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Working memory plays an important role in cognitive science and is a basic process for learning. While working memory is limited in regard to capacity and duration, different cognitive tasks are designed to overcome these difficulties. This study investigated information flow during a novel visual working memory task in which participants respond to exaggerated and normal pictures. Ten healthy men (mean age 28.5 ± 4.57 years) participated in two stages of the encoding and retrieval tasks. The electroencephalogram (EEG) signals are recorded. Moreover, the adaptive directed transfer function (ADTF) method is used as a computational tool to investigate the dynamic process of visual working memory retrieval on the extracted event-related potentials (ERPs) from the EEG signal. Network connectivity and P300 sub-components (P3a, P3b, and LPC) are also extracted during visual working memory retrieval. Then, the nonparametric Wilcoxon test and five classifiers are applied to network properties for features selection and classification between exaggerated-old and normal-old pictures. The Z-values of Ge is more distinctive rather than other network properties. In terms of the machine learning approach, the accuracy, F1-score, and specificity of the k-nearest neighbors (KNN), classifiers are 81%, 77%, and 81%, respectively. KNN classifier ranked first compared with other classifiers. Furthermore, the results of in-degree/out-degree matrices show that the information flows continuously in the right hemisphere during the retrieval of exaggerated pictures, from P3a to P3b. During the retrieval of visual working memory, the networks associated with attentional processes show greater activation for exaggerated pictures compared to normal pictures. This suggests that the exaggerated pictures may have captured more attention and thus required greater cognitive resources for retrieval. [ABSTRACT FROM AUTHOR]

Purpose: The objective is to suggest time as an important variable to consider in the network model, specifically when discussing causality. Methods: There is a consideration of the context of functional connectivity because of the time importance of observing the feature inside the neuroscience context. A network model was constructed using the Bayesian network method, utilizing a dataset consisting of three rats' local field potentials. The model took into consideration the time delay of communication among brain areas, as recorded in this study. In pursuit of this objective, the delayed mutual information method was employed to ascertain the temporal delay between local field potentials and K2 score for the purpose of model comparison. Results: Bayesian network depicted the probabilistic relationship among rat's brain areas. Delayed mutual information captured the lag among brain areas, and after its appliance on the Bayesian network model, posed better results. Conclusion: The primary novelty of this research lies in its integration of minor delays within the Bayesian network approach, accomplished through the utilization of the delayed mutual information technique prior to its implementation. The suggested methodology incorporates an essential feature that supports the analysis of functional connectivity among brain areas, thereby providing support for the dynamics of neurophysiology. [ABSTRACT FROM AUTHOR]

We present a relational program logic for reasoning about information flow properties formalised in an assertion language based on partial equivalence relations. We define and prove the soundness of the logic, a proof technique for precise, logic-based information flow properties. The logic extends Hoare logic and its unary state predicates to binary PER-based predicates for relating observationally equivalent states. A salient feature of the logic is that it is capable of reasoning about programs that test on secret data in a secure manner. [ABSTRACT FROM AUTHOR]

The aim of this paper is to revisit the definition of differential operators on hypergraphs, which are a natural extension of graphs in systems based on interactions beyond pairs. In particular, we focus on the definition of Laplacian and p-Laplace operators for oriented and unoriented hypergraphs, their basic properties, variational structure, and their scale spaces. We illustrate that diffusion equations on hypergraphs are possible models for different applications such as information flow on social networks or image processing. Moreover, the spectral analysis and scale spaces induced by these operators provide a potential method to further analyze complex data and their multiscale structure. The quest for spectral analysis and suitable scale spaces on hypergraphs motivates in particular a definition of differential operators with trivial first eigenfunction and thus more interpretable second eigenfunctions. This property is not automatically satisfied in existing definitions of hypergraph p-Laplacians, and we hence provide a novel axiomatic approach that extends previous definitions and can be specialized to satisfy such (or other) desired properties. [ABSTRACT FROM AUTHOR]

Bigeye tuna Thunnus obesus is an important migratory species that forages deeply, and El Niño events highly influence its distribution in the eastern Pacific Ocean. While sea surface temperature is widely recognized as the main factor affecting bigeye tuna (BET) distribution during El Niño events, the roles of different types of El Niño and subsurface oceanic signals, such as ocean heat content and mixed layer depth, remain unclear. We conducted A spatial-temporal analysis to investigate the relationship among BET distribution, El Niño events, and the underlying oceanic signals to address this knowledge gap. We used monthly purse seine fisheries data of BET in the eastern tropical Pacific Ocean (ETPO) from 1994 to 2012 and extracted the central-Pacific El Niño (CPEN) indices based on Niño 3 and Niño 4 indexes. Furthermore, we employed Explainable Artificial Intelligence (XAI) models to identify the main patterns and feature importance of the six environmental variables and used information flow analysis to determine the causality between the selected factors and BET distribution. Finally, we analyzed Argo datasets to calculate the vertical, horizontal, and zonal mean temperature differences during CPEN and normal years to clarify the oceanic thermodynamic structure differences between the two types of years. Our findings reveal that BET distribution during the CPEN years is mainly driven by advection feedback of subsurface warmer thermal signals and vertically warmer habitats in the CPEN domain area, especially in high-yield fishing areas. The high frequency of CPEN events will likely lead to the westward shift of fisheries centers. [ABSTRACT FROM AUTHOR]

