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An explicit analytic solution is given for the Langevin equation applied to the Gaussian Network Model of a protein subjected to both a random and a deterministic periodic force. Synchronous and asynchronous components of time correlation functions are derived and an expression for phase differences in the time correlations of residue pairs is obtained. The synchronous component enables the determination of dynamic communities within the protein structure. The asynchronous component reveals causality, where the time correlation function between residues i and j differs depending on whether i is observed before j or vice versa, resulting in directional information flow. Driver and driven residues in the allosteric process of cyclophilin A and human NAD‐dependent isocitrate dehydrogenase are determined by a perturbation‐scanning technique. Factors affecting phase differences between fluctuations of residues, such as network topology, connectivity, and residue centrality, are identified. Within the constraints of the isotropic Gaussian Network Model, our results show that asynchronicity increases with viscosity and distance between residues, decreases with increasing connectivity, and decreases with increasing levels of eigenvector centrality. [ABSTRACT FROM AUTHOR]

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

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

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]

Lakes are important in supporting ecosystem services and livelihoods. However, their food webs and ecological functioning are continuously threatened by anthropogenic influences. Food web models have been widely used in studying trophodynamics, fisheries impacts, and ecological functioning of temperate lakes, but less often in Afrotropical lake systems. We used Ecopath mass‐balanced trophic models annually in 1999, 2010, and 2020 to assess trends in ecosystem function, and the impact of fisheries on the Lake Baringo Ecosystem, a shallow freshwater lake in Kenya. Pre‐balance (PREBAL) and Pedigree analyses supplemented Ecopath models. Model input data were from field sampling, published and gray literature. Food web trophic models indicated a bottom‐up grazer and detrital food chains in all 3 years. Odum's ecosystem development indicators (total productivity to total biomass and total respiration ratios; TPP/TB and TPP/TR) showed that the lake was in a low to intermediate developmental stage, with room for bio‐manipulation, and a highly reduced mean transfer efficiency (TE) (6.4%–0.49%) indicated low trophic transfer of internal production. System omnivory (SOI) and connectance (CI) indices that varied among years indicated temporal variation in food web complexity. Indices of system resilience (overhead and ascendency) indicated an increasing potential for the lake to recover from perturbations. The mean trophic level of the catch (MTLc) increased from 1999 to 2010 and decreased in 2020, by fishing down the food chain as fishing pressure increased. Oreochromis niloticus, an endemic cichlid, was the keystone species (KSi >0) controlling community structure, while the lungfish Protopterus aethiopicus, the top predator in the lake, was not a keystone species (KSi <0). We recommend an integrated approach to lake management that incorporates watershed regulations, regulates fishing effort on the keystone species (O. niloticus), and monitors water quality for sustainable management of the Lake Baringo ecosystem. [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.

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 implementation of Nearly Zero-Energy Buildings (NZEB) renovation packages in Europe needs to be accelerated to meet the current decarbonization goals. To achieve this level of performance, building renovation strategies should shift towards solutions that incorporate a multitude of passive and active components, increasing the complexity and costs of the execution. Moreover, it requires the involvement of different stakeholders of the building supply-chain, resulting in additional difficulties in communication and coordination processes. To address this challenge, the present study aims at mapping the renovation process in digital platforms and addressing the respective bottlenecks. In terms of renovation process, several digital platforms were analysed to identify the type of information that the stakeholders require during the different renovation phases. By structuring the information along the renovation process phases, the different stakeholders can identify when the information can be provided and how the different type of information links to each other. [ABSTRACT FROM AUTHOR]

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]

• Our multimodal quantitative method unveils directional dynamics of epileptic network during interictal to ictal transition. • Epileptogenic zone exhibits maximum outflow during preictal, and at seizure onset. • Regions that are hypoperfused during seizures exhibit maximum outflow during ictal evolution and can serve as a control network. Ictal Single Photon Emission Computed Tomography (SPECT) and stereo-electroencephalography (SEEG) are diagnostic techniques used for the management of patients with drug-resistant focal epilepsies. While hyperperfusion patterns in ictal SPECT studies reveal seizure onset and propagation pathways, the role of ictal hypoperfusion remains poorly understood. The goal of this study was to systematically characterize the spatio-temporal information flow dynamics between differently perfused brain regions using stereo-EEG recordings. We identified seizure-free patients after resective epilepsy surgery who had prior ictal SPECT and SEEG investigations. We estimated directional connectivity between the epileptogenic-zone (EZ), non-resected areas of hyperperfusion, hypoperfusion, and baseline perfusion during the interictal, preictal, ictal, and postictal periods. Compared to the background, we noted significant information flow (1) during the preictal period from the EZ to the baseline and hyperperfused regions, (2) during the ictal onset from the EZ to all three regions, and (3) during the period of seizure evolution from the area of hypoperfusion to all three regions. Hypoperfused brain regions were found to indirectly interact with the EZ during the ictal period. Our unique study, combining intracranial electrophysiology and perfusion imaging, presents compelling evidence of dynamic changes in directional connectivity between brain regions during the transition from interictal to ictal states. [ABSTRACT FROM AUTHOR]

