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The Internet of things (IoT) has recently received a great deal of attention, and there has been a large increase in the number of IoT devices owing to its significance in current communication networks. In addition, the validation of devices is an important concern and a major safety demand in IoT systems, as any faults in the authentication or identification procedure will lead to threatening attacks that cause the system to close. In this study, a new, three-phase authentication protocol in IoT is implemented. The initial phase concerns the user registration phase, in which encryption takes place with a hybrid Elliptic Curve Cryptography (ECC)–Advanced Encryption Standard (AES) model with an optimization strategy, whereby key generation is optimally accomplished via a Self-Improved Aquila Optimizer (SI-AO). The second and third phases include the login process and the authentication phase, in which information flow control-based authentication is conducted. Finally, decryption is achieved based on the hybrid ECC–AES model. The employed scheme’s improvement is established using various metrics.

This study explores fine-grained feature-based face attribute recognition techniques to enhance the accuracy of face recognition in low-resolution and complex environments. The article proposes a global feature extraction method and a local texture feature extraction method to extract global and regional features by enhancing feature reuse and information flow through dense connectivity and ShuffleNet V2 framework. Then, a multiscale feature exchange method is used to fuse different scale features to enhance the capture of detail information. Finally, efficient feature integration is achieved by the multiscale feature fusion method. Experimental results on the CK+ and FER2013 datasets show that the accuracy of this method on face expression recognition reaches 97.24% and 95.93%, respectively, and the average recognition accuracy in the face attribute recognition experiments on the CelebA dataset is 97.11%, which is significantly better than the comparison algorithm. In addition, the analysis of the recognition effect on low-resolution faces shows that this paper’s method achieves a recognition accuracy of 54.03% at a resolution of 15 × 15 and a high accuracy of over 99% at resolutions of 70 × 70 and above. These results show that the face attribute recognition technique based on fine-grained features proposed in this paper significantly improves recognition accuracy.

The transition towards smart grid introduces the potential for revolutionary changes in the present energy management systems. It provides the grid with the necessary functionalities to transform into a decentralized energy system, and integrate large-scale variable renewable energy sources with enhanced demand-side management. Saudi Arabia is among the countries with significant potential to generate electricity from renewable energy sources, especially solar. The development of smart energy, smart communication, and smart information systems to establish a state-of-the art smart grid that enables not only security and automation but also has the flexibility of integrating technological innovation in the upcoming years. The model developed is characterized as government-led, commercially-driven, based on technological readiness, and community-oriented with numerous projects systematically initiated targeting innovation, human resources development, and industrial viability. Therefore, this paper aims to highlight the trends associated while considering the case scenario of the country and outlining the challenges and potential solutions with the establishment of smart grids.

Copyright of Journal of Fuzhou University is the property of Journal of Fuzhou University, Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Power Generation Technology is the property of Power Generation Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This research describes a detailed analysis of the enactment of a lean Tool, the Last Planner System during pandemic period which helps in optimizing resources for better coordination among all stakeholders in a pandemic period. The LPS, as it is known, focusses on minimizing factors such as uncertainties, barriers, and variability to make construction projects more flexible for better project management. These include variations and deviations, supervision, delays in approvals, change resistance, and subcontractor dedication, which are all related to various stakeholders in the project. Following that, a Design Science Research technique is used to evaluate the effect of applying the LPS in buildings to address and achieve the objective of reducing the impact of setbacks created by the stakeholders such as architects, consultants, clients, project management team, etc., during the implementation of the LPS during any pandemic so as not to reduce the progress of execution. An action strategy is being used to attain this goal, and four case studies are documented which concern the implementation of the LPS in the building of the Boy's Hostel, Research Scholar Quarters, Faculty Housing, and Girls' Hostel at Chennai, Tamilnadu, India. Data was gathered by observation of site activities, interviews, documentation analysis, and a questionnaire survey and grouped into various factors. While adopting the LPS tool in research, the factors affecting the implementation in the COVID-19 pandemic period were found. Further, these factors were analysed, measured, ranked, and validated for adopting in projects. [ABSTRACT FROM AUTHOR]

