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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]

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

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

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

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

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]

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

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

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

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

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.

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

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

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

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

Introduction: by doing so, Twitter’s trending algorithm sets the benchmark for what online discussion and information flow look like. It must be clearly understood by the researchers and the users as to how it developed and impacted. Objective: this paper discusses the Twitter trending algorithm discussion until 2023, highlighting its aspects and ethical considerations. Method: to demonstrate trend identification, we adopted a cross-sectional approach that involved data mining of trends defined by the Twitter platform from January 2020 to October 2023, applying machine learning techniques. In total, 1,984,544 unique trends were identified in the two cities over the 1584 days of Twitter API research. Results: this research identified that there are many changes in the current trending algorithm regarding Twitter, and current real-time content and users’ participation are the major concerns. The assessed model, known as TrendDetector, predicts the trend of commercials to be 80 %, while the non-commercial trend is assessed to be 60 %. Trend selection was guided by the traffic of tweets, the number of users, and the extent of new content. Conclusions: user-generated activities, content, and spread, as well as the structure and design of the platform, are thus an intricate mix in the case of the trending algorithm on Twitter. It enhances the timely acquisition of information while being associated with preconditions of bias and manipulation. Future research must look at aspects such as the algorithm’s transparency and the ethicality of the trends it selects. [ABSTRACT FROM AUTHOR]

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

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

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

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

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.)

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]

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

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