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Background: Adverse events occur frequently at emergency departments (ED) because of several risk factors related to varying conditions. It is still unclear, which factors lead to patient safety incident reports. The aim of this study was to explore the root causes behind ED-associated patient safety incidents reported by personnel, and based on the findings, to suggest learning objectives for improving patient safety. Methods: The study material included incident reports (n = 340) which concerned the ED of a teaching hospital over one year. We used a mixed method combining quantitative descriptive statistics and qualitative research by inductive content analysis and deductive Ishikawa root cause analysis. Results: Most (76.5%) incidents were reported after patient transfer from the ED. Nurses reported 70% of incidents and physicians 7.4%. Of the reports, 40% were related to information flow or management. Incidents were evaluated as no harm (29.4%), mild (46%), moderate (19.7%), and severe (1.2%) harm to the patient. The main consequences for the organization were reputation loss (44.1%) and extra work (38.9%). In the qualitative analysis, nine specific problem groups were found: insufficient introduction, adherence to guidelines and protocols, insufficient human resources, deficient professional skills, medication management deficiencies, incomplete information transfer from the ED, language proficiency, unprofessional behaviour, identification error, and patient-dependent problems. Six organizational themes were identified: medical staff orientation, onboarding and competence requirements; human resources; electronic medical records and information transfer; medication documentation system; interprofessional collaboration; resources for specific patient groups such as geriatric, mental health, and patients with substance abuse disorder. Entirely human factor-related themes could not be defined because their associations with system factors were complex and multifaceted. Individual and organizational learning objectives were addressed, such as adherence to the proper use of instructions and adequate onboarding. Conclusions: System factors caused most of the patient safety incidents reported concerning ED. The introduction and training of ED -processes is elementary, as is multiprofessional collaboration. More research is needed about teamwork skills, patients with special needs and non-critical patients, and the reporting of severe incidents. [ABSTRACT FROM AUTHOR]

Successful retirement planning critically depends on access to accurate and up-to-date information. In this paper, we focus on the Australian Superannuation industry to examine the influence of digital technology in facilitating communication and information flow among its various actors. Using a qualitative research methodology, we conducted 22 semi-structured interviews with various industry actors including Superfunds, fund members, consultants, IT and digital solutions providers, and representatives from industry regulators. Our findings highlight the need for these actors to enhance their resource and knowledge-sharing capabilities, consumer need recognition, and information flow to ultimately enable Superfund members to improve their retirement planning and financial well-being. JEL Classification: D14 Household Saving • Personal Finance, D15 Intertemporal Household Choice • Life Cycle Models and Saving, J26 Retirement • Retirement Policies and J32 Nonwage Labor Costs and Benefits • Retirement Plans • Private Pensions [ABSTRACT FROM AUTHOR]

Sleep is a highly stereotyped phenomenon, requiring robust spatiotemporal coordination of neural activity. Understanding how the brain coordinates neural activity with sleep onset can provide insights into the physiological functions subserved by sleep and the pathologic phenomena associated with sleep onset. We quantified whole-brain network changes in synchrony and information flow during the transition from wakefulness to light non-rapid eye movement (NREM) sleep, using MEG imaging in a convenient sample of 14 healthy human participants (11 female; mean 63.4 years [SD 11.8 years]). We furthermore performed computational modeling to infer excitatory and inhibitory properties of local neural activity. The transition from wakefulness to light NREM was identified to be encoded in spatially and temporally specific patterns of long-range synchrony. Within the delta band, there was a global increase in connectivity from wakefulness to light NREM, which was highest in frontoparietal regions. Within the theta band, there was an increase in connectivity in fronto-parieto-occipital regions and a decrease in temporal regions from wakefulness to Stage 1 sleep. Patterns of information flow revealed that mesial frontal regions receive hierarchically organized inputs from broad cortical regions upon sleep onset, including direct inflow from occipital regions and indirect inflow via parieto-temporal regions within the delta frequency band. Finally, biophysical neural mass modeling demonstrated changes in the anterior-to-posterior distribution of cortical excitation-to-inhibition with increased excitation-to-inhibition model parameters in anterior regions in light NREM compared with wakefulness. Together, these findings uncover whole-brain corticocortical structure and the orchestration of local and long-range, frequency-specific cortical interactions in the sleep-wake transition.SIGNIFICANCE STATEMENT Our work uncovers spatiotemporal cortical structure of neural synchrony and information flow upon the transition from wakefulness to light non-rapid eye movement sleep. Mesial frontal regions were identified to receive hierarchically organized inputs from broad cortical regions, including both direct inputs from occipital regions and indirect inputs via the parieto-temporal regions within the delta frequency range. Biophysical neural mass modeling revealed a spatially heterogeneous, anterior-posterior distribution of cortical excitation-to-inhibition. Our findings shed light on the orchestration of local and long-range cortical neural structure that is fundamental to sleep onset, and support an emerging view of cortically driven regulation of sleep homeostasis.