Replay attacks are among the most well-known attacks against vote privacy. Many e-voting systems have been proven vulnerable to replay attacks, including systems like Helios that are used in real practical elections. Despite their popularity, it is commonly believed that replay attacks are inefficient but the actual threat that they pose to vote privacy has never been studied formally. Therefore, in this paper, we precisely analyze for the first time how efficient replay attacks really are. We study this question from commonly used and complementary perspectives on vote privacy, showing as an independent contribution that a simple extension of a popular game-based privacy definition corresponds to a strong entropy-based notion. Our results demonstrate that replay attacks can be devastating for a voter's privacy even when an adversary's resources are very limited. We illustrate our formal findings by applying them to a number of real-world elections, showing that a modest number of replays can result in significant privacy loss. Overall, our work reveals that, contrary to a common belief, replay attacks can be very efficient and must therefore be considered a serious threat. [ABSTRACT FROM AUTHOR]

As improved recording technologies have created new opportunities for neurophysiological investigation, emphasis has shifted from individual neurons to multiple populations that form circuits, and it has become important to provide evidence of cross-population coordinated activity. We review various methods for doing so, placing them in six major categories while avoiding technical descriptions and instead focusing on high-level motivations and concerns. Our aim is to indicate what the methods can achieve and the circumstances under which they are likely to succeed. Toward this end, we include a discussion of four crosscutting issues: the definition of neural populations, trial-to-trial variability and Poisson-like noise, time-varying dynamics, and causality. [ABSTRACT FROM AUTHOR]

Replay attacks are among the most well-known attacks against vote privacy. Many e-voting systems have been proven vulnerable to replay attacks, including systems like Helios that are used in real practical elections. Despite their popularity, it is commonly believed that replay attacks are inefficient but the actual threat that they pose to vote privacy has never been studied formally. Therefore, in this paper, we precisely analyze for the first time how efficient replay attacks really are. We study this question from commonly used and complementary perspectives on vote privacy, showing as an independent contribution that a simple extension of a popular game-based privacy definition corresponds to a strong entropy-based notion. Our results demonstrate that replay attacks can be devastating for a voter's privacy even when an adversary's resources are very limited. We illustrate our formal findings by applying them to a number of real-world elections, showing that a modest number of replays can result in significant privacy loss. Overall, our work reveals that, contrary to a common belief, replay attacks can be very efficient and must therefore be considered a serious threat. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to analyze the scientific cooperation network of ontology researchers using social network indicators. It also investigates the correlation between centrality indicators and the productivity and efficiency of researchers in this field. Methodology: The present study is an applied research project that utilizes scientometric techniques and indicators. The social network analysis method was employed to illustrate and analyze the scientific cooperation network in the field of ontology. In order to retrieve the outputs related to the field of ontology, a search was conducted for the term "Ontolog*" in the title and subject fields of the Web of Science database from 1990 to July 2, 2021. The search strategy used is TI=(Ontolog*) OR TS=(Ontolog*). In the next step, the recovered findings were limited to research conducted in the fields of computer science, information science, and librarianship. This is because ontology is a subject that is also used in other fields such as philosophy, religious studies, biology, and so on. Despite the fact that the concept of ontology in this research is "a tool that specifies the concepts of the related field, their characteristics, and the relationships between concepts and characteristics, thereby increasing the semantic interaction between documents and sources to process complex, advanced, and text-sensitive questions." Among the retrieved data, original articles, conference articles, review articles, and editorials with more credibility were selected. In the end, 29,611 research articles were obtained. The retrieved records were entered into VOSviewer version 16.6.1 software for matrix design and visual map design. To standardize the names, Gephi software was used. The matrix designed in Gephi software was then imported, and micro-indexes of social network analysis were calculated. These measures included degree centrality, closeness centrality, betweenness centrality, and eigenvector centrality. The calculation of macro indicators for the co-authorship network in the field of ontology outputs was also performed using the UCINET software. To test the research hypotheses, SPSS software Version 24 was utilized. Due to the non-normal distribution of the data, a non-parametric test (Spearman's correlation) was used to test the hypotheses. Findings: Examination of the four measures of centrality, which include degree centrality, closeness centrality, betweenness centrality, and special vector centrality, revealed that Pascal Hitzler from Kansas State University, USA; Stefano Borgo from the Italian National Research Council; Jeff Z. Pan from the University of Edinburgh, Scotland; Stefan Schulz from the Medical University of Graz, Austria; Barry Smith from the University of Buffalo, USA; Nicola Guarino from the National Research Council of Italy; Ian Horrock from the University of Oxford, England; Bernardo Cuenca Grau from the University of Oxford, England; Heiner Stuckenschmidt from the University of Mannheim, Germany; and Jerome Euzenat from the Diderot University of Paris, France are the most influential researchers in the co-authorship network in the field of ontology. The analysis of the scientific cooperation network in the field of ontology using macro indicators of social network analysis revealed that the network is not cohesive. This is evident from the density value, which is less than one, and the high clustering coefficient of the network. Of course, the flow of information in this network is fast, based on the network's diameter and average distance. Additionally, the findings revealed a significant and positive correlation between centrality measures (such as degree, closeness, betweenness, and special vector centrality) and both the number of scientific productions and the number of citations. However, it is worth noting that the relationship between closeness centrality and scientific productions was found to be not significant. Conclusion: The results showed that the cooperation between researchers who know each other has led to a decrease in the diameter of the network and the average distance in the web of ontology co-authorship. As a result, the information flow in this network has intensified, overcoming the weaknesses of low density and high clustering coefficient. According to the results, when authors collaborate with colleagues they know in the field of ontology, the network diameter and average distance decrease, resulting in increased information flow in the ontology co-authorship network. This holds true even when the density and clustering coefficient are high. [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]

