Information Flow Model Research Articles

Understanding the effect of nanoconfinement on the structure of water hydrogen bond networks.

Using an integrated approach of network theory and atomistic molecular dynamics simulations, we performed a detailed topological analysis on hydrogen bond networks of water confined between either two graphene sheets or two lipid bilayers to explore the structural perturbation of these networks under nanoscale confinement. The hydrogen bond network structure can be perturbed to a considerable extent when water is confined by such surfaces, yet no small-world behaviour is observed. The presence of ions also reduces the network complexity but its effect may be small depending on the type of confining surfaces. We developed an information flow model to evaluate the fluctuating nature of hydrogen bond networks and to characterise the dynamic, long-distance hydrogen-bonded chains in water. We found that the length and directionality of the hydrogen bond "trails" are highly susceptible to the type of confining surfaces and the degree of confinement. In particular, the endpoints of the hydrogen bond trails are not completely random in confined water, in which inherent distributions are determined by the density of water and the density of hydrogen bonds. This work forms the basis for the study of the pure effect of hydrogen bond network topology on various transport processes, such as proton transfer, that occur along a sequence of hydrogen bonds in a biochemical system. Our results suggest that a combined effect of the structure and lifetime of the hydrogen bond network of interfacial water may contribute to high lateral proton diffusivity at the surface of a lipid membrane.

Efficient scheduling of power communication resources based on fuzzy weighted constraint equalisation

Resources in the power communication system are affected by crosstalk between power grid subnets during scheduling, resulting in a poor equalisation of resource allocation. In order to improve equalisation allocation of resources in the power communication system, an efficient scheduling method of power communication system resources based on fuzzy weighted constraint equalisation is proposed. In this method, the nonlinear time series analysis method is used to construct an information flow model of power communication system resource data and the channel equalisation control is carried out in transmission links of the power communication system; the fuzzy weighted constraint equalisation method is used to implement adaptive baud-spaced equalisation handling during resource scheduling and the fuzzy mesh-based clustering algorithm is used to carry out pairing of classification attribute weights of power resources, so as to achieve efficient clustering of resources and to improve the link equalisation in resource scheduling. Simulation results show that with this method, the average data recall rate during resource scheduling reaches about 90% and the equalisation curve changes very smoothly, which fully indicates that with this method, the data recall rate and scheduling efficiency are high and the equalisation capability of communication channels is strong.

Extensional Petri net

Abstract Petri nets form a concurrent model for distributed and asynchronous systems. They are capable of modeling information flow in a closed system, but are generally not suitable for the study of compositionality. We address the issue of Petri net compositionality by introducing extensional Petri nets. In an extensional Petri net some places are external while others are internal. Every external place is labeled by a distinguished interface name. When composing two extensional Petri nets two places with a same label are coerced. An external place can be turned into an internal place by applying localization operator. The paper takes a look at bisimulation semantics and observational properties of the extensional Petri nets.

Economic Analysis of Micro-chain and Micro-commerce Based on Information Flow and Thermodynamic Models

Micropayment by WeChat is widely used these days across the world which can be extended to microcommerce. While it can be considered as the essence of a microchain in society, it is also found to enjoy similar characteristics like Huizhou Merchants, such as the way of travelling and the aims for daily uses. The information flow model is presented to account for the profits caused by asymmetry in modern society within blockchain. In contrast, the thermodynamic model with entropy is used to show the profits in the microcommerce, and predicts that a large block chain may not necessarily increase values.

Ventral pallidum encodes relative reward value earlier and more robustly than nucleus accumbens

The ventral striatopallidal system, a basal ganglia network thought to convert limbic information into behavioral action, includes the nucleus accumbens (NAc) and the ventral pallidum (VP), typically described as a major output of NAc. Here, to investigate how reward-related information is transformed across this circuit, we measure the activity of neurons in NAc and VP when rats receive two highly palatable but differentially preferred rewards, allowing us to track the reward-specific information contained within the neural activity of each region. In VP, we find a prominent preference-related signal that flexibly reports the relative value of reward outcomes across multiple conditions. This reward-specific firing in VP is present in a greater proportion of the population and arises sooner following reward delivery than in NAc. Our findings establish VP as a preeminent value signaler and challenge the existing model of information flow in the ventral basal ganglia.