Information on Land Use and Land Cover Map (LULCM) is essential for environment and socioeconomic applications. Such maps are generally derived from Multispectral Remote Sensing Images (MRSI) via classification. The classification process can be described as information flow from images to maps through a trained classifier. Characterizing the information flow is essential for understanding the classification mechanism, providing solutions that address such theoretical issues as “what is the maximum number of classes that can be classified from a given MRSI?” and “how much information gain can be obtained?” Consequently, two interesting questions naturally arise, i.e. (i) How can we characterize the information flow? and (ii) What is the mathematical form of the information flow? To answer these two questions, this study first hypothesizes that thermodynamic entropy is the appropriate measure of information for both MRSI and LULCM. This hypothesis is then supported by kinetic-theory-based experiments. Thereafter, upon such an entropy, a generalized Jarzynski equation is formulated to mathematically model the information flow, which contains such parameters as thermodynamic entropy of MRSI, thermodynamic entropy of LULCM, weighted F1-score (classification accuracy), and total number of classes. This generalized Jarzynski equation has been successfully validated by hypothesis-driven experiments where 694 Sentinel-2 images are classified into 10 classes by four classical classifiers. This study provides a way for linking thermodynamic laws and concepts to the characterization and understanding of information flow in land cover classification, opening a new door for constructing domain knowledge.

Copyright of Meta: Avaliação is the property of Revista Meta: Avaliacao 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.)

Information on Land Use and Land Cover Map (LULCM) is essential for environment and socioeconomic applications. Such maps are generally derived from Multispectral Remote Sensing Images (MRSI) via classification. The classification process can be described as information flow from images to maps through a trained classifier. Characterizing the information flow is essential for understanding the classification mechanism, providing solutions that address such theoretical issues as "what is the maximum number of classes that can be classified from a given MRSI?" and "how much information gain can be obtained?" Consequently, two interesting questions naturally arise, i.e. (i) How can we characterize the information flow? and (ii) What is the mathematical form of the information flow? To answer these two questions, this study first hypothesizes that thermodynamic entropy is the appropriate measure of information for both MRSI and LULCM. This hypothesis is then supported by kinetic-theory-based experiments. Thereafter, upon such an entropy, a generalized Jarzynski equation is formulated to mathematically model the information flow, which contains such parameters as thermodynamic entropy of MRSI, thermodynamic entropy of LULCM, weighted F1-score (classification accuracy), and total number of classes. This generalized Jarzynski equation has been successfully validated by hypothesis-driven experiments where 694 Sentinel-2 images are classified into 10 classes by four classical classifiers. This study provides a way for linking thermodynamic laws and concepts to the characterization and understanding of information flow in land cover classification, opening a new door for constructing domain knowledge. [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]