This study aims to assess the current status of green agricultural development and its influencing factors in Lincang City, a national innovation demonstration zone for sustainable development; it also seeks to enhance the potential and competitiveness of green agricultural development in underdeveloped border areas. To achieve this, an evaluation index system is constructed encompassing six dimensions. Using a coupled coordination and obstacle degree approach, this study explores the spatiotemporal differences in the level of green agricultural and sustainable development, as well as the power, coupled coordination degree, and factors that negatively impact green agricultural development in Lincang City from 2010 to 2019. The Liang-Kleeman information flow method is applied to uncover the key information flow factors that influence the coupled coordination degree in each county and district of Lincang City. The results reveal several insights: First, the comprehensive score of sustainable green agricultural development increased from 0.4405 to 0.5975 during the study period. Second, the coupling coordination degree of green agricultural development was relatively low, fluctuating between 0.1821 and 0.2816. Overall, the development has shifted from severe imbalance to mild imbalance. Third, the obstacle degree increased by 3.75%. From a systemic perspective, the "resource conservation" layer had the highest barrier level, with the maximum value being observed in Yun County at 25.5%. Further analysis of the indicators reveals that the use of outdated water-saving irrigation techniques has resulted in low irrigation efficiency and excessive water resource waste. This is the main cause of the high barrier levels in terms of water-saving irrigation intensity and effective irrigation area. Moreover, the excessive use of chemical pesticides to enhance vegetable production has contributed to high barrier levels for achieving yields of pollution-free vegetable production per unit area. Finally, the information flow values of the factors influencing the coordinated and harmonious development of green agriculture exhibit significant regional heterogeneity among counties and districts. The highest information flow value for the area of drought- and flood-resistant crop cultivation is in Zhengkang County at 1.86. Based on these results, local government departments and decision-makers should focus on promoting comprehensive improvements in the level of green agricultural development. It is crucial to tailor measures to the specific needs of each county to address the shortcomings in green agricultural development. Additionally, efforts should be made to strengthen the innovation-driven chain of green agricultural development, including production, processing and sales. Enhancing the green agricultural development system is essential for long-term progress. [ABSTRACT FROM AUTHOR]