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

Maintaining diversity in drug development in research into Alzheimer's disease (AD) is necessary to avoid over-reliance on targeting AD neuropathology. Treatments that reduce or prevent the generation of oxidative stress, frequently cited for its causal role in the aging process and AD, could be useful in at-risk populations or diagnosed AD patients. However, in this review, it is argued that clinical research into antioxidants in AD could provide more useful feedback as to the therapeutic value of the oxidative stress theory of AD. Improving comparability between randomized controlled trials (RCTs) is vital from a waste-reduction and priority-setting point of view for AD clinical research. For as well as attempting to improve meaningful outcomes for patients, RCTs of antioxidants in AD should strive to maximize the extraction of clinically useful information and actionable feedback from trial outcomes. Solutions to maximize information flow from RCTs of antioxidants in AD are offered here in the form of checklist questions to improve ongoing and future trials centered around the following dimensions: adhesion to reporting guidelines like CONSORT, biomarker enrichment, simple tests of treatment, and innovative trial design. [ABSTRACT FROM AUTHOR]

For the management of information flow, the use of the functional modelling methodology is proposed, as part of the graph theory for describing the functioning of systems, such as public authorities as organizational-management structures. The methodology is based on concepts such as functional block and interface arc (flow). In organizational-management structures, one of the functional blocks is the manager, and the others are managed. Each of the blocks is a source of information and generates information flows, transferring information to a managed functional block or several ones. For the implementation and management of information exchange by the public authorities in crisis management, processing and protection of data flow, when working in a group environment, it is proposed to build an adequate communication and information system using the methodology of functional modelling. [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 describes the possibilities of communication through information flows in the tasks of recognition and prediction using the example of assessing the change in the state of defects in materials.In the context of communication between several elements of the system, an important part is the formation of effective information flows and optimal messages. In this context, the grouping of information according to the principle of informativeness is proposed, using the example of the problem of recognition and further forecasting.Information transfer with the Internet of Things involves exchange over wireless networks and network protocols.In the paper, it is proposed to use the segmented area of the recognized object and use it to check the forecast. The paper proposes to use the segmented region of the recognized object in the image and use it to check the prediction. In addition, sensor data can be used to test defect classification. This design of information flows can improve performance.

Copyright of Urban History Review / Revue d'Histoire Urbaine is the property of University of Toronto Press 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 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]

Background: Working memory deficits in Alzheimer's disease (AD) are linked to impairments in the retrieval of stored memory information. However, research on the mechanism of impaired working memory retrieval in Alzheimer's disease is still lacking. Objective: The medial prefrontal cortex (mPFC) and mediodorsal thalamus (MD) are involved in memory retrieval. The purpose of this study is to investigate the functional interactions and information transmission between mPFC and MD in the AD model. Methods: We recorded local field potentials from mPFC and MD while the mice (APP/PS1 transgenic model and control) performed a T-maze spatial working memory task. The temporal dynamics of oscillatory activity and bidirectional information flow between mPFC and MD were assessed during the task phases. Results: We mainly found a significant decrease in theta flow from mPFC to MD in APP/PS1 mice during retrieval. Conclusions: Our results indicate an important role of the mPFC-MD input for retrieval and the disrupted information transfer from mPFC to MD may be the underlying mechanism of working memory deficits in APP/PS1 mice. [ABSTRACT FROM AUTHOR]