Identifying and quantifying unexpected events in non-equilibrium systems is critical work that is necessary for systems managers to make well-informed decisions, particularly when forecasting rare and extreme events. In this paper neural networks are integrated to increase the predictive capacity of information theory. Two information theory techniques, “Information Length (IL) and Information Flow (IF)”, are being examined for their sensitivity to rapid changes. To simulate the first occurrence of extreme and rare events, we utilize a nonautonomous Kramer model to introduce a perturbation. we introduced a Dynamic Osprey Long Short-Term Memory (DOLSTM) for predicting rare and extreme events in non-equilibrium systems. Our results show that IL performs better than IF in accurately forecasting unexpected occurrences when combined with a neural network. This study highlights a novel integration between information theory & neural networks, giving an effective strategy for forecasting rare & extreme events in non-equilibrium environments. An effective methodology for identifying and forecasting the behavior of dynamic systems is established by combining information-length diagnostics with neural network predictions, especially in situations involving rare and extreme events. This novel method illustrates that the theory of information and neural networks can be used to provide robust predictions for dynamic systems, when encountering rare and extreme events. [ABSTRACT FROM AUTHOR]

The purpose of the study is to identify the impact of social media on the formation of public opinion in Ukraine. In the context of the war in Ukraine, public opinion plays a crucial role in resistance, morale, and support for government policies. This study examines the impact of social media use on the formation of Ukrainian public opinion during the conflict. Using a survey methodology (N=310), the relationship between social media use, interpersonal trust (ITS), and Fear of Missing Out (FOMO) was assessed. Results reveal that active social media use is associated with lower interpersonal trust and a pronounced FOMO syndrome (R²=0.571), suggesting a reduced ability for critical evaluation of information. These findings have implications for the development of communication strategies, public policies, and psychosocial interventions aimed at counteracting disinformation and promoting informed public opinion in times of conflict. [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 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. [ABSTRACT FROM AUTHOR]

The volume of flow demand in cyber-physical power systems (CPPSs) fluctuates unevenly across coupled networks and is susceptible to congestion or overload due to consumers' energy demand or extreme disasters. Therefore, considering the elasticity of real networks, communication links with excessive information flow do not immediately disconnect but have a certain degree of redundancy. This paper proposes a dynamic cascading failure iterating model based on the distribution of information flow overload in a communication network and power flow betweenness in the physical power grid. First, a nonlinear load capacity model of a communication network with overload and weighted edges is introduced, fully considering the three link states: normal, failure, and overload. Then, flow betweenness substitutes for branch flows in the physical power network, and power flow on failed lines is redistributed using the load capacity model, simplifying the calculations. Third, under the influence of coupling relations, a comprehensive model based on improved percolation theory is constructed, with attack strategies formulated to more accurately assess the coupled networks. Simulations on the IEEE-39 bus system demonstrate that considering the overload capacity of communication links on a small scale enhances the robustness of coupled networks. Furthermore, deliberate link attacks cause more rapid and extensive damage compared to random attacks. [ABSTRACT FROM AUTHOR]

In recent years, the growing incidence of blended and online learning has highlighted instructional design concerns, especially STEM instructional design. Existing studies have often adopted observations, questionnaires, or interviews to evaluate STEM instructional design plans. However, there is still a lack of quantitative, measurable, and objective assessment methods. To close this research gap, this study proposes an innovative method for assessing STEM instructional design plans based on information flows. In this study, 20 STEM instructional design plans were designed, analysed, and assessed using the proposed information flow-based assessment method. The results indicated that the proposed method is feasible and effective for assessing STEM instructional design plans. STEM instructional design plans could be significantly improved based on the proposed method. The results and implications for instructors and practitioners are discussed in depth.

This study analyzes the impact of the information environment (IE) and credit default swap (CDS) transaction costs on information transmission between the stock and CDS markets. Using the daily regression analysis on the Korean firm’s stock and CDS data from 2004 to 2023, the results show that companies with superior IE in the stock market exhibit a larger and more sensitive total information flow from the stock market to the CDS market. Companies with lower transaction costs in the CDS market demonstrate faster information flow. In the case of companies with superior IE, fundamental information is reflected in stock prices with high weight and thus the CDS spreads change reflecting information about stock prices. According to this study’s findings, the primary factor influencing the information flow from the stock market to the CDS market is the information environment of the company in the stock market, rather than transaction costs in the CDS market.