The quoter model: A paradigmatic model of the social flow of written information.

We propose a model for the social flow of information in the form of text data, which simulates the posting and sharing of short social media posts. Nodes in a graph representing a social network take turns generating words, leading to a symbolic time series associated with each node. Information propagates over the graph via a quoting mechanism, where nodes randomly copy short segments of text from each other. We characterize information flows from these text via information-theoretic estimators, and we derive analytic relationships between model parameters and the values of these estimators. We explore and validate the model with simulations on small network motifs and larger random graphs. Tractable models such as ours that generate symbolic data while controlling the information flow allow us to test and compare measures of information flow applicable to real social media data. In particular, by choosing different network structures, we can develop test scenarios to determine whether or not measures of information flow can distinguish between true and spurious interactions, and how topological network properties relate to information flow.

Optimal Scheduling Model of Cloud Storage Resources Based on Double Threshold Load Balancing Control

Effective resource scheduling design in cloud storage system can integrate cloud computing central resources and improve resource optimal allocation ability. An optimal scheduling method for cloud storage resources is proposed based on two-threshold load balancing control. The spatial distribution of storage resources is optimized by using grid region collocation method, and the model of cloud storage resource distribution information flow is constructed by nonlinear time series reorganization method, and the auto-correlation feature matching of cloud storage resource information flow is carried out. The association rule feature quantity of cloud storage resource is mined, the phase space of cloud storage system is reconstructed according to the distribution attribute of association rule feature, the priority attribute list of resource scheduling is constructed, and segment interpolation method is adopted to allocate cloud resource. The threshold value of resource scheduling is determined by adaptive optimization of cloud resource allocation threshold using piecewise interpolation method, and load balancing control of cloud resource allocation is carried out by double threshold control method. The partition scheduling of cloud storage resources is implemented according to the priority list. The simulation results show that the proposed method has good load balance, high precision and high throughput, and it has good application value in resource optimization.

Reliable IoT Storage: Minimizing Bandwidth Use in Storage Without Newcomer Nodes

This letter characterizes the optimal policies for bandwidth use and storage for the problem of distributed storage in Internet of Things (IoT) scenarios, where lost nodes cannot be replaced by new nodes as is typically assumed in Data Center and Cloud scenarios. We develop an information flow model that captures the overall process of data transmission between IoT devices, from the initial preparation stage (generating redundancy from the original data) to the different repair stages with fewer and fewer devices. Our numerical results show that in a system with 10 nodes, the proposed optimal scheme can save as much as 10.3% of bandwidth use, and as much as 44% storage use with respect to the closest suboptimal approach.

Degrees of Freedom of the Circular Multirelay MIMO Interference Channel in IoT Networks

In this paper, we study the degrees of freedom (DoF) of a new network information flow model named the circular multirelay multiple-input multiple-output interference channel (CMMI). In this model, there are two clusters and each of them contains three users. Each user equipped with $M$ antennas in one cluster intends to deliver data streams to another user in the same cluster in a circular one-way transmission via the common distributed $K~N$ -antenna relay nodes. The CMMI network model can be considered as a basic component to construct the complicated Internet of Things networks. By assuming linear processing at the users and the relays, we show that the original analysis of DoF comes down in finding solutions of some nonlinear matrix equations with rank constraints. Toward this end, by using linear precoding and post-processing techniques, we propose two different approaches to solve the nonlinear matrix equations based on different antenna configurations. We show that a DoF of $\text{max} \{ {\text{min} \{ M,({{\sqrt {6K} }}/{12}) \}{,}~\text{min} \{ {({M}/{3}),({KN}/{2})} \}} \}$ is achievable for ${\forall ({M}/{N}) \in ({0, + \infty })}$ . In addition, to assess the optimal DoF, the cut-set approach is used for deriving the DoF upper bound by innovatively separating certain users to form two-pair two-way relay channels. We show that the DoF of CMMI is upper bounded by ${\text{max} \{{\text{min} \{{M,({KN}/{3})} \}, \text{min} \{ {({2M}/{3)},({KN}/{2})} \}} \}}$ . By combining the achievable DoF and the upper bound, we finally show that the optimal DoF of CMMI can be achieved for ${({M}/{N}) {\in } [{0,({{\sqrt {6K} }}/{12})}] {\cup } [{({3K}/{2}), + \infty }),{\forall } {K \ge 1}}$ .