This paper investigates the fact that construction projects, due to their specificity, are complex, temporary and dynamic. Over their course, participants change, successive construction works are done and new information becomes available. This carries over to difficulties in communication. In the literature, numerous studies note the fact that a network-based approach to the analysis and monitoring of communication as a part of complex construction projects is commendable. Relations between agents, knowledge and tasks in the context of communication within a construction project can be visualized in the form of a meta-network, and suitably developed structural measures can be used to analyze them. In this paper, the authors used meta-network theory to analyze relations between project participants, knowledge and tasks in the context of communication within a construction project, on the basis of the construction of a housing estate located in Katowice, Poland. Meta-network structural analysis allowed for a deeper understanding of these relations and the detection of essential information about the level of communication in the project under investigation, which was a basis for further discussion. The authors also stress the benefits from the approach presented and argue that it should be a starting point for effective management in the sphere of communication in construction companies.

The information diffusion and interactions within financial markets have a significant impact on the price discovery process and the sentiment and risk dispersion. Despite its importance, limited research has been conducted on information flow dynamics within the Tehran Stock Exchange, which is a vital component of Iran's capital market. This study aims to fill this gap by examining the information flow dynamics among 39 major industries from March 27, 2010, to June 21, 2023. Effective transfer entropy is employed to quantify the intensity of information flow between industry indices. Sequence of information matrices are constructed using rolling one-week windows over one-year periods. Given the occurrence of critical events during the research period, their influence on information flow dynamics is analyzed using Frobenius distance-based k-nearest neighbor networks, Influence Strength analysis, and threshold networks. The findings reveal that the effective transfer entropy matrix exhibits time-varying characteristics and remains stable throughout most periods. Furthermore, critical events significantly impact information flow dynamics, with abnormal values of Influence Strength associated with market volatility and major events. Additionally, the dominant source of information in the information flow network changes over time, highlighting the transient nature of industry dominance within the network.IntroductionThe diffusion of information and interactions within financial markets greatly influences the price discovery process and affects sentiment and risk dispersion. The potential for growth in the Tehran Stock Exchange (TSE) through the introduction of innovative financial instruments can offer investors additional investment opportunities. Therefore, understanding the dynamics of information transmission within the market aids investors in decision-making.Existing literature suggests that stock price volatilities are interconnected, and stocks within the same industry often exhibit high correlations. Additionally, industry stock price indexes within the market can serve as leading indicators of economic activity. Analyzing the information flow network at the industry index level holds significant implications for investors, portfolio managers, and policymakers seeking to devise appropriate risk-mitigating strategies, especially industry sector rotation strategies.Despite the Tehran Stock Exchange being a vital component of Iran's capital market, there has been limited research on the information flow network between industries and its time-varying characteristics. Furthermore, despite significant events occurring during the specified sample period, there is a lack of empirical evidence regarding their impacts on information flow within the Tehran securities market.Methods and MaterialIn this research, the dynamics of information flow between the 39 major industries are investigated from March 27, 2010, to June 21, 2023. Following Ni (2023), the Effective transfer entropy that measures the intensity of information flow between industries indices is calculated. Then the sequence of information matrices is created by rolling a one-week calculation window. In this paper, the calculation window of 237-trading day widths and the rolling window of 5-day widths are used to calculate the information matrices of length 591. Moreover, using quantiles of return series, and , the information matrix sequences are constructed.Given that the research period encompasses critical events, their influence on information flow is examined using various methodologies, including the Frobenius distance-based k-nearest neighbor network, Influence Strength (IS) analysis, and a threshold-directed network of information matrices. Results and DiscussionUpon depicting the Frobenius distance matrix based on Q1, significant shifts in the distance between the information matrices are observed. These shifts often coincide with critical events that have impacted the market.The IS series graph over the research period reveals several local peaks. For some peaks, no significant events occurred during the research period. Peak 2, however, corresponds to severe market fluctuations and turmoil, primarily stemming from the global impact of the 2008 financial crisis. Additionally, this time window aligns with the initial period of oil and petrochemical sanctions against Iran, leading to a decline in the total index of the TSE. Peak 4 reflects a decrease in the TSE's total index following Iran's nuclear agreement with the P5+1 in 2015 (post-JCPOA). During peak period 5, coinciding with the US withdrawal from the JCPOA and the re-imposition of all US sanctions, the TSE's total index experienced a drop. Peaks 1, 3, and 7 correspond to the bursting of stock price bubbles in 2009, 2013, and 2020, respectively.The findings also highlight that the window corresponding to the maximum value of IS (0.1757) is from 31/12/2012 to 7/1/2014, coinciding with the bursting of the stock price bubble in January 2014. Peak 6 corresponds to the window from 19/7/2020 to 7/7/2021, which includes the early days of the COVID-19 pandemic. Lastly, from 1/6/2022 to 3/6/2023, the government's decision to abolish the preferential exchange rate for importing basic goods negatively affected the prices of some listed companies in the TSE and the indexes of related industries. Comparing the patterns of IS calculated based on Q1 vs. Q2 demonstrates the correspondence between the local peaks.On the other hand, examining the Financial industry (node 37), the series reached its peak during 2/2/2016-25/1/2017. During this period, the TSE faced a significant decline in the total index due to uncertainty caused by the JCPOA. Analysis of the directional network of the information matrix, filtered with a threshold of 0.01, reveals that in the post-JCPOA period, there is an information flow between the Financial industry and all other industries except the Furniture industry (node 20) and Peymankari industry (node 26).Furthermore, aside from node 37, which serves as the central node during this period, node 34 (Banking industry, deg = 34), node 39 (EstekrajeNaft industry, deg=33), and node 35 (SayerMali industry, deg =32) also exhibit high degrees. Additionally, the network constructed from the information matrix corresponding to peak 6 indicates several central nodes. However, during the time window corresponding to peak 6, node 24 (Daroee industry) with the highest (0.0105) exerts the strongest influence on the network.The results also demonstrate that for certain industries, such as the Pharmaceutical industry, the value of increased during the 19/1/2016-11/1/2017 period, corresponding to the post-JCPOA era. However, for other industries, the maximum value of occurred mainly during other critical periods, such as the stock price bubble bursts in 2010 and 2014 and the imposition of new sanctions against Iran..ConclusionThe findings indicate that the effective transfer entropy matrix exhibits time-varying characteristics and remains stable over the majority of periods. Additionally, critical events have notably impacted the dynamics of information flow, with abnormal values of Influence Strength correlating with market volatility and significant events. Moreover, the primary source of information in the sequence of the information flow network evolves over time, suggesting that the dominant industry in the network is not consistently sustainable.