The stock option-implied volatility skew reflects both the structural risk characteristics of the underlying company and the short-term information flow about the stock price movement. This paper builds a semistructural, cross-sectional option pricing model to separate the structural risk contributions from the information flow. The model identifies two structural risk sources that contribute to the cross-sectional variation of the skew: the company's business cyclicality and its default risk. The model can explain as much as 44% of the cross-sectional variation in implied volatility skew and is particularly informative during and after recessions. The remaining skew variation reflects mainly short-term information flow and can be used to construct stock portfolios with much better investment performance and without hidden structural risk exposures. This paper was accepted by Agostino Capponi, finance. Funding: L. Wu gratefully acknowledges support by a grant from the City University of New York PSC-CUNY Research Award Program. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2023.4872. [ABSTRACT FROM AUTHOR]

Although decades of effort have been devoted to building Physical‐Conceptual (PC) models for predicting the time‐series evolution of geoscientific systems, recent work shows that Machine Learning (ML) based Gated Recurrent Neural Network technology can be used to develop models that are much more accurate. However, the difficulty of extracting physical understanding from ML‐based models complicates their utility for enhancing scientific knowledge regarding system structure and function. Here, we propose a physically interpretable Mass‐Conserving‐Perceptron (MCP) as a way to bridge the gap between PC‐based and ML‐based modeling approaches. The MCP exploits the inherent isomorphism between the directed graph structures underlying both PC models and GRNNs to explicitly represent the mass‐conserving nature of physical processes while enabling the functional nature of such processes to be directly learned (in an interpretable manner) from available data using off‐the‐shelf ML technology. As a proof of concept, we investigate the functional expressivity (capacity) of the MCP, explore its ability to parsimoniously represent the rainfall‐runoff (RR) dynamics of the Leaf River Basin, and demonstrate its utility for scientific hypothesis testing. To conclude, we discuss extensions of the concept to enable ML‐based physical‐conceptual representation of the coupled nature of mass‐energy‐information flows through geoscientific systems. Plain Language Summary: We develop a physically interpretable computational unit, referred to as the Mass‐Conserving‐Perceptron (MCP). Networks of such units can be used to model the conservative nature of the input‐state‐output dynamics of mass flows in geoscientific systems, while Machine Learning (ML) technology can be used to learn the functional nature of the physical processes governing such system behaviors. Testing using data from the Leaf River Basin demonstrates the considerable functional expressivity (capacity) and interpretability of even a single‐MCP‐node‐based model, while providing excellent predictive performance and the ability to conduct scientific hypothesis testing. The concept can easily be extended to facilitate ML‐based physical‐conceptual representation of the coupled nature of mass‐energy‐information flows through geoscientific systems, thereby facilitating the development of synergistic physics‐AI modeling approaches. Key Points: We develop a physically interpretable unit (Mass‐Conserving‐Perceptron) that can be used as a basic component of geoscientific modelsOff‐the‐shelf Machine Learning technology can be used to learn the functional nature of the physical processes governing system behaviorsThe concept can be extended to facilitate ML‐based representation of coupled mass‐energy‐information flows in geoscientific systems [ABSTRACT FROM AUTHOR]

This paper examines whether sell‐side analysts' interactions with buy‐side analysts influence the quality of sell‐side research output. We hypothesise that these interactions offer the sell side a view of the buy side's private information, which enhances the quality of sell‐side research. Our findings show that analyst earnings forecast accuracy improves with these interactions with diminishing returns. Results are robust to alternative proxies for research quality and information flow from buy‐side to sell‐side analysts. Additional tests rule out endogeneity concerns, strengthening the inference that feedback from interactions with buy‐side analysts improves the quality of sell‐side research output. [ABSTRACT FROM AUTHOR]

(1) Background: A current trend observed in the logistics sector is the use of Industry 4.0 tools to improve and enhance the efficiency of cargo handling processes. One of the popular solutions is an augmented reality system that supports operators in everyday tasks. The article aims to present design assumptions for implementing an augmented reality system to support air cargo handling at the warehouse. (2) Methods: Research was carried out based on a five-stage analytical procedure, aiming to analyze the current state and identify the potential for implementing the AR system. The following methods were used to collect data: co-participant observations, process analysis, direct interviews, analysis of internal documentation, and applicable legal regulations. (3) Results: The conducted research allowed for identifying information flows accompanying cargo flows and developing a project to automate selected information flows. The obtained results made it possible to identify operations for which the AR system's implementation will increase their effectiveness and efficiency. (4) Conclusions: The obtained results identified the need to develop a hybrid algorithm for arranging cargo in the warehouse and to build a system supporting self-verification of markings on air cargo. [ABSTRACT FROM AUTHOR]