An empirical study of the Volkswagen recall crisis in China: Customer's risk perceptions and behavior responses based on an information flow

ABSTRACTProduct-harm crises usually lead to product recalls, which would attract extensive public concern on the issue of product defects, but it is not yet clear how product recalls influence customers' risk perceptions and behavior responses. In this study, guided by the protective action decision model (PADM) and the heuristic-systematic model (HSM), and through an empirical study involving 467 participants drawn from the customers of Volkswagen, a conceptual model of information flow was developed, which starts from individuals' product experience, strengthening or weakening through information seeking and processing, then directly acts on the risk perception, and ultimately influences protective actions. By using structural equation modeling to test the model, and the results shown that customers' information need, seeking, and processing are significantly related to risk perception, their behavioral intentions (loyalty or boycott) are also significantly influenced by risk perception and product experience. Next, multiple-group analysis is employed to compare the distinctions between two groups of customers (those whose cars belong to the recall, and those not belonging to the recall). Unexpectedly, the role of information processing in the recall group is weaker than that in the not recalled group. At last, theoretical and practical implications and suggestions for further research are also discussed.

Hidden complexities in information flow between primary and specialty care clinics

Consultations are a critical part of healthcare for many patients; however, they pose macrocognitive challenges at both individual and system levels. Coordination between primary care and specialty clinics is challenging, because information must be shared across clinics, roles, and time. We conducted a study of the consultations process in the U.S. Department of Veterans Affairs. Using interviews, observations, and document review, we identified five limitations in the current documentation of information flow: scheduling is omitted, information transfers appear to be standardized and well understood, all information relevant to consultations appears to be efficiently tracked from beginning to end, CPRS appears to support critical communication about consultations, and follow-up is de-emphasized. We offer a more ecologically oriented, descriptive model of information flow, and highlight common breakdowns in the consultations process.

Medical Exam Question Answering with Large-scale Reading Comprehension

Reading and understanding text is one important component in computer aided diagnosis in clinical medicine, also being a major research problem in the field of NLP. In this work, we introduce a question-answering task called MedQA to study answering questions in clinical medicine using knowledge in a large-scale document collection. The aim of MedQA is to answer real-world questions with large-scale reading comprehension. We propose our solution SeaReader---a modular end-to-end reading comprehension model based on LSTM networks and dual-path attention architecture. The novel dual-path attention models information flow from two perspectives and has the ability to simultaneously read individual documents and integrate information across multiple documents. In experiments our SeaReader achieved a large increase in accuracy on MedQA over competing models. Additionally, we develop a series of novel techniques to demonstrate the interpretation of the question answering process in SeaReader.

The geriatric information flow model

An abstract information flow model is proposed for geriatric care, the geriatric information flow model (GIFM) by adopting an information model from cancer care and introduce characteristics for geriatric care (patient population, multidisciplinary and multi-professional approach, cross-sectoral approach). The ideal-typical geriatric patient treatment process flow is introduced. Actors (patients, physicians, therapists, organizations), information objects, and information relations as well as information flows are defined. Process flow, actors, information object, and information relations are proved with qualitative material from an accompanying study. The GIFM is validated by mapping four typical knowledge processes (multi-professional geriatric team session, interdisciplinary clinical case conferences, tumor boards, transition management) onto the model. The GIFM is stated as useful for understanding information flows and relations in geriatric care. All processes for validation can be mapped onto GIFM. In future work the GIFM should be tested with more knowledge process and could also be used for identifying gaps in the IT support of geriatric care. A study on high and low information quality in geriatric care is also proposed.

Nursing Information Flow in Long-Term Care Facilities.