Security researchers utilize taint analyses to uncover suspicious behaviors in Android apps. Current static taint analyzers cannot handle ICC, reflection, and lifecycles dependably, increasing the result verification cost. On the other hand, current dynamic taint trackers accurately detect execution paths. However, they depend on specific Android versions and modified devices, reducing their usability and applicability. In addition, they require app exercise every time running the taint analysis. This paper presents a new dynamic taint tracker called T-Recs, tracking information flows by recording and reconstructing the app execution. First, before the taint analysis, the app’s runtime data are obtained by instrumenting logging code into the app’s bytecode and running the app to be independent of specific Android versions and devices. Then, T-Recs performs the taint analysis accurately with the logged data and separately from the app exercise. This paper is an extended version of our work published. Previously, T-Recs’ accuracy was mainly evaluated in privacy leak detection. The results show that T-Recs outperforms compared analyzers, which are FlowDroid (w/ and w/o IC3), Amandroid, DroidSafe, and TaintDroid (w/ and w/o IntelliDroid). This paper also involves DroidRA and IccTA. This paper shows that T-Recs detects ICC- and reflection-related leaks missed by FlowDroid in popular Google Play apps. The other static analyzers fail to analyze most of the apps. These experiments also indicate an advantage of T-Recs: its users can re-execute T-Recs’ taint analysis without re-exercising the app. T-Recs’ app-runtime overhead and parallel execution performance were also evaluated, and the results are acceptable.

