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

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]

Reactive inhibitory control is crucial for survival. Traditionally, this control in mammals was attributed solely to the hyperdirect pathway, with cortical control signals flowing unidirectionally from the subthalamic nucleus (STN) to basal ganglia output regions. Yet recent findings have put this model into question, suggesting that the STN is assisted in stopping actions through ascending control signals to the striatum mediated by the external globus pallidus (GPe). Here, we investigate this suggestion by harnessing a biologically constrained spiking model of the cortico-basal gangliathalamic (CBGT) circuit that includes pallidostriatal pathways originating from arkypallidal neurons. Through a series of experiments probing the interaction between three critical inhibitory nodes (the STN, arkypallidal cells, and indirect pathway spiny projection neurons), we find that the GPe acts as a critical mediator of both ascending and descending inhibitory signals in the CBGT circuit. In particular, pallidostriatal pathways regulate this process by weakening the direct pathway dominance of the evidence accumulation process driving decisions, which increases the relative suppressive influence of the indirect pathway on basal ganglia output. These findings delineate how pallidostriatal pathways can facilitate action cancellation by managing the bidirectional flow of information within CBGT circuits. [ABSTRACT FROM AUTHOR]

Despite being able to predict accurate user-item interactions, recommender systems also lead to homogeneous information flow, resulting in negative online experiences. However, users' coping processes for this stressful event remain basically unclear. Drawing on coping theory, this study examines how users appraise two stressors (information narrowing and information redundancy) in homogenous information flow, especially in different user experiences, and thus adopt algorithmic resistance and avoidance coping strategies. Data (N = 407) were collected based on a survey. The empirical results indicate that information narrowing and information redundancy are significantly and negatively related to challenge appraisal, and exert significant and positive effects on threat appraisal. The moderating effect denotes that user experience strengthens the relationship between information narrowing and information redundancy to threat appraisal. Threat appraisal further triggers algorithmic resistance and avoidance of users. The paper provides several implications for theory and research by revealing the mechanism through which recommender systems users cope with homogeneous information flow. Our findings also provide new insights for designers of recommender systems platforms. [ABSTRACT FROM AUTHOR]

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]

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

Copyright of 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 flow is one of the three main flows of supply chains. It is an abstract concept that can be challenging for students to grasp in its entirety. This article describes a role‐playing game for teaching the topic of information flow in an undergraduate supply chain management course. The game allows students to simulate receiving and fulfilling customer orders by playing five roles within a manufacturing company. Students use TagScan, an augmented reality barcoding and logistics system launched by a technology company in western Canada, to track information throughout the game. Pre‐ and postsurvey results demonstrate the effectiveness of the proposed game in helping students visualize abstract course concepts and understand the types of information being tracked, the available information transmission technology, and the dynamics of information flow in a supply chain. Students were actively engaged in this in‐class activity and responded positively to the learning‐by‐gaming experience. [ABSTRACT FROM AUTHOR]

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]

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]

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]

Analogies are used to make abstract topics meaningful and more easily comprehensible to learners. Incorporating simple analogies into STEM classrooms is a fairly common practice, but the analogies are typically generated and explained by the instructor for the learners. We hypothesize that challenging learners to create complex, extended analogies themselves can promote integration of content knowledge and development of critical thinking skills, which are essential for deep learning, but are challenging to teach. In this qualitative study, college biology students (n = 30) were asked to construct a complex analogy about the flow of genetic information using a familiar item. One week later, participants constructed a second analogy about the same topic, but this time using a more challenging item. Twenty participants worked on the challenging analogy in pairs, while the other 10 worked alone. Analysis of the 50 interviews resulted in a novel‐scoring scheme, which measured both content knowledge (understanding of biology terms) and critical thinking (alignment of relationships between elements of the analogy). Most participants improved slightly due to practice, but they improved dramatically when working with a partner. The biggest gains were seen in critical thinking, not content knowledge. Having students construct complex, sophisticated analogies in pairs is a high‐impact practice that can help students develop their critical thinking skills, which are crucial in academic and professional settings. The discussion between partners likely requires students to justify their explanations and critique their partner's explanations, which are characteristics of critical thinking. [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]