Aim: To describe the contributing factors and types of reported medication incidents in home care related to the flow of information in different phases of the medication process, as reported by multi‐professional healthcare groups. Design: This descriptive, qualitative study used retrospective data. Methods: An incident‐reporting database was used to collect 14,289 incident reports from 2017 to 2019 in a city in Finland. We used this data to select medication incidents (n = 1027) related to the flow of information in home care and between home care and hospitals. Data were divided into five groups based on the medication phase: (1) prescribing, (2) dispensing, (3) administration, (4) documentation and (5) self‐administration. In addition, the types of medication‐related incidents were described. The data were examined using abductive content analysis. The EQUATOR SRQR checklist was used in this report. Results: Four main categories were identified from the data: (1) issues related to information management, (2) cooperation issues between different actors, (3) work environment and lack of resources and (4) factors related to healthcare workers. Cooperation issues contributed to medication‐related incidents during each phase. Incomplete communication was a contributing factor to medication incidents. This occurred between home care, remote care, hospital, the client and the client's relatives. Specifically, a lack of information‐sharing occurred in repatriation situations, where care transitioned between different healthcare professionals. Conclusion: Healthcare professionals, organisations, clients and their relatives should focus on the efficient and safe acquisition of medications. Specifically, the use of electronic communication systems, together with oral reports and checklists for discharge situations, and timely cooperation with pharmacists should be developed to manage information flows. Relevance to Clinical Practice: These findings demonstrate that healthcare professionals require uniform models and strategies to accurately and safely prescribe, dispense and administer medications in home care settings. No patient or public contributions. [ABSTRACT FROM AUTHOR]

This study investigates how financial sanctions packages targeting Russia influenced traditional information flow dynamics with other international financial markets and products. While providing empirical evidence regarding the use of payment systems and finance as weapons of war, it is crucial to understand if the market's response to international sanctions diminished as expectations shifted over time. Results, supported by robustness testing procedures, indicate important dynamic information flows relating to specific sanctions after the onset of the Russia-Ukraine war. In particular, those sanctions relating to the exclusion of Russia from the SWIFT payment system and those targeting banks and private wealth resulted in significant contagion effects sourced from all Russian markets examined. Such influence, however, is found to moderate and dilute as investors reconstruct their expectations and valuations. While targeted sanctions appear to impose intended market isolation, it is also associated with significant contagion effects. Although such secondary effects dissipate, they should be seen as important when implementing further targeted sanction packages. [ABSTRACT FROM AUTHOR]

This research used the newly developed tool of ICEEMDAN (improved complete ensemble empirical mode decomposition with adaptive noise) to decompose the Baidu index among 336 prefectural-level administrative units in China. Then this paper constructed directed networks to compare with undirected networks by analyzing the evolutionary characteristics of urban hierarchy structure, node asymmetry, spatial organization, and structural complexity in information networks between 2014 and 2019. The results show that: ① A large amount of noise interference exits in intercity Baidu search data, and noise reduction processing is necessary. After noise decomposition, the long-term trend data can better reveal the evolution characteristics of nodes. ② Considering the significant asymmetry of the information network, directed network analysis is a more effective method than undirected network analysis to identify the difference between node characteristics and their trends. ③ Node degree centrality shows a disproportional growth path, demonstrating growth dominance shifting from in-degree to out-degree at different stages. ④ Dominant flow analysis sheds light on two spatial organization patterns in the information network. One pattern is on the national scale, with disassortative network and scale-free network features. The other is on urban agglomerations and provincial scales and features assortative network and small-world network properties. The information network organization is a complex process, especially with the differences in long distance and geographic proximity interactions as two patterns indicate. This illustrates the characteristics of the spatial organization of two models, the Center Place Model and the Network Model, as well as the interaction between cyberspace and physical space. [ABSTRACT FROM AUTHOR]

The features of various supply chain systems use at the enterprise are generalized in the article. The evolution of supply chain systems is studied starting from the material requirements planning (MRP) system, which became the basis for the construction of such systems, to the modern ERP II system with a CSRP unit, as well as DRP-I, DRP-II systems, which emphasize the distribution requirements planning taking into account the requirements and requests of consumers. The main advantages and a number of disadvantages in the context of using MRP, ERP and DRP supply chain systems are identified. The main advantages of the systems analyzed are the integration of data from different sources into a single digital environment, such as procurement plans, reports and other information bases; construction of a flexible system of planning and management of material flows, which provides an opportunity to quickly respond to the needs of supply chains in real time; automation of the main business processes related to the procurement system, production management and logistics, which contributes to the reduction of risks related to the human factor, which can negatively affect the company’s activities. The role of an ERP system is determined, enabling companies to gain a deeper understanding of their supply chain ecosystem. At the same time, the key tasks of the ERP system are the active management of various activities that take place throughout the product life cycle to maximize consumer value, the level of satisfaction of consumer expectations and achieving competitive advantage. The main stages of ERP implementation are studied and summarized. The necessity of resource planning in the ERP system, synchronized with customers, coverage of relations with customers by the formation of the CSRP concept, which involves the integration of the customer into the enterprise management system, is determined. The DRP-I and DRPII systems are characterized, which are implemented for the purpose of planning, accounting and management of transport operations related to the movement of material and technical resources and finished products. [ABSTRACT FROM AUTHOR]

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.)

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.

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.

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.

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 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.

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.