The secure transmission of information is receiving more and more attention nowadays. Aeolus is a novel platform designed to enhance the development of distributed applications by preventing unauthorized disclosure of information. And one of the most representative systems for information transmission is the file system, therefore it is of great significance to formally analyze the Aeolus-based file system. In this paper, we use Communicating Sequential Processes (CSP) to model and formalize the file system based on Aeolus. Moreover, we utilize the Process Analysis Toolkit (PAT) to simulate and verify the CSP description of our established model. We specifically verify the validity of five properties: Deadlock Freedom, Divergence Freedom, Reachability, Secrecy, and Integrity. The verification results demonstrate that the model successfully satisfies these properties, affirming the effectiveness of the framework in ensuring file operations and guaranteeing the secure transmission of information. [ABSTRACT FROM AUTHOR]

We examine whether politically connected firms play a role in disseminating political information via their management guidance. Using campaign financing activity or the presence of a government affairs office to proxy for firms' access to political information, we find that politically connected firms are more likely to issue management guidance, and their guidance is more likely to discuss government policies. Further, these relations are attenuated for firms facing high proprietary costs of disclosure. To provide evidence on the source of the political information disclosed through guidance, we examine the timing of when guidance is issued. We find that politically connected firms are more likely to issue guidance and change their government policy–related disclosures prior to the public revelation of government policy decisions. Collectively, these findings suggest that the privileged information firms obtain through their political connections is shared with investors through voluntary disclosures. [ABSTRACT FROM AUTHOR]

Using transaction-level tick-by-tick data of same- and next-day settlement of the Russian Ruble versus the US Dollar exchange rate (RUB/USD) traded on the Moscow Exchange Market during the period 2005–2013, we analyze the impact of trading hours extensions on volatility. During the sample period, the Moscow Exchange extended trading hours three times for the same-day settlement and two times for the next-day settlement of the RUB/USD rate. To analyze the effect of the implementations, various measures of historical and realized volatility are calculated for 5- and 15-min intraday intervals spanning a period of three months both prior to and following trading hours extensions. Besides historical volatility measures, we also examine volume and spread. We apply an autoregressive moving average-autoregressive conditional heteroscedasticity (ARMA-GARCH) model utilizing realized volatility and a trade classification rule to estimate the probability of informed trading. The extensions of trading hours cause a significant increase in both volatility and volume for further analyzing the reasons behind volatility changes. Volatility changes mostly occur after the opening of the market. The length of the extension has a significant positive effect on realized volatility. The results indicate that informed trading increased substantially after the opening for the rate of same-day settlement, whereas this is not observed for next-day settlement. Although trading hours extensions raise opportunities for more transactions and liquidity in foreign exchange markets, they may also lead to higher volatility in the market. Furthermore, this distortion is more significant at opening and midday. A potential explanation for the increased volatility mostly at the opening is that the trading hours extension attracts informed traders rather than liquidity providers. [ABSTRACT FROM AUTHOR]

This paper introduces a design and analysis method of the intelligent multiple-vehicle system (IMVS) based on preview control in the environment of the vehicle to vehicle (V2V). Considering the time delay, the vehicle longitudinal dynamics system is constructed. The information flow between vehicles in the platoon is realized by the onboard sensors and V2V. Based on the actual data sampling and processing, the cooperative optimal preview control problem of the vehicle platoon stability system is discretized. The error is used as a term of the state vector to expand, and the original problem is transformed into an augmented global optimal control problem. The existence and stability of the controller solution are analyzed. The asymptotically stable optimal control law is obtained by using the state augmented matrix, and the global optimal preview controller of original platoon stability is obtained. The simulation results verify the effectiveness of the designed controller. The following error decreases with the increase of the preview step, which ensures the stability of the platoon. From the first follower to the tenth one, the average spacing error decreases by 92.9 %, and the average following error of velocity decreases from 3.97 to 1.56 %. [ABSTRACT FROM AUTHOR]

The direct role of successive intense magnetospheric substorms in injecting/energizing particles into the storm-time ring current is still debated and controversial. Whereas in the recent past it has been observed the absence of a net information flow between magnetic storms and substorms, previous in-situ satellite observations have evidenced that ionospheric-origin ions dominate the population of the ring current during the main phase of geomagnetic storms. As a matter of fact, the controversy arises mainly by the use of sophisticated data-driven techniques somewhat contradicting in-situ measurements. In this framework, the main aim of this work is to attempt an adaption of the powerful information-theoretic approach, i.e., the transfer entropy, in a consistent way with physics modeling and observations and to explore the possible motivations behind the underlying contradictions that emerge when these techniques are used. Our idea is to characterize the dynamics of the information flow within the magnetosphere-ionosphere system using a database of geomagnetic storms instead of considering a long time series of geomagnetic indices. We found a net information flow between the external driver and the geomagnetic indices and also between high and low latitude indices themselves, which turns out to be very well localized during the different phases of a magnetic storm. [ABSTRACT FROM AUTHOR]

