oriented Programs

In this paper, we propose a sound method to synthesize a permissive monitor using boolean supervisory controller synthesis that observes a Java program at certain checkpoints, predicts information flow violations and applies suitable countermeasures to prevent violations. We introduce an approach for modeling heap and information flow via heap. To improve permissiveness, we train the monitor and remove false positives by executing the program along with its executable model. If a security violation is detected, the user can define sound countermeasures, including declassification to apply in checkpoints. We prove that the monitored program ensures localized delimited release in case of declassifying information and termination-insensitive noninterference in case of no declassification. We implement a tool to automate the whole process and generate a monitor. Our method is evaluated by applying it on the Droidbench benchmark and one real-life Android application.

Research on the Effect of English Talents Gathering Based on Big Data Hotspot Collection Technology

Talent is the best group in human resources, and the Talents are the best group in human resources, and English-speaking talents are the most dynamic factor in productivity. In order to improve the quantitative analysis ability of the English talent aggregation effect, the English talents aggregation effect analysis model is proposed based on large-scale data collection technology. The collection information flow model of the hotspot big data of English talents aggregation effect is constructed. The high-dimensional feature grouping method is used to reconstruct the hotspot big data of the English talents aggregation effect. The piecewise linear test method is used to analyze the statistical characteristics of the hotspot big data of the English talents aggregation effect and extract the frequent vector set which reflects the hot big data category attribute of the English talents aggregation effect. According to the result of feature extraction, the fuzzy English talents aggregation is processed to realize the fusion of big data information of English talents aggregation effect hotspot. Combined with quantitative analysis method, the automatic classification of big data association rules is realized. The experimental and simulation results show that the proposed method is more accurate and effective than the traditional methods in collecting hotspot data, which is 26% and 76% higher than the traditional methods. This method has better accuracy and improved data aggregation effect in collecting hotspot data of English talents gathering and has strong collecting ability and characteristics. The research improved hotspot big data’s English talents gathering effect.

Use of the PVS formal logic system in the method of formal proof of security in the construction of information security systems

The object of research is the information and telecommunication system (ITS) and ensuring the protection of information stored, processed and circulating in it. One of the most problematic areas in the creation of secure ITS is the logical inconsistency and incompleteness of the information security policy. That is, a set of laws, rules, restrictions, recommendations, etc., which regulate the procedure for processing information and are aimed at protecting information from a certain set of threats. The reason for such problems is usually the absence of pre-design modeling of the information security system as a component of the information and telecommunications system, which in the end causes the latter to be vulnerable. An important prerequisite for the creation of a secure ITS is the construction of a subject-object model of the system, which makes it possible to determine the connections between objects, their features, to model information flows and types of access to information and infrastructure resources. According to the existing clear, complete and consistent subject-object model of the ITS, it becomes possible to apply mathematical methods to modeling the processes of its functioning, including for solving the problem of formal proof of security. The paper considers the main idea of the method of formal proof of security, which can be used when building information security systems or assessing the security of the created information and telecommunications system. It is shown that for its implementation it is possible to use the methodology of automatic theorem proving. One of the ways to solve this problem, which is proposed in the work, is the use of the PVS (Prototype Verification System) formal logic system, which is widely used for writing specifications and constructing proofs. The main components of this system are considered, as well as the possibilities of its use for automatic proof of statements about the impossibility of unauthorized access under the conditions of a certain security policy. An example of the use of the PVS system for the formal proof of the security of the system in the framework of the Bella-LaPadula security policy is given.

Information Flow in Logics in the Vicinity of BB

Situation theory, and channel theory in particular, have been used to provide motivational accounts of the ternary relation semantics of relevant, substructural, and various non-classical logics. Among the constraints imposed by channel-theory, we must posit a certain existence criterion for situations which result from the composites of multiple channels (this is used in modeling information flow). In associative non-classical logics, it is relatively easy to show that a certain such condition is met, but the problem is trickier in non-associative logics. Following Tedder (2017), where it was shown that the conjunction-conditional fragment of the logic B admits the existence of composite channels, I present a generalised ver- sion of the previous argument, appropriate to logics with disjunction, in the neighbourhood ternary relation semantic framework. I close by suggesting that the logic BB+(^I), which falls between Lavers' system BB+ and B+ , satisfies the conditions for the general argument to go through (and prove that it satisfies all but one of those conditions).