This paper seeks to analyze the information flow between the Bourse Régionale des Valeurs Mobilières (BRVM) and Environmental, Social, and Governance (ESG) stocks, focusing on the time and frequency domains. By studying these aspects, we aim to gain a deeper understanding of how information is transmitted between BRVM and ESG stocks, shedding light on the dynamics and interactions within this context. The study analyzes the decomposed daily returns of four indices: BRVM Composite Index (BRVMCI), BRVM 10 Index (BRVM10), FTSE/JSE SA All Share Index (FTSEJSE), and FTSE/JSE Top 30 Responsible Investment Index (FTSERI). We employed Rényi transfer entropy estimates to measure the information flow between the stocks returns.To ensure the robustness of the findings, the study also utilizes the Dynamic Conditional Co-variance-Generalized autoregressive Conditional Heteroscedasticity (DCC-GARCH) method. The study covers the period from June 23, 2014, to April 22, 2022. The results showed positive information flow but a mixture of significant and insignificant transfer entropies. The decomposed findings give evidence to reject the null hypothesis of no information flow in the short-and medium-terms but not in the longterm.The exchange of seemingly insignificant information among stocks presents opportunities for fund managers to diversify their portfolios across various time frames.To expedite economic growth and lower equity costs, institutional leaders should collaborate with governments to establish economic and political foundations that integrate markets. Such integration would yield benefits for both markets, but the BRVM stands to gain more due to its less developed and illiquid nature.

The identification of city communities is essential for the regional planning and management of metropolitan areas. City communities could be identified from the perspective of mobile flows, and integrating the advantages of multisource flow data is essential to measure intercity connectivity. In this research, a multisource flow fusion method, which avoids the one-sidedness of a single flow, is proposed to combine the characteristics of population flow, traffic flow, and information flow. Moreover, the silhouette coefficient and hierarchical clustering algorithm are used to determine the number of city communities and the final clustering result. The results show that (1) although population flow, cargo flow, and information flow are positively correlated, there are also some differences among them; (2) the fusion flow between cities in China presents an obvious diamond structure, among which Beijing, Shanghai, Guangzhou, and Chengdu are the four points of the diamond, and Wuhan is located almost in the centre of the diamond structure; and (3) city communities are identified based on multisource flow and hierarchical clustering algorithm, which meet the principles that closely connected cities are in the same community and nonclosely connected cities are in different communities.

With the increase of new energy penetration, the power balance and frequency stability problems brought by its randomness, intermittency and volatility has become increasingly serious. It is difficult to cope with these problems only by traditional centralized power plants. Flexible resources in the power systems should also bear a part of the responsibility for the balance of power and energy. Virtual power plant (VPP) can aggregate a large number of distributed flexible resources with different characteristics, participate in the electricity markets as a whole and accept the dispatch of the grid, and provide important support for the real-time power balance of the power systems. The development of VPPs should be based on a large number of flexible resources, advanced communication and dispatching/control technologies, and efficient business models and good market policies. The operation of VPPs can be attributed to the energy flow of the energy network, the information interaction of the information network and the value transfer of the value network. Therefore, based on the three-layer network architeciture of “energy-information-value”, the operation modes and control schemes of different types of VPPs were analyzed, and the idea of “three flow separation-convergence” for VPP architecture design was proposed. The findings provide useful guidance for the design, construction and operation of VPPs.