This study examines the dynamic causality between the carbon emission market and the clean energy market, using an information flow-based, quantitative Liang causality analysis which is firmly grounded on physics and derived from first principles. The dynamic causal relationships between European Union Allowance (EUA) prices and clean energy index allow us to explore whether the causality in return or in variance from CO2 emission allowances to the clean energy index is time-varying. The results show that the causal relationships in return and in variance between EUA and Clean Energy Index (CEI) are drastically time-varying. For the causality in return, a significant unidirectional long-term and stable causality from CEI to EUA is identified after March 2020. For that in variance, a bidirectional causality is found after March 2020, but values after 2020 are opposite to those in return. It seems when fluctuations in the clean energy market are low, the clean energy market has a weak causal effect on the carbon emission market but when volatility in the clean energy market is increasing, causalities between the two markets are significantly strengthened. These results obtained through this rigorous causality analysis can serve as a reference for academics, market participants, and policymakers to understand the underlying links between EUA prices and clean energy index. [ABSTRACT FROM AUTHOR]

Android security researchers utilize taint analysis to uncover apps' bugs and policy-violating behaviors. However, the investigations are unsafe because current taint trackers can be circumvented by apps that cause information flows across API calls. A context-tainting tracker (CTT) is devised to tackle the problem, but since the technique relies on a hand-picked list of flow-causing API methods, it will miss information flows when unlisted methods are exploited. It can also produce a large number of false positives and cannot be practically used. This paper presents a new taint-tracking technique operating value logging and matching based on the flows' characteristics to track them with reducing the dependency on the list of API methods. We implemented our approach into our taint tracker called VTDroid. We confirmed its effectiveness with our test suite consisting of 31 anti-taint analysis techniques compared to three current tools: CTT, TaintDroid, and FlowDroid. We also evaluated VTDroid and the current tools with popular apps collected from two major app stores. The results show that VTDroid outperforms CTT in precision and TaintDroid and FlowDroid in recall for privacy leak detection. Also, security analysts can utilize VTDroid to detect user input validations with slightly more false positives and fewer false negatives than FlowDroid in VTDroid's code coverage. [ABSTRACT FROM AUTHOR]

The Pacific–North American (PNA) teleconnection pattern is shown to be remarkably influenced by the South China Sea (SCS), through causal inference with information flow, a real physical notion which has just been rigorously derived from first principles. The information flow rate, which measures the strength of causality from the Pacific sea surface temperature anomaly (SSTA) to the PNA index, shows a distinct causal pattern within the SCS. This pattern is rather robust; it exists with different datasets, and lasts through time. To validate, sensitivity experiments have been designed with purported SCS warming and cooling. By comparing the forced PNA evolutions to the standard case, PNA-like responses have been revealed. Specifically, relatively strong positive (negative) SCS SSTA would trigger a positive (negative) propagating PNA-like response. The responding wave train carries energy toward Pacific and North America, and hence affects the PNA growth. Also located is the wave origin using the Rossby wave source diagnosis. This study suggests that the SCS effect should be taken into account in interpreting and predicting PNA. [ABSTRACT FROM AUTHOR]

Maritime supply chain (MarSC) stakeholders interact with third parties (e.g. freight forwarders, 3PLs, financial institutes, custom authorities) to facilitate the cargo flow and exchange of information, documents, or financials. Hence, MarSC stakeholders are increasingly interested in innovative technological solutions that vouch for the authenticity and/or the ownership of digital assets without the control of a central third party. Extended research is carried out to prove how applications based on the distributed ledger technology or blockchain address these requirements, yet limited research investigates their purchasing process and economic implications. This paper uses the phytosanitary certificate in an international supply chain flow as a case study where interaction between multiple stakeholders is fundamental and analyses the purchase scenarios of a blockchain-based tool. To do so, it uses a theoretical model that identifies and quantifies the costs and benefits incurred by MarSC stakeholders, formulates gain-sharing scenarios and presents the results of a sensitivity analysis to show the dependence between the data-use and the potential economic gains it generates. The results show that freight forwarders could share economic benefits with shippers or consignees to anticipate purchasing a blockchain-based tool. [ABSTRACT FROM AUTHOR]

Vancomycin is one of the most prescribed antibiotics in pediatric intensive care units (PICU) in US hospitals. However, a detailed understanding of workflow and information flow among various stakeholders regarding vancomycin treatment processes in clinical settings is lacking. We conducted direct observations and informant interviews to develop the mapping of key processes and information flow for vancomycin treatment, with an emphasis on therapeutic drug monitoring (TDM) dose adjustment decision-making. A health information technology (HIT) sociotechnical framework was used to identify EHR related safety concerns. A total of 27 vancomycin treatment activities were observed over a 60-h duration including infusion administration, infusion completion, trough concentration blood draw and therapeutic decision making processes. Workflow and information flow mappings revealed (1) deviations between the documented timestamp used for TDM decision making and the actual time the tasks executed and (2) the lack of information flow regarding infusion completion and interruption. Missing features, insufficient usability and lack of integration with workflow and communication in the EHR were deemed safety gaps that may affect the accuracy of therapeutic decisions. Our case study identified gaps in information flow among clinical team members via EHR in TDM processes to provide insights for the improvement of the EHR system for antibiotic treatment purposes. In particular, the potential harm of the missing, uncertain, and inaccurate documented TDM task times warrant further investigations. [ABSTRACT FROM AUTHOR]