Long-term care (LTC), residential care requiring 24-hour nursing services, plays an important role in the health care service delivery system. The purpose of this study was to identify the needed clinical information and information flow to support LTC Registered Nurses (RNs) in care collaboration and clinical decision making. This descriptive qualitative study combines direct observations and semistructured interviews, conducted at Alberta's LTC facilities between May 2014 and August 2015. The constant comparative method (CCM) of joint coding was used for data analysis. Nine RNs from six LTC facilities participated in the study. The RN practice environment includes two essential RN information management aspects: information resources and information spaces. Ten commonly used information resources by RNs included: (1) RN-personal notes; (2) facility-specific templates/forms; (3) nursing processes/tasks; (4) paper-based resident profile; (5) daily care plans; (6) RN-notebooks; (7) medication administration records (MARs); (8) reporting software application (RAI-MDS); (9) people (care providers); and (10) references (i.e., books). Nurses used a combination of shared information spaces, such as the Nurses Station or RN-notebook, and personal information spaces, such as personal notebooks or "sticky" notes. Four essential RN information management functions were identified: collection, classification, storage, and distribution. Six sets of information were necessary to perform RN care tasks and communication, including: (1) admission, discharge, and transfer (ADT); (2) assessment; (3) care plan; (4) intervention (with two subsets: medication and care procedure); (5) report; and (6) reference. Based on the RN information management system requirements, a graphic information flow model was constructed. This baseline study identified key components of a current LTC nursing information management system. The information flow model may assist health information technology (HIT) developers to consolidate the design of HIT solutions for LTC, and serve as a communication tool between nurses and information technology (IT) staff to refine requirements and support further LTC HIT research.

DNA‐based molecular tools for monitoring cellular activity

Methods to determine levels of protein‐protein interactions are essential in analysis of cellular signaling activity. High‐resolution analysis of proteins or protein complexes requires either advanced microscopy, or molecular methods that combine distance dependence with signal amplification. By using DNA molecules as tools, we have over the years developed several molecular methods utilizing a variety of enzymes to modify and amplify the DNA molecules used in the methods. One such method is Proximity Ligation Assay (PLA) where pairs of antibodies equipped with DNA oligonucleotides (so‐called proximity probes) are used to target proteins. Proximal binding of such probes template the creation of a circular DNA molecule, which can be amplified using rolling circle amplification (RCA). The single‐stranded RCA product from a single recognition event will contain several hundreds of repetitive motifs that can be visualized by hybridization of fluorophore‐conjugated oligonucleotides.Information on levels of both free and interacting proteins is important to determine if the interactions monitored are a result of a post‐translational protein modification, e.g. phosphorylations, that changes the conformation of the proteins and subsequently results in a different dissociation constant (KD), or if the amount of interactions reflects mere expression levels of the proteins. New more complex measurements are needed in order to model signaling pathway activity and information flow in single cells. We have hence further developed PLA to provided information on levels of both free and interacting proteins.This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

МОДЕЛЮВАННЯ ІНФОРМАЦІЙНИХ ПОТОКІВ В СИСТЕМАХ УПРАВЛІННЯ ПРОЕКТНО-ОРІЄНТОВАНИМИ ПІДПРИЄМСТВАМИ

The application of multi-agent approach to the modeling of information flows in management systems of project-oriented enterprises is proposed. The peculiarity of the activities of modern enterprises, corporations in various industries is to ensure flexibility, mobility, universalization while ensuring high productivity of production, speed and adequacy of making strategic and operational decisions that are in line with the strategies of external environment and internal dynamics. This will ensure the support and adoption of effective management decisions. Effective enterprise management, in particular in chemical industry, depends on the organization and formation of information flows, including the presentation of information in a form suitable for management. The presented multi-agent model determines the flow of information flows during interactions of the system with the environment, external dynamics of multi-agent system and the movement of information flows within the agent, that is, internal dynamics of the system. Graphical representation of the transfer of information flows in multi-agent system will provide a visual representation of the flow of information flows, their transmission, processing, providing fast access to data, their exchange on all channels, processing by appropriate algorithms and presentation in a convenient manner. The considered modeling technology makes it possible to detect anomalies in the work of agents and multi-agent system. The obtained results show that the management of a project-oriented enterprise requires the processing of a large amount of information and decision is always taken on the basis of this information. Formation of information flows, obtaining the necessary information, its processing, storage, bringing it to the viewpoint, which facilitates decision making, is an important process. Conclusions are made about the possibility of using this model in the development of decision support systems in the management of project-oriented enterprises

Enabling an effective knowledge and information flow between the phases of building construction and facilities management