In musculoskeletal systems, describing accurately the coupling direction and intensity between physiological electrical signals is crucial. The maximum information coefficient (MIC) can effectively quantify the coupling strength, especially for short time series. However, it cannot identify the direction of information transmission. This paper proposes an effective time-delayed back maximum information coefficient (TDBackMIC) analysis method by introducing a time delay parameter to measure the causal coupling. Firstly, the effectiveness of TDBackMIC is verified on simulations, and then it is applied to the analysis of functional cortical-muscular coupling and intermuscular coupling networks to explore the difference of coupling characteristics under different grip force intensities. Experimental results show that functional cortical-muscular coupling and intermuscular coupling are bidirectional. The average coupling strength of EEG $\rightarrow $ EMG and EMG $\rightarrow $ EEG in beta band is 0.86 ± 0.04 and 0.81 ± 0.05 at 10% maximum voluntary contraction (MVC) condition, 0.83 ± 0.05 and 0.76 ± 0.04 at 20% MVC, and 0.76 ± 0.03 and 0.73 ± 0.04 at 30% MVC. With the increase of grip strength, the strength of functional cortical-muscular coupling in beta frequency band decreases, the intermuscular coupling network exhibits enhanced connectivity, and the information exchange is closer. The results demonstrate that TDBackMIC can accurately judge the causal coupling relationship, and functional cortical-muscular coupling and intermuscular coupling network under different grip forces are different, which provides a certain theoretical basis for sports rehabilitation.

This study analyzes the information flow between the palm oil, soybean oil, and crude oil futures markets and proposes ways to develop the vegetable oil market, especially the palm oil futures market. In contrast to the results of a Granger causality test, we provide strong evidence that interactive cause–effect relationships exist between each pair of the three oil futures markets. Yet, the emergence of the net information flow depends on the trading volume and liquidity (i) from the crude oil futures to the two vegetable oil futures markets and (ii) from the soybean oil futures to the palm oil futures market. Our findings support the fact that the palm oil futures market is still subject to tight investment constraints and, therefore, fails to play a leading role in information discovery regarding the crude oil and soybean oil futures markets. This suggests that policymakers could pursue a series of initiatives, such as introducing US dollar-based trading and extending trading hours, to further develop the palm oil futures market and reduce market friction.

The Chengdu–Chongqing urban agglomeration (CCUA), as the only national-level city cluster in southwestern China, serves as a strategic support for the Yangtze River Economic Belt and an important demonstration area for promoting new urbanization in the country. The study of the networked characteristics of the CCUA contributes to a systematic understanding of its spatial connectivity patterns, optimization of spatial structure and layout, and promotion of high-quality regional development. In this study, we constructed models for traffic flow, information flow, migration flow, and composite flow to calculate the strength of connections between cities and the total flow of various elements in the CCUA. ArcGIS spatial visualization tools were used to depict the spatial connectivity patterns of the element flows within the CCUA. Additionally, social network analysis methods, including network density, centrality, and cohesive subgroups analysis, were employed to reveal the spatial network structure characteristics of the CCUA. The findings are as follows: (1) The overall structure of the cities within the CCUA is relatively loose, with significant differences in connectivity strength. It exhibits a west-strong and east-weak pattern, with Chengdu-Chongqing, Chengdu-Deyang, Chengdu-Mianyang, and Chengdu-Meishan occupying the top tier, while Zigong and Ya'an have relatively weak connections with other cities. Chengdu and Chongqing have prominent positions in the CCUA, with Chengdu having a more prominent core position compared to Chongqing, resulting in an overall hierarchical distribution of “1 + 1+7 + 7”. (2) The network density of the element flows in the CCUA is relatively low, indicating a generally weak element connectivity. The centrality of cities other than Chengdu and Chongqing is at a moderate to lower level, suggesting a weak overall resource connectivity capacity in the CCUA. (3) Most cities tend to form cohesive subgroups based on geographic proximity, while the cohesive subgroup in Chongqing is still in its early stages of development due to administrative boundaries. The research results quantitatively depict the spatial network structure characteristics of the CCUA, providing theoretical references for its development.