Privacy is a rather murky concept, but the absence of privacy is clearly felt in today's digital world. With the significant increase in surveillance power of software and hardware, storage capacity, computing power as well as advancement in data science technologies, the threat to privacy is ever increasing. Within the digital realm, privacy has been studied in different platforms, contexts, and environments. In this capacity, Professor Jim Greer is one of the pioneering thought leaders to study privacy in online learning environment. Additionally, strongly related to privacy, trust and personalization are two important components of online learning. This article reflects on Greer's contributions to grapple with the following questions: (1) To what extent are privacy, trust, and personalization desired in online learning? (2) How can privacy, trust, and personalization be supported in online learning? In this vein, we study three theories (i.e., limitation theory, control theory, and contextual integrity theory) and different mechanisms (e.g., privacy preferences, identity management, contextual information flow) for privacy. Additionally, this article discusses the ways to achieve trust and personalization without compromising privacy. [ABSTRACT FROM AUTHOR]

Internal communication is pivotal to a company's performance during construction projects. Although internal communication is known to be strongly affected by the type of business, it is still unclear which influence factors have a greater impact. This research aimed to investigate the causal relationship between internal communication effectiveness and company performance for the different business types within the construction industry, namely owners, contractors, and architects/engineers. Structural equation modeling was applied to analyze the influence factors of 52 measurement variables and 11 latent variables. Based on the results of 317 completed surveys collected from 15 construction-related firms, the authors successfully investigated crucial factors in company performance according to the different business types. The results showed that vertical information flows are more influential in organizational performance than horizontal information flows due to hierarchical organizational characteristics common to most construction-related firms. The results assist managers in evaluating their internal communication based on the company's business type and developing organizational strategies to mitigate any communication-related issues. This research contributes to the construction industry's body of knowledge by demonstrating the influence factors crucial to internal communication effectiveness for different business types. [ABSTRACT FROM AUTHOR]

Ever since databases became an ubiquitous part of enterprises or businesses, security and privacy became a requirement. Traditionally, privacy was realized through various methods of database access control and relied much on the use of statically defined views, which are essentially logical constructs imposed over database tables that can alter or restrict the data that can be viewed by an user. Privacy is about the responsible maintenance of private information. This responsibility is hard to define, which is why laws are necessary. With a vast accumulation of personal data in databases, there has been a heightened awareness and concern about the storage and use of private information leading to privacy-related guidelines, regulations and legislations, Compliance with these regulations has become one of the major concerns for organizations and companies. Traditionally, privacy in databases (DBs) have been addressed through access control techniques including multi-level security (MLS) based on mandatory access control (MAC), and restricted views to the users. As view definitions to comply with regulations became quite complex for accommodating all the restrictions in one view, explicit constructs for specifying privacy policies were introduced for complying with medical regulations like HIPAA (Health Insurance Portability and Accountability Act) from USA, in relational database systems. These enabled fine grained access control (FGAC) capable of enforcing disclosure control enunciated databases. Application of information flow control that is needed for multi-level security (MLS) databases to preserve privacy among multiple users but have their challenges like new abstractions for managing information flow in a relational database system, handling transactions and integrity constraints without introducing covert channels etc. As the DBs need to work alongside information flow controlled programming languages and operating systems for tracking flows, there is a need to enforce the security policy not only on the DBMS but also on the application platform. Due to the underlying requirement of decentralization, it calls for declassification/endorsement and santization requirements on the DB. In this paper, we shall first review some of the major privacy enhancing techniques used traditionally for DBs including MLS DBs, and then explore application of decentralized information flow control models for realizing information flow secure DBs in a robust manner. Towards the end, we shall also touch upon some of the roles of anonymization and psuedonymization including inference control and differential privacy in realizing privacy in practice. [ABSTRACT FROM AUTHOR]

Background: Public awareness is crucial for successful deployment of tidal energy, a renewable energy source that can provide clean electricity to remote islands. However, considering public attitudes on tidal energy are not well known, especially in developing countries, a barrier exists in implementing public engagement strategies. This study aims to contribute by identifying strategies for information provision—the initial step in public engagement—and estimate how these can be engaged to enhance support for tidal energy among the local public in a remote area of a developing country, in this case, Flores Timur Regency, Indonesia, considering their socio-cultural background. Methods: In this paper, we employ statistical analyses using multinomial probit modelling to identify the key variables that shape information flow. The aptness of the variables is then verified using post-estimation techniques for their use as input parameters for the simulation of the information flow in the field study area. Agent-based simulation (ABS) is employed to replicate the actual conditions in Flores Timur Regency, Indonesia, and simulate the flow of information through the local community. Results: According to the multinomial probit estimations, the people belonging to the top hierarchical group show a higher probability to support tidal energy compared to the members belonging to the lower groups. Understandably, around twice as many information flow cycles are needed to disseminate information to the members of the lowest hierarchical group, compared to the members of the top hierarchical group. The results also show that increasing the amount of available information has a positive impact on information dissemination. Conclusions: This study demonstrated that information provision is highly effective with propagation of information that specifically highlights the individual benefits, rather than the community benefits of tidal energy. Additionally, savings in terms of costs, time, and efforts can be realized if the most influential members of the local community are targeted initially before including all other stakeholders. The study also indicated that locals absorb more information and increase their support for tidal energy when additional data is made available. Finally, as long-term strategy, information provision becomes most effective when the local population gains higher educational capabilities. [ABSTRACT FROM AUTHOR]