INFORMATION AND ACCOUNTING SUPPORT FOR INVESTMENT ANALYSIS OF BUSINESSFOR MANAGEMENT DECISION MAKING IN INDUSTRY 4.0

Directions for the development of information and accounting support for investment analysis of business structures for making management decisionsin Industry 4.0 are proposed. The key areas of changes in accounting in Industry 4.0 are defined as follows: expanding the display of the scope of theenterprise in the accounting; formation of approaches to the integration of accounting information for reporting; improving the efficiency of accounting,etc. The opportunities provided by the information and accounting platform for the development of the company's investment activities have beenanalyzed. Conceptual provisions of management accounting for investment analysis in the context of domestic practice are proposed. A model ofinformation flows generated by an investment project during the entire period of time from the inception of an investment idea and the selection ofinvestment proposals to the moment of evaluating the results of the project has been built. Approaches to the formation and disclosure of accountinginformation on the implementation of investment projects by companies, as well as methodological approaches to the analytical support of themanagement decision-making process, have been improved. The elements of the accounting policy for management accounting for the investmentprocess have been clarified. As objects of accounting for investment activities, the actual costs of creating investment activities by their types (materialand labor costs, depreciation deductions of fixed assets and intangible assets) and the movement of cash and non-cash funds as expenses in the areas oftheir expenditure are proposed. A methodological approach to the calculation of cash flows and disclosure of elements of accounting policies formanagement accounting is proposed. The study emphasizes the need to use special software products that automate the processes of accounting foreconomic activities and tools for studying the accumulated data from the perspectives that are most relevant for the enterprise.

Information Integrity

The understanding and promotion of integrity in information security has traditionally been underemphasized or even ignored. From implantable medical devices and electronic voting to vehicle control, the critical importance of information integrity to our well-being has compelled review of its treatment in the literature. Through formal information flow models, the data modification view, and the relationship to data quality, information integrity will be surveyed. Illustrations are given for databases and information trustworthiness. Integrity protection is advancing but lacks standardization in terminology and application. Integrity must be better understood, and pursued, to achieve devices and systems that are beneficial and safe for the future.

Modeling information flows of the process of drilling wells and developing hydrocarbon deposits on the shelf

The article discusses the issues of modeling business processes and information flows of the process of drilling wells and developing hydrocarbon deposits on the shelf. It is shown that the IDEF0 notation is the most convenient for describing the tasks of the upper level of control and building a complex information model of the object under study. The resulting models make it possible not only to determine the functional interaction of individual independent technological problems, but also to justify the choice of methods and tools for their solution. This is of particular importance in the system of professional development of specialists. Information systems such as WellView (Peloton Company) or GeoView (TyumenNIIgiprogaz) are considered as examples.

The Continuum Between Temperament and Mental Illness as Dynamical Phases and Transitions.

The full range of biopsychosocial complexity is mind-boggling, spanning a vast range of spatiotemporal scales with complicated vertical, horizontal, and diagonal feedback interactions between contributing systems. It is unlikely that such complexity can be dealt with by a single model. One approach is to focus on a narrower range of phenomena which involve fewer systems but still cover the range of spatiotemporal scales. The suggestion is to focus on the relationship between temperament in healthy individuals and mental illness, which have been conjectured to lie along a continuum of neurobehavioral regulation involving neurochemical regulatory systems (e.g., monoamine and acetylcholine, opiate receptors, neuropeptides, oxytocin), and cortical regulatory systems (e.g., prefrontal, limbic). Temperament and mental illness are quintessentially dynamical phenomena, and need to be addressed in dynamical terms. A meteorological metaphor suggests similarities between temperament and chronic mental illness and climate, between individual behaviors and weather, and acute mental illness and frontal weather events. The transition from normative temperament to chronic mental illness is analogous to climate change. This leads to the conjecture that temperament and chronic mental illness describe distinct, high level, dynamical phases. This suggests approaching biopsychosocial complexity through the study of dynamical phases, their order and control parameters, and their phase transitions. Unlike transitions in physical systems, these biopsychosocial phase transitions involve information and semiotics. The application of complex adaptive dynamical systems theory has led to a host of markers including geometrical markers (periodicity, intermittency, recurrence, chaos) and analytical markers such as fluctuation spectroscopy, scaling, entropy, recurrence time. Clinically accessible biomarkers, in particular heart rate variability and activity markers have been suggested to distinguish these dynamical phases and to signal the presence of transitional states. A particular formal model of these dynamical phases will be presented based upon the process algebra, which has been used to model information flow in complex systems. In particular it describes the dual influences of energy and information on the dynamics of complex systems. The process algebra model is well-suited for dealing with the particular dynamical features of the continuum, which include transience, contextuality, and emergence. These dynamical phases will be described using the process algebra model and implications for clinical practice will be discussed.

Network propagation of rare variants in Alzheimer's disease reveals tissue-specific hub genes and communities.

State-of-the-art rare variant association testing methods aggregate the contribution of rare variants in biologically relevant genomic regions to boost statistical power. However, testing single genes separately does not consider the complex interaction landscape of genes, nor the downstream effects of non-synonymous variants on protein structure and function. Here we present the NETwork Propagation-based Assessment of Genetic Events (NETPAGE), an integrative approach aimed at investigating the biological pathways through which rare variation results in complex disease phenotypes. We applied NETPAGE to sporadic, late-onset Alzheimer's disease (AD), using whole-genome sequencing from the AD Neuroimaging Initiative (ADNI) cohort, as well as whole-exome sequencing from the AD Sequencing Project (ADSP). NETPAGE is based on network propagation, a framework that models information flow on a graph and simulates the percolation of genetic variation through tissue-specific gene interaction networks. The result of network propagation is a set of smoothed gene scores that can be tested for association with disease status through sparse regression. The application of NETPAGE to AD enabled the identification of a set of connected genes whose smoothed variation profile was robustly associated to case-control status, based on gene interactions in the hippocampus. Additionally, smoothed scores significantly correlated with risk of conversion to AD in Mild Cognitive Impairment (MCI) subjects. Lastly, we investigated tissue-specific transcriptional dysregulation of the core genes in two independent RNA-seq datasets, as well as significant enrichments in terms of gene sets with known connections to AD. We present a framework that enables enhanced genetic association testing for a wide range of traits, diseases, and sample sizes.