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]

There is a lack of knowledge of the interactions required over time among the members of a building project design team. Without a specific target, it is impossible to identify gaps to propose improvement plans in the coordination and management of projects in the early stages. Therefore, this study proposes a model of the required interaction among the members of a building project design team during the different phases of the design process. The research was divided into three stages: (1) design team interactions; (2) construction of ideal interaction networks and proposed evaluation model; and (3) pilot cases—evaluation and analysis. Through this study, eight ideal networks were constructed, four for each interaction (information flow, collaboration, and coordination) and one for each design phase. In addition, a series of metrics were proposed to evaluate the current state of a building project, which, together with the constructed model networks, allowed the development of an evaluation method for real projects. Finally, two pilot cases were used to exemplify the use of the proposed model tool. [ABSTRACT FROM AUTHOR]

In musculoskeletal systems, describing accurately the coupling direction and intensity between physiological electrical signals is crucial. The maximum information coefficient (MIC) can effectively quantify the coupling strength, especially for short time series. However, it cannot identify the direction of information transmission. This paper proposes an effective time-delayed back maximum information coefficient (TDBackMIC) analysis method by introducing a time delay parameter to measure the causal coupling. Firstly, the effectiveness of TDBackMIC is verified on simulations, and then it is applied to the analysis of functional cortical-muscular coupling and intermuscular coupling networks to explore the difference of coupling characteristics under different grip force intensities. Experimental results show that functional cortical-muscular coupling and intermuscular coupling are bidirectional. The average coupling strength of EEG $\rightarrow $ EMG and EMG $\rightarrow $ EEG in beta band is 0.86 ± 0.04 and 0.81 ± 0.05 at 10% maximum voluntary contraction (MVC) condition, 0.83 ± 0.05 and 0.76 ± 0.04 at 20% MVC, and 0.76 ± 0.03 and 0.73 ± 0.04 at 30% MVC. With the increase of grip strength, the strength of functional cortical-muscular coupling in beta frequency band decreases, the intermuscular coupling network exhibits enhanced connectivity, and the information exchange is closer. The results demonstrate that TDBackMIC can accurately judge the causal coupling relationship, and functional cortical-muscular coupling and intermuscular coupling network under different grip forces are different, which provides a certain theoretical basis for sports rehabilitation. [ABSTRACT FROM AUTHOR]

The relevance of this article is due to the fact that the information explosion, the digital age presents us with a completely different set of tools, provided services and mechanisms, and the development of activities with a new logic. The article refers to the significance and role of the library sector in the life of 21st century societies. In the era of information explosion, one of the greatest challenges of the library sector is the influence of artificial intelligence, which reduces the importance of the human factor. Thus, the circumstance of the librarian carrying out a function in the library is reduced incomparably, despite the fact that the reader gets any information he needs, saving space and time. This conjuncture gives rise to deep thinking among representatives of the library sector. In this case, each library should study the international experience, prospectively repeating what is useful, which will only improve the latter's work. This should be done taking into account a number of features of what kind of library we are dealing with, national, academic, scientific, children's, etc. However, with good library management, the right strategy and the necessary resources, it is possible to turn challenges into favorable conditions, taking advantage of new and limitless opportunities. [ABSTRACT FROM AUTHOR]

Copyright of Synesis is the property of Synesis and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Arid Land Geography is the property of Chinese Academy of Sciences, Xinjiang Institute of Ecology & Geography and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Social networks form a major parts of people’s lives, and individuals often make important life decisions based on information that spreads through these networks. For this reason, it is important to know whether individuals from different protected groups have equal access to information flowing through a network. In this article, we define the Information Unfairness (IUF) metric, which quantifies inequality in access to information across protected groups. We then introduce MinIUF, an algorithm for reducing inequalities in information flow by adding edges to the network. Finally, we provide an in-depth analysis of information flow with respect to an attribute of interest, such as gender, across different types of networks to evaluate whether the structure of these networks allows groups to equally access information flowing in the network. Moreover, we investigate the causes of unfairness in such networks and how it can be improved. [ABSTRACT FROM AUTHOR]