With the support of information and communication technologies (ICTs), production elements are flowing and interacting with each other continuously. Human production and living activities are no longer confined to the traditional space (space of places, SoPs), but extended to a new kind of space (space of flows, SoFs). With its liquidity, SoFs can greatly break through the limitation of time and space. By means of the flow and interaction of all the element flows, SoFs has been aggressively expanded to the whole field of human activities, and is guiding the formation and development of the regional space to a large extent. This paper aims at conducting a more comprehensive understanding of SoFs. Our research mainly includes the following correlative contents: (1) studying the formation and development of the concept of SoFs, and the association between SoFs and SoPs; (2) analysing the theory of SoFs and its interaction mechanism: (a) revealing the embodiment of the three elements of space in SoFs; (b) analysing the definitions and functions of material flow, population flow, information flow and capital flow, and their relationships; (c) focusing on the information guidance mechanism for the formation and development of SoFs; (d) analysing the interaction mechanism among entities in SoFs by introducing the labor division theory, interdependence theory, and competitive cooperation theory. The main conclusions can be drawn as follows: (1) SoFs depends on and goes beyond SoPs. They will coexist for a long time. (2) In SoFs, all participants have their own "positions" or "locations." Meanwhile, there are "distances" among different participants. The flow of the element flows in SoFs is caused by demands, attractions or other external forces. (3) All kinds of element flows in SoFs interweave and influence each other. By this way, they promote the development and evolution of SoFs. Any element is indispensable, while the information flow is at the core position and plays a guiding role in the formation and development of SoFs. [ABSTRACT FROM AUTHOR]

Based on Bayesian network (BN) and information flow (IF), a new machine learning-based model named IFBN is put forward to interpolate missing time series of multiple ocean variables. An improved BN structural learning algorithm with IF is designed to mine causal relationships among ocean variables to build network structure. Nondirectional inference mechanism of BN is applied to achieve the synchronous interpolation of multiple missing time series. With the IFBN, all ocean variables are placed in a causal network visually, making full use of information about related variables to fill missing data. More importantly, the synchronous interpolation of multiple variables can avoid model retraining when interpolative objects change. Interpolation experiments show that IFBN has even better interpolation accuracy, effectiveness and stability than existing methods. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

We consider heterogeneous distributed storage systems (DSSs) having flexible reconstruction degree, where each node in the system has nonuniform repair bandwidth and nonuniform storage capacity. In particular, a data collector can reconstruct the file using some nodes in the system and, for a node failure, the system can be repaired by some set of active nodes. Using min-cut bound, we investigate the fundamental trade-off between storage and repair costs for our model of the heterogeneous DSS. Further, the problem is formulated as bi-objective optimization linear programing problem for various heterogeneous DSSs. For some DSSs, it is shown that the calculated min-cut bound is tight. [ABSTRACT FROM AUTHOR]

Non-homogeneous and time-varying information flow that affects the price discovery processes within and across markets is a common occurrence in reality but is often neglected in the literature of price discovery. To analyze such information flow within and across markets, we propose a new strategy with a new dynamic price discovery measure. We use this strategy to test the efficient home market hypothesis and the sector effect hypothesis based on the intraday data of the 115 stocks cross-listed and traded in the Canadian and U.S. stock markets. We find that the Canadian stock market is more efficient in price discovery for the Canadian stocks cross-listed in the U.S. stock market. A higher trading volume in the Canadian market makes price discovery in that market more efficient. The Canadian stock market is more efficient in price discovery for stocks in the basic materials sector but not in the technology and financial sectors. The NYSE Alternext is more efficient for junior stocks while the NASDAQ is more efficient for technology stocks. [ABSTRACT FROM AUTHOR]

How do group dynamics affect individuals within the group? How do individuals, in turn, affect group dynamics? As society comes together, individuals affect the group dynamics and vice versa. Social dynamics look at group dynamics, its effect on individuals, conformity, leadership, networks, and more. In the past two decades, the game theoretic Parrondo's paradox has been used to model and explain the different aspects of social dynamics. Two losing games can be combined in a certain manner to give a winning outcome—this is known as Parrondo's paradox. In this review, the connections between Parrondo's paradox and social dynamics are discussed with emphasis on (i) cooperation and competition, (ii) resource redistribution and social welfare, and (iii) information flow and decision-making. [ABSTRACT FROM AUTHOR]

Much recent work has illuminated the growth, innovation, and prosperity of entire cities, but there is relatively less evidence concerning the growth and prosperity of individual neighborhoods. In this paper we show that diversity of amenities within a city neighborhood, computed from openly available points of interest on digital maps, accurately predicts human mobility ("flows") between city neighborhoods and that these flows accurately predict neighborhood economic productivity. Additionally, the diversity of consumption behaviour or the diversity of flows together with geographic centrality and population density accurately predicts neighborhood economic growth, even after controlling for standard factors such as population, etc. We develop our models using geo-located purchase data from Istanbul, and then validate the relationships using openly available data from Beijing and several U.S. cities. Our results suggest that the diversity of goods and services within a city neighborhood is the largest single factor driving both human mobility and economic growth. [ABSTRACT FROM AUTHOR]

Opacity is an important property in control of information flow among networked agents. In this paper, we investigate information control problems in networked discrete event systems using opacity. In a networked discrete event system, communication among agents is via a shared communication network. Since delays and losses are unavoidable in networked discrete event systems, they must be considered in investigating opacity. We call opacity under communication delays and losses network opacity. We first define three network opacities: strong network opacity, weak network opacity, and network non-opacity. We derive necessary and sufficient conditions for network opacities and develop methods to check network opacities. We then apply network opacity to solve a problem in battery management systems. [ABSTRACT FROM AUTHOR]