Citrus fruit commodities cultivated by farmers have supply chain management problems at distribution agents and citrus fruit enthusiasts on a local scale. This study aimed to analyze the supply chain management (SCM) channel, the smoothness of SCM, and the efficiency of SCM in citrus fruits. The research was conducted in Poncokusumo Village, Malang Regency. Collecting data using interviews with respondents farmers and traders of oranges. Measurement of data using a Likert scale ranging from 1 to 5. The descriptive analysis method is used to describe the SCM phenomenon of citrus fruits, including information flow, commodity flow, and financial flow. The results showed 2 channel citrus fruits ended up in small traders. The smoothness analysis of SCM shows that the financial flow from small traders to middlemen is less smooth than the flow of commodities from small traders to large traders. The efficiency index and farmer share results indicate that channel two is efficient. It is recommended that middlemen apply a pre-order system so that packaging costs and transportation costs do not increase and wholesalers should communicate intensely with farmers so that sales information is conveyed directly to wholesalers.

Adequate and desirable connections between suppliers and customers necessitate an appropriate flow of information. Therefore, a promising and proper data collaboration in the supply chain is of tremendous significance. Thus, the study’s main objective is to provide multiple objective programming models under uncertain conditions to assess the performance of suppliers. To meet that aim, a case study for the reliability assessment of the presented model is carried out. That section is associated with supply chain visibility (SCV). Likewise, the likelihood of unpredicted and undesirable incidents involving supply chain risk (SCR) is taken into consideration. The intimate relation between visibility and risk of the supply chain is deemed efficient for the performance of the supply chain. Incoherence in maximization and minimization of SCR and SCV and other factors, including costs, capacity, or demand, necessitates multiple objective programming models to assess suppliers’ performance to accomplish the before-mentioned aims. The study’s results indicate the high reliability of the proposed model. Besides, the numeral results reveal that decision-makers in selecting suppliers mainly decrease SCR and then attempt to enhance SCV. In conclusion, the provided model in the study can be a desirable model for analyzing and estimating supplier performance with SCR and SCV simultaneously.

The directional organization of multiple nociceptive regions, particularly within obscure operculoinsular areas, underlying multidimensional pain processing remains elusive. This study aims to establish the fundamental organization between somatosensory and insular cortices in routing nociceptive information. By employing an integrated multimodal approach of high-field fMRI, intracranial electrophysiology, and transsynaptic viral tracing in rats, we observed a hierarchically organized connection of S1/S2 → posterior insula → anterior insula in routing nociceptive information. The directional nociceptive pathway determined by early fMRI responses was consistent with that examined by early evoked LFP, intrinsic effective connectivity, and anatomical projection, suggesting fMRI could provide a valuable facility to discern directional neural circuits in animals and humans non-invasively. Moreover, our knowledge of the nociceptive hierarchical organization of somatosensory and insular cortices and the interface role of the posterior insula may have implications for the development of targeted pain therapies.

The digital revolution has transformed the exchange of information between people, blurring the traditional roles of sources and recipients as active and passive entities. To study this, we build on a publicly available database of quotes, organized as units of information flowing through media and blogs with minimal distortion. Building on this, we offer an innovative interpretation of the observed temporal patterns through a minimal model with two ingredients: a two-way feedback between sources and recipients, and a delay in the media’s response to activity on blogs. Our model successfully fits the variety of observed patterns, revealing different attention decays in media and blogs, with rebounds of information typically occurring between 1 and 4 days after the initial dissemination. More important perhaps, the model uncovers a functional relationship between the rate of information flow from media to blogs and the decay of public attention, suggesting a simplification in the mechanisms of information exchange in digital media. Although further research is required to generalize these findings fully, our results demonstrate that even a bare-bones model can capture essential mechanisms of information dynamics in the digital environment.

This study examined Greece's Agricultural Knowledge and Innovation System (AKIS) and assessed the flow of information and linkages among eight stakeholder groups: policy, education, research, consulting, agricultural cooperatives, credit, private enterprises, and farmers. Data were collected using an online survey tool from 61 experts/representatives following an initial phone communication. The Graph Theoretical Technique was utilized to achieve the survey's objectives. The results revealed dominant and subordinated actors in the system and identified a critical pathway for information flow within AKIS. Policymakers can leverage these findings to strengthen linkages, address information gaps, and promote innovation and equitable development in the agricultural sector.

Copyright of Revista Ibero-Americana de Ciência da Informação is the property of Revista Ibero-Americana de Ciencia da Informacao 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 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 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]

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]

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]

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.