Ideology and influence in the debate over Russian election interference

The salience of the debate over Russian political interference in the USA has increased significantly during the Trump administration. However, there is no consensus over how to respond to the interference of this openly illiberal power. This paper argues that we need to reconceptualize our understanding of Russian influence to understand this high level of contestation. While the current understanding of Russian influence is characterized as a problem of unwanted information flows, we argue that we also need to take seriously the ideological influence that the Russia regime has in the USA—that the appeal of the Russian regime’s conservative and populist ideology can help to explain this division. By taking ideological attraction seriously, we can explain why it is currently difficult for the USA to respond to this threat and why solutions to Russian interference based on the information flow models need to be reconsidered.

Analysis of Information Flow Characteristics in Shop Floor: State-of-the-Art and Future Research Directions for Developing Countries

The information flow in a company is a whole set of static or dynamic data obtained from communication used for the conception and realization of products; facing competition, every company aims at improving its performance to meet customer needs. The focus on information flow to improve the performances of companies has always been a major preoccupation in the research domain, which always contributes to the evolution of the analysis of information flow for companies to be more efficient. This article presents the state of the art on the analysis of information flow model based on information characteristics in shop floor manufacturing companies (known as advanced in technology) to ameliorate their performance and future research directions for small- and medium-sized manufacturing companies for developing countries to be more competitive and efficient.

Информационная система учета результатов научной деятельности ФСИН России: вопросы создания и перспективы применения

The research is aimed at developing and implementing proposals for automating the activities of scientific and educational organizations of the Federal Penitentiary Service of Russia for accounting, collecting, processing and analyzing the information on the results of scientific and scientific&technical activities of scientific and educational organizations of the Federal Penitentiary Service of Russia.The practical and scientific significance of the work lies in the development and testing of algorithms, the study of information flows and structures at the creation of an information system for recording the results of scientific activities of scientific and educational organizations of the Federal Penitentiary Service of Russia – the information system «UIS Nauka» (UIS Science). The research methodology is based on the use of methods of systems analysis, modeling of information flows, the theory of complex systems. The study made it possible to formulate functional requirements for the system for recording the results of scientific activity and implement them in a prototype of the information system «UIS Nauka» in the form of a cross-platform, cross-browser web application. The creation of this system made it possible to provide the Federal Penitentiary Service of Russia with an effective tool for planning, coordinating, and recording the results of scientific activities. Key words: information system «UIS Nauka», scientific research, scientific and technical activities, the penal system of the Russian Federation.

Big data network security index correlation measure based on the fusion of modified two order cone programming model

The existing technology can not meet the needs of large data network security index measurement in the era of big data. New theories and methods need to be explored to support the application of large data. In this paper, an improved second-order cone programming model (MTOCPM) is used as the data expression vector. The power spectral density feature of large data is extracted from a large number of noisy and fuzzy data. The second-order cone programming model of large data information flow is constructed by rough concept lattice method. Combining with the finite convergence of the second-order cone programming model algorithm, the reliability of each clustering sample is measured, and the accurate search of data clustering centre is realised. Experiments show that the method based on MTOCPM fusion can not only greatly reduce the communication cost between network nodes, but also improve the global measurement accuracy by about 10%.

Stack Attention-Pruning Aggregates Multiscale Graph Convolution Networks for Hyperspectral Remote Sensing Image Classification

The hyperspectral remote sensing images are classified by traditional neural networks methods can achieve promising performance, but only operate on regular square regions with fixed. This will lead to between neighborhood pixels have limitations in achieve long-distances joint interaction modeling and cross-spacetime information flow for capturing complex spatial-temporal dependencies. Meanwhile ignoring importance detail information and improved utilization of irrelevant information. In the work, we propose a stack attention-pruning multiscale aggregates graph convolution framework (SAP-MAGACN). The framework can automatically learn and selectively attend to the relevant subspace structure by stack attention-pruning module, can effectively disentangle the complex space structure of remote sensing images and capture the rich structural semantics. Meanwhile a refine graph of neighborhood pixels are constructures. Then we adopt the aggregation manner for multiscale graph convolution of pixels nodes in different neighborhood for effective long-range joint interaction modeling. Finally, we leverage dense cross-spacetime edges to completion propagation of multiscale spatial-temporal information, and gradually produce the discriminative embedded features and effectively distinguish the categories of boundary pixels. The experimental results shown the propose SAP-MAGCN outperformance all others state-of-the-art methods on Indian Pines and Salinas public benchmark datasets. Such as the OA, AA and Kappa of our propose SAP-MAGCN frameworks is 96.75%, 95.73% and 97.33%, respectively, on Indian Pines datasets.