Patient admissions, discharges, and transfers are high work demand activities that have been associated with 30-day readmissions and increased patient mortality. Most mitigation strategies target peak demand, but variable demand may be more significant. Self-organizing holarchic open systems (SOHOs) and resilience engineering frameworks may explain system behavior, but a few quantitative studies of resilient organizational performance have been published. We used three measures to explore SOHO and resilience engineering constructs. We collected hourly data over 2 years, from five inter-related units in a cardiovascular disease division of a metropolitan teaching hospital. Our results show that information flows (inbound, outbound, answered, and unanswered telephone calls) representing anticipatory management are related to patient flows (patient admissions discharges and transfers) and nurse-staffing levels (nurse-to-patient ratios). We also found overall system stability despite high patient flow effects in lower level units. Unexpectedly, the time to recovery from high patient flow events lasted up to 7 days. We conclude that constructs proposed by resilience engineering can be quantified using simple measures collated within routine operations. The application of nonlinear statistical analyses can uncover important insights about resilient performance that may assist managers in better preparing for managing and recovering from unexpected variation in patient flow. [ABSTRACT FROM AUTHOR]

The global experience of geographical study of information flows is reviewed for the first time based on journal papers and monographs published in 1988–2017. It is established that the issue is understudied, subject to a shift from understanding the relevance of such studies to attempts to measure and structure information flows. The key areas of geographic study are identified: flows without channels and traffic, channels without traffic, traffic without channels, and flows with channels and traffic. No publication has been retrieved for the fourth area, which is most important for regional studies. Three new approaches are proposed in the framework of next-generation communication network studies and in line with the fourth area: uplink–downlink traffic distribution, redistribution of traffic inside a region proportionally to the population size of settlements, and the definition of potential of mobile communication field. The new approaches are tested with a case study of Russia's Irkutsk oblast. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

We examine territorial marketing, a direction of regional policy, which is gaining increasing popularity across the globe; it emerged at the interface of marketing and socio-economic geography and is based on the notion of the uniqueness of each place. We discuss the methodological issues related to this direction and to its relevance to Irkutsk. A study is made of the use and prospects of the images of the city of Irkutsk as the tools for the promotion of the place and the attraction of migrants and tourists. The investigation was made at different geographical scales: regional (Irkutsk-Baikal); microgeographical toponymics, and statistical analysis of the individual perception of the city. Use was made of different investigation techniques: a multi-scale treatment of the same geographic phenomena against the background of the world, the country, the region and the agglomeration; analysis of the city's recreational-geographical location as a variety of the economic-geographic location; comparison of street names according to the locality of the names, that is, the extent to which they are connected with the history and culture of the city as well as according to their popularity and content analysis of texts and images taken from the Internet and belonging both to tourists and to local residents, and images in the field of emotions. Some recommendations are made for the use of the images of the city in its marketing promotion. It is pointed out that the identified images were used in practice; in particular, in designing the historical № 130 Quarter in Irkutsk where timber representing one of the city images was widely used. [ABSTRACT FROM AUTHOR]

This study investigates linear and nonlinear price information flows between the Chinese Stock Index 300 (CSI300) and futures market using high-frequency data and their wavelet transformed series for three regimes for which stock short-selling restrictions in China are different. Empirical results generally indicate information feedback between these two markets regardless of assumptions of linear and nonlinear causality and regimes for original series and wavelet transformed data at different scales. [ABSTRACT FROM AUTHOR]

The method of cumulative estimation of the parameters of information flows in the radio channels of information telecommunication systems is considered. Estimation is performed under the conditions of a complex information target situation based on a comparison of the indicated parameters with a simulated reference measure. It is proposed that the simulated directional pattern of an adaptive antenna system that implements a space-time processing algorithm be used as the measure. It is shown that the use of phase and correlation methods of simulation of a measure assures a high degree of precision of estimation of the parameters. Estimators may be obtained on a time scale close to real time through the use of the present highly efficient procedure. [ABSTRACT FROM AUTHOR]

Demand for autonomous swarms, where robots can cooperate with each other without human intervention, is set to grow rapidly in the near future. Currently, one of the main challenges in swarm robotics is understanding how the behaviour of individual robots leads to an observed emergent collective performance. In this paper, a novel approach to understanding robot swarms that perform foraging is proposed in the form of the Information-Cost-Reward (ICR) framework. The framework relates the way in which robots obtain and share information (about where work needs to be done) to the swarm's ability to exploit that information in order to obtain reward efficiently in the context of a particular task and environment. The ICR framework can be applied to analyse underlying mechanisms that lead to observed swarm performance, as well as to inform hypotheses about the suitability of a particular robot control strategy for new swarm missions. Additionally, the information-centred understanding that the framework offers paves a way towards a new swarm design methodology where general principles of collective robot behaviour guide algorithm design. [ABSTRACT FROM AUTHOR]

Developing practical but secure programs remains an important and open problem. Recently, the operating-system and architecture communities have proposed novel systems, which we refer to as interactive-security systems. They provide primitives that a program can use to perform security-critical operations, such as reading from and writing to system storage by restricting some modules to execute with limited privileges. Developing programs that use the low-level primitives provided by such systems to correctly ensure end-to-end security guarantees while preserving intended functionality is a challenging problem. This paper describes previous and proposed work on techniques and tools that enable a programmer to generate programs automatically that use such primitives. For two interactive security systems, namely the Capsicum capability system and the HiStar information-flow system, we developed languages of policies that a programmer can use to directly express security and functionality requirements, along with synthesizers that take a program and policy in the language and generate a program that correctly uses system primitives to satisfy the policy. We propose future work on developing a similar synthesizer for novel architectures that enable an application to execute different modules in Secure Isolated Regions without trusting any other software components on a platform, including the operating system. [ABSTRACT FROM AUTHOR]