Digital well-being is related to people's abilities to process the information flow in such a way as to maintain a good level of psycho-physic functioning, to react to technostress adequately, and to filter the information flow to recognize cyberepta. The article reviews various studies that indicate good digital practices are related to learning and developing digital skills, ethical use of digital devices, and cyberthreat recognition. [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]

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 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]

Purpose: This paper aims to examine the interaction between gatekeeping and trust in a public sector organization, where employees at lower hierarchical levels are expected to autonomously translate and transform directives into public services. This requires them to have access to operational steering information, i.e. information about directives and how to interpret and apply them. This study focuses on how gatekeeping structures regulate flows of operational steering information and how the gatekeeping structures affect the development of trust. Design/methodology/approach: The research design is qualitative. The data material consisted of semi-structured interviews with 26 employees in home care and schools and of eight complementary nonparticipant observations. Thematic analysis revealed the presence of static and dynamic gatekeeping structures, which are characterized by fixed and variable arrangements of information sources and channels, respectively. Findings: In static gatekeeping structures, managers or domain experts typically act as gatekeepers, and employees also perform gatekeeping activities collectively. Gatekeeping structures allow employees to switch between acting as gatekeepers and being gated, depending on the situation. The results show that gatekeeping structures for intermediation of operational steering information may support or impede employees' work, thus affecting their trust in their peers and their work organization. Research limitations/implications: Although the present study included both interviews and observations, these primarily occurred within scheduled and prearranged activities rather than capturing the nuances of the typical daily work of teachers and home care employees. As a result, certain perspectives may have been unintentionally omitted. Practical implications: The participants were recruited through the City of Oslo contact people, which may have impacted their status or perception in some way. Moreover, the study was conducted in the City of Oslo, a specific organization with its own unique set of values, norms and processes. The trust-based management in the City of Oslo is likely not representative of all public sector organizations. Originality/value: This study contributes conceptually by introducing gatekeeping structures and operational steering information and empirically by providing evidence of their relationship to trust development in public service delivery. Thus, it contributes to the research fields of information management and public administration. [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]

(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]

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]

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

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 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]

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Copyright of World Regional Studies is the property of World Regional Studies 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.)

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. [ABSTRACT FROM AUTHOR]

Purpose: With the rapid-growing house market in the past decade, the purpose of this paper is to study the important issue of house price information flows among 12 major cities in China, including Shanghai, Beijing, Xiamen, Shenzhen, Guangzhou, Hangzhou, Ningbo, Nanjing, Zhuhai, Fuzhou, Suzhou and Dongguan, during the period of June 2010 to May 2019. Design/methodology/approach: The authors approach this issue in both time and frequency domains, latter of which is facilitated through wavelet analysis and by exploring both linear and nonlinear causality under the vector autoregressive framework. Findings: The main findings are threefold. First, in the long run of the time domain and for timescales beyond 16 months of the frequency domain, house prices of all cities significantly affect each other. For timescales up to 16 months, linear causality is weaker and is most often identified for the scale of four to eight months. Second, while nonlinear causality is seldom determined in the time domain and is never found for timescales up to four months, it is identified for scales beyond four months and particularly for those beyond 32 months. Third, nonlinear causality found in the frequency domain is partly explained by the volatility spillover effect. Originality/value: Results here should be of use to policymakers in certain policy analysis. [ABSTRACT FROM AUTHOR]

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We study the positivity and causality axioms for Markov categories as properties of dilations and information flow and also develop variations thereof for arbitrary semicartesian monoidal categories. These help us show that being a positive Markov category is merely an additional property of a symmetric monoidal category (rather than extra structure). We also characterize the positivity of representable Markov categories and prove that causality implies positivity , but not conversely. Finally, we note that positivity fails for quasi-Borel spaces and interpret this failure as a privacy property of probabilistic name generation. [ABSTRACT FROM AUTHOR]

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

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

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

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

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. [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]

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