Using functional neuroimaging, electrophysiological techniques and neural data processing techniques, neuroscientists have found that mathematically gifted adolescents exhibit unusual neurocognitive features in the activation of task-related brain regions. Hemispheric information interaction, functional reorganization of networks, and utilization of task-related brain regions are beneficial to rapid and efficient task processing. Based on Granger causality channel selection, the transfer entropy (TE) value between effective channels was computed, and the information flow patterns in the directed functional brain networks derived from electroencephalography (EEG) data during deductive reasoning tasks were explored. We evaluated the workspace configuration patterns of the brain network and the global integration characteristics of separated brain regions using node strength, motif, directed clustering coefficient and characteristic path length in the brain networks of mathematically gifted adolescents with effective connectivity. The empirical results demonstrated that a more integrated functional network at the global level and a more efficient clique at the local level support a pattern of workspace configuration in the mathematically gifted brain that is more conducive to task-related information processing. [ABSTRACT FROM AUTHOR]

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With the rapid development of the idea of integration and cooperation and the proliferation of information systems and technology in supply chains, the importance of information, and its management are becoming more and more evident. In this chapter, we explore how the instruments used in a circuit network be applicable to a supply chain network. We borrow the laws developed in electric circuit theory and conceptually explain the relationship and open discussion on how to apply it for future researchers. [ABSTRACT FROM AUTHOR]

Electroconvulsive therapy is effectively used for treatment-resistant depression; however, its neural mechanism is largely unknown. Resting-state functional magnetic resonance imaging is promising for monitoring outcomes of electroconvulsive therapy for depression. This study aimed to explore the imaging correlates of the electroconvulsive therapy effects on depression using Granger causality analysis and dynamic functional connectivity analyses. We performed advanced analyses of resting-state functional magnetic resonance imaging data at the beginning and intermediate stages and end of the therapeutic course to identify neural markers that reflect or predict the therapeutic effects of electroconvulsive therapy on depression. We demonstrated that information flow between the functional networks analyzed by Granger causality changes during electroconvulsive therapy, and this change was correlated with the therapeutic outcome. Information flow and the dwell time (an index reflecting the temporal stability of functional connectivity) before electroconvulsive therapy are correlated with depressive symptoms during and after treatment. First, the sample size was small. A larger group is needed to confirm our findings. Second, the influence of concomitant pharmacotherapy on our results was not fully addressed, although we expected it to be minimal because only minor changes in pharmacotherapy occurred during electroconvulsive therapy. Third, different scanners were used the groups, although the acquisition parameters were the same; a direct comparison between patient and healthy participant data was not possible. Thus, we presented the data of the healthy participants separately from that of the patients as a reference. These results show the specific properties of functional brain connectivity. • Information flow between networks changes during electroconvulsive therapy (ECT). • This change was correlated with the therapeutic outcome. • Information flow before ECT correlated with depressive symptoms during and after ECT. • Dwell time before ECT correlated with depressive symptoms during and after ECT. [ABSTRACT FROM AUTHOR]

Copyright of Ciência da Informação is the property of Instituto Brasileiro de Informacao em Ciencia e Tecnologia (IBICT) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The dynamic flow of information and rapidly developing technologies have a significant impact on today's knowledge society. Following the trends in technology and user behavior, libraries as part of this community are also changing. They are transforming the way they gather information, their architectural environment, and the services they offer. Thus, from guardians of knowledge, they are becoming centers of learning. Their passivity remains in the past and they are on the path of active communication with readers, combined with digitization. The subject of the article is the academic library of the University of Economics - Varna, and the topic of research is the direction of transition as a result of the changing environment. The aim is to trace the opportunities and services that the institution provides to its readers and the prospects for development. [ABSTRACT FROM AUTHOR]