Information asymmetry is often a characteristic present in food value chains. However, high information asymmetry reduces trust and may compromise food safety and quality. Traceability enhances information sharing and disclosure thereby reducing the imperfection. Qualitative and quantitative data was collected from Kenyan certified farmers and organic outlets to generate empirical information on the extent and factors that contribute to traceability along organic kale value chains. Interviews were conducted using a semi-structured questionnaire. Data was analysed using descriptive statistics and factor analysis. Majority (62 %) of smallholder producers had a functional traceability system. Factor analysis revealed that organizational activities and personnel perception contribute to traceability in an organization. Variables that contribute to organizational activities are documentation/record keeping (0.754), certification by other quality management standards (0.711), training on food safety and traceability management (0.444) and system monitoring (0.770). Such knowledge can be used by players along the value chain to reduce information asymmetry. The results suggest need for awareness creation among stakeholders on how organizational activities can be used to improve traceability through enhanced information flow for safer and better quality organic products. In addition, perception of personnel which plays an important role in traceability can be improved through training personnel on beneficial consequences of a traceability system. [ABSTRACT FROM AUTHOR]

Most previous studies of functional brain networks have been conducted on undirected networks despite the fact that direction of information flow is able to provide additional information on how one brain region influences another. The current study explores the application of normalized transfer entropy (NTE) to detect and identify the patterns of information flow in the functional brain networks derived from EEG data during cognitive activity. Using a combination of signal processing, information and graph-theoretic techniques, this study has identified and characterized the changing connectivity patterns of the directed functional brain networks during different cognitive tasks. The functional brain networks constructed from EEG data using non-linear measure NTE also exhibit small-world property. An exponential truncated power-law fits the in-degree and out-degree distribution of directed functional brain networks. The empirical results demonstrate not only the application of transfer entropy in evaluating the directed functional brain networks, but also in determining the information flow patterns and thus provide more insights into the dynamics of the neuronal clusters underpinning cognitive function. [ABSTRACT FROM AUTHOR]

This study examines whether firms that establish political connections gain differential access to relevant information over legislative developments, thereby mitigating the negative consequences of uncertainty. I find that political connections (partially) offset the negative relation between investment and political uncertainty documented in prior research. My results do not appear to be driven by connected firms pursuing investments that are insensitive to uncertainty. I perform additional tests to address concerns over correlated omitted variables. First, I identify a setting around a tax policy development designed to provide new investment incentives to firms. In this setting, I predict and find that reduced information asymmetry for connected firms results in delaying investment in anticipation of future lucrative tax incentives. Second, I perform a falsification test and document that political connections do not mitigate the effects of general economic uncertainty. Finally, I continue to find support for my hypothesis within a propensity matched sample. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to develop of a single mechanism of the adaptive routing of different types of traffic based on the current quality of service requirements. Software-defined networking is a technology of the future. The current development trend of communication systems constantly confirms this fact. However, to date, the use of this technology in its current form is only justified in large networks of major technology companies and service providers. Currently, a large number of dynamic routing protocols have been developed in communication networks. Our task is to create a solution that can make it possible to use the ability of each node to make a decision on the transmission of information by every possible means for each type of traffic. This task can be accomplished by solving the problem of the development of generalized metric that characterizes the communication channels between devices in the network in detail and the problem of the development of a mechanism of adaptive network logical topology reconfiguration (route control) in order to ensure the high quality of service of the whole network that meets current quality requirements for a particular type of service. [ABSTRACT FROM AUTHOR]

By making use of the trace distance as a measure we investigate the influence of classical driving fields on a open quantum system when the system and its environment are initially in a correlated state. It is shown that the amount of trace distance is sensitive to the classical driving fields which implies that the information flowing between open system and its environment can be controlled by the classical driving fields. Furthermore, we also explore the dependence of the trace distance on the initial parameters when the total system is considered in the thermal equilibrium state. We find that the trace distance on the coupling strength can be used to demonstrate the level crossing of the ground state of the system. In particular, the classical driving fields have significant effect on the level crossing of the ground state. [ABSTRACT FROM AUTHOR]

Intracellular biochemical networks fluctuate dynamically due to various internal and external sources of fluctuation. Dissecting the fluctuation into biologically relevant components is important for understanding how a cell controls and harnesses noise and how information is transferred over apparently noisy intracellular networks. While substantial theoretical and experimental advancement on the decomposition of fluctuation was achieved for feedforward networks without any loop, we still lack a theoretical basis that can consistently extend such advancement to feedback networks. The main obstacle that hampers is the circulative propagation of fluctuation by feedback loops. In order to define the relevant quantity for the impact of feedback loops for fluctuation, disentanglement of the causally interlocked influences between the components is required. In addition, we also lack an approach that enables us to infer non-perturbatively the influence of the feedback to fluctuation in the same way as the dual reporter system does in the feedforward networks. In this work, we address these problems by extending the work on the fluctuation decomposition and the dual reporter system. For a single-loop feedback network with two components, we define feedback loop gain as the feedback efficiency that is consistent with the fluctuation decomposition for feedforward networks. Then, we clarify the relation of the feedback efficiency with the fluctuation propagation in an open-looped FF network. Finally, by extending the dual reporter system, we propose a conjugate feedback and feedforward system for estimating the feedback efficiency non-perturbatively only from the statistics of the system. [ABSTRACT FROM AUTHOR]

