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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.)

Abstract 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.

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

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

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

Information 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]

The inspection of roads for defects, as basic transportation infrastructure, is critical to maintain the efficient and safe operation of the transportation system. Visual perception-based pavement defect detection methods can efficiently and accurately identify pavement issues and provide data support for intelligent preventive maintenance. This paper summarizes the development of pavement defect detection technology, from feature extraction to automatic feature extraction. Deep learning-based pavement defect detection methods are analyzed and compared in three aspects: classification, object detection, and segmentation. By integrating recent research methods, the paper further reviews innovative approaches to pavement defect detection modeling in four areas: information flow models, Transformer models, attention mechanisms, and the application of 3D technology. Additionally, various defect types, pavement defect datasets, and defect detection systems are introduced. Finally, the paper provides an outlook on future trends and summarizes the key findings. [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]

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

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

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]

Transparency, a value that must be built through organizational actions, is a challenge commonly faced by public administration. This challenge is addressed through specific actions embedded in the control environment, which become part of the routine and behavior of every public official to establish a constant flow of information within and outside an entity. To gather information supporting the above, a tool was designed (semi-structured interviews) and applied to eight public officials. The results revealed actions within their control environment dynamics, highlighting the interaction that gives organizational meaning to endogenize transparency. This exploration of actions not only opens up new possibilities for making a profound change in organizational culture, starting from managerial levels, but also instills hope and optimism for a more transparent public administration. It underscores the importance of addressing individual and organizational aspects, paving the way for a more transparent future. As a result, this research not only contributes to the development of investigations focused on analyzing the control environment in public organizations but also provides a beacon of hope for public administration professionals and policymakers. [ABSTRACT FROM AUTHOR]

The purpose of this study was to understand students' task value toward a class. Task value is the utility students see in the course material. As expected, good instructional practices (i.e. clarity, nonverbal immediate behaviors) were positive contributors of task value. However, the substantive impact on task value came from information flow, a variable borrowed from the organizational culture literature. The supported model showed instructor clarity and nonverbal immediate behaviors indirectly, positively influenced task value through the mediation of perceived immediacy and information flow. This indicates that a large proportion of whether students have task value in a course hinge upon whether students understand the rationale behind instructors' pedagogical choices. [ABSTRACT FROM AUTHOR]

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]