PurposeThe purpose of this study is to investigate ways of transferring knowledge and information during the life-cycle phases of construction projects, particularly between the construction and occupancy phases, and to find an approach to minimise knowledge and information gaps during the handover process.Design/methodology/approachThe study applied a qualitative approach involving a literature review and an archival analysis of information flow in the studied cases of a construction project, followed by a cross-cases analysis and expert interviews. Data on information flow were collected from three cases of building construction projects in Perth, Western Australia. In addition, a total of 18 local facilities management experts were interviewed to identify the key reasons of knowledge and information gaps and to propose an effective knowledge flow model.FindingsThe findings of this study indicated a significant knowledge and information gap, which exists during the handover process in construction projects in Western Australia. The findings of case analysis and expert interviews identified that the project handover guidelines were often ignored in construction projects in Western Australia, and the handover phase was not given the same priority as the design and construction phases by most of the project stakeholders, which led to information and knowledge gaps between the project construction and post-occupancy phases. The study conducted, integrated knowledge and information flow modelling to analyse the knowledge and information gaps followed by mapping the gaps against existing knowledge sharing frameworks (KSFs) before proposing an integrated knowledge sharing conceptual model to improve current practice and to enhance the information flow during the various phases of the construction project life cycle.Research limitations/implicationsThe study is based on three cases in Perth, Western Australia, and thus the findings and recommendations are contextual. Whilst laying a good foundation to do so, further research is needed to investigate more cases in Western Australia and beyond to fully generalise the findings from this study.Originality/valueThe study contributes to improve the handover process and information flows in project life-cycle phases in Western Australia and develop an information flow model followed by bringing together existing KSFs, namely, the open communication channel (OCC), soft landing framework (SLF) and building information modelling (BIM), to propose an integrated knowledge sharing conceptual model. The methodology used here to analyse the information flow in a diagrammatic manner, the mapping of FM issues against the KSFs’ capabilities and a conceptual model to facilitate change in the industry’s silo mindset are the main contributions of this paper.

Comparison of Sequential and Parallel Software Implementations of the Hybrid Fluid Model of Information Flows for Computer Networks with Complex Topology

Comparison of Sequential and Parallel Software Implementations of the Hybrid Fluid Model of Information Flows for Computer Networks with Complex Topology

Information Diffusion on Social Media During Natural Disasters.

Social media analytics has drawn new quantitative insights of human activity patterns. Many applications of social media analytics, from pandemic prediction to earthquake response, require an in-depth understanding of how these patterns change when human encounter unfamiliar conditions. In this paper, we select two earthquakes in China as the social context in Sina-Weibo (or Weibo for short), the largest Chinese microblog site. After proposing a formalized Weibo information flow model to represent the information spread on Weibo, we study the information spread from three main perspectives: individual characteristics, the types of social relationships between interactive participants, and the topology of real interaction networks. The quantitative analyses draw the following conclusions. First, the shadow of Dunbar’s number is evident in the “declared friends/followers” distributions, and the number of each participant’s friends/followers who also participated in the earthquake information dissemination show the typical power-law distribution, indicating a rich-gets-richer phenomenon. Second, an individual’s number of followers is the most critical factor in user influence. Strangers are very important forces for disseminating real-time news after an earthquake. Third, two types of real interaction networks share the scale-free and small-world property, but with a looser organizational structure. In addition, correlations between different influence groups indicate that when compared with other online social media, the discussion on Weibo is mainly dominated and influenced by verified users.

Towards Quantified Data Analysis of Information Flow Tracking for Secure System Design

Computing hardware has become an attractive attack surface due to the globalization of semi-conductor design and supply chain, and the wide integration of third-party intellectual property cores. Recently, gate-level information flow tracking (GLIFT) has been proposed to monitor the flow of information in secure hardware design by associating data objects with sensitivity labels and tracking the flow of labeled data. GLIFT can be used to model and verify security-related properties, such as confidentiality and integrity. However, existing work in this realm only considers binary labels. These are inadequate for understanding simultaneous information flow behaviors and the root source information flows. In this paper, we propose a precise multi-bit GLIFT method to perform simultaneous multi-bit flow tracking for understanding exactly which bits are affecting a data object at the same time. The proposed method provides more detailed insights into simultaneous information flow behaviors and thus allows proof of quantitative information flow data properties. We compare the complexity and verification performance for different information flow models using primitive gates, IWLS benchmarks, several cryptographic cores, and trust-HUB benchmarks. Experimental results have demonstrated that our method can reason about multi-bit information flow behaviors and identify potential security flaws.