This paper reviews the key thematic foci of the Science Studies abstracts journal published by the INION RAS, the structure of abstract information, and the timeliness of publications. Suggestions on how to further improve abstracting in the short-term are made. [ABSTRACT FROM AUTHOR]

This article addresses the information content of the open interest of CDS markets. Using a panel database of 481 firms, I show that open interest innovations help to predict subsequent CDS rate changes and stock returns. The open interest dynamics appears to convey specific information on the reference entity and common information. On the one hand, there is evidence that positive open interest growth precedes the announcement of negative earnings surprises, and that high open interest growth prior to these events is linked to positive and significant CDS rate changes. This forecasting power relates with proxies of investors' attention and market frictions. The predictive power on CDS rates is larger for illiquid contracts and for entities with low credit risk, whereas the predictive power over stock returns is larger for entities that display greater open interest outstanding. On the other hand, this article also shows that the aggregate open interest growth has predictive power on the subsequent returns of CDS and bond main indexes, and to a lesser extent on stock market returns. [ABSTRACT FROM AUTHOR]

Some of the factors that can contribute to the success of collaborative adaptive management-such as social learning, open communication, and trust-are built upon a foundation of the open exchange of information about science and management between participants and the public. Despite the importance of information transparency, the use and flow of information in collaborative adaptive management has not been characterized in detail in the literature, and currently there exist opportunities to develop strategies for increasing the exchange of information, as well as to track information flow in such contexts. As digital information channels and networks have been increased over the last decade, powerful new information monitoring tools have also been evolved allowing for the complete characterization of information products through their production, transport, use, and monitoring. This study uses these tools to investigate the use of various science and management information products in a case study-the Sierra Nevada Adaptive Management Project-using a mixed method (citation analysis, web analytics, and content analysis) research approach borrowed from the information processing and management field. The results from our case study show that information technologies greatly facilitate the flow and use of digital information, leading to multiparty collaborations such as knowledge transfer and public participation in science research. We conclude with recommendations for expanding information exchange in collaborative adaptive management by taking advantage of available information technologies and networks. [ABSTRACT FROM AUTHOR]

Continuous improvement in process efficiency for specialist outpatient clinic (SOC) systems is increasingly being demanded due to the growth of the patient population in Singapore. In this paper, we propose a discrete event simulation (DES) model to represent the patient and information flow in an ophthalmic SOC system in the Singapore National Eye Centre (SNEC). Different improvement strategies to reduce the turnaround time for patients in the SOC were proposed and evaluated with the aid of the DES model and the Design of Experiment (DOE). Two strategies for better patient appointment scheduling and one strategy for dilation-free examination are estimated to have a significant impact on turnaround time for patients. One of the improvement strategies has been implemented in the actual SOC system in the SNEC with promising improvement reported. [ABSTRACT FROM AUTHOR]

Understanding the patterns of interconnections between neurons in complex networks is an enormous challenge using traditional physiological approaches. Here we combine the use of an information theoretic approach with intracellular recording to establish patterns of connections between layers of interneurons in a neural network responsible for mediating reflex movements of the hind limb of an insect. By analysing delayed mutual information of the synaptic and spiking responses of sensory neurons, spiking and nonspiking interneurons in response to movement of a joint receptor that monitors the position of the tibia relative to the femur, we are able to predict the patterns of interconnections between the layers of sensory neurons and interneurons in the network, with results matching closely those known from the literature. In addition, we use cross-correlation methods to establish the sign of those interconnections and show that they also show a high degree of similarity with those established for these networks over the last 30 years. The method proposed in this paper has great potential to elucidate functional connectivity at the neuronal level in many different neuronal networks. [ABSTRACT FROM AUTHOR]

With the rapid development of industry and projects for ensuring the amounts of water that those industries require, the capacities and scales of hydraulic structures including reservoirs, dams, and weirs have increased sharply. Numerous dams experience some seepage, when water is held behind them; thus, dam leakage detection has been and continues to be a major concern. The basic task involves finding the path of the leakage, especially the concentrated leakage path. Using tracer techniques, we treat dam leaks as multiple attribute evaluation problems, which is one innovation presented in this paper. Entropy weight-set pair analysis, a new method of identifying leaks, is introduced. The water samples and the information flows including conductivity, pH, Cl, D, and O are set as alternatives and indexes, respectively. Then, the connection degree is calculated according to set pair analysis, followed by the weight of each index using an information entropy weight method based on the principle of maximum entropy. Finally, the integrated degree of connection of all the samples is calculated, to provide a basis for classifying water samples and judging the adequacy of the water resources. To verify the validity and feasibility of this proposed model, the Ling'ao reservoir, which is situated in Guangdong province of China's southeastern monsoon zone and notable for its particularly heavy seepage is taken as a case study. The results indicate that the model is suitable for objectively analyzing dam seepage problems, especially given that the results match those obtained for the horizontal velocity as measured with drillings and the temperature distribution across the dam body. [ABSTRACT FROM AUTHOR]