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Purpose This paper aims to conduct a qualitative assessment of synergies between information flows of a multifamily product platform used for industrialized housing and materials passports that can promote a circular economy in the construction industry. Design/methodology/approach Using a single case study method, the research assesses the availability and accessibility of materials passport-relevant information generated by a leading Swedish industrialized housing construction firm. Data is collected using semistructured interviews, document analysis and an extended research visit. Findings The research findings identify the functional layers of the product platform, map the information flow using a process diagram, assess the availability and accessibility of material passport relevant information by lifecycle stage and actor, and summarize the key points using a SWOT (strengths, weaknesses, opportunities and threats) analysis. Research limitations/implications The three main implications are: the technical and process platforms used in industrialized construction allow for generating standardized, digital and reusable information; the vertical integration of trades and long-term relationships with suppliers improve transparency and reduce fragmentation in information flows; and the design-build-operate business model strategy incentivizes actors to manage information flows in the use phase. Practical implications Industrialized construction firms can use this paper as an approach to understand and map their information flows to identify suitable approaches to generate and manage materials passports. Originality/value The specific characteristics of product platforms and industrialized construction provide a unique opportunity for circular information flow across the building lifecycle, which can support material passport adoption to a degree not often found in the traditional construction industry.

This paper focuses on strategic information flows between buyers and suppliers within logistics supply chain relationships and on subsequent relationship-specific performance outcomes. Our analysis of dyadic data collected from 91 buyer—supplier logistics relationships finds that buyer and supplier strategic information flows positively impact the relationship-specific performance of both sharing and receiving parties. Specifically, each party gains financially from improved management of assets, reduced costs of operations, and enhanced productivity. Moreover, each benefits operationally from improved planning, control, and flexibility of resources. Buyer dependence on the supplier increases buyer strategic information flows to the supplier. Additionally, buyer IT customization and both buyer and supplier trusting beliefs in the receiving party positively impact strategic information sharing with partners. This study suggests that partnerships for supply chain services engage in cooperative initiatives to generate relational rents and are an alternative to conventional \"arms length\" transactional exchanges. These partnerships need to be motivated to go beyond the sharing of order-related information (which must occur in transactional exchanges) and to share strategic information (which has the potential for both additional rent generation and risks of misappropriation).

Little is currently known about the factors that promote the propagation of information in online social networks following terrorist events. In this paper we took the case of the terrorist event in Woolwich, London in 2013 and built models to predict information flow size and survival using data derived from the popular social networking site Twitter. We define information flows as the propagation over time of information posted to Twitter via the action of retweeting. Following a comparison with different predictive methods, and due to the distribution exhibited by our dependent size measure, we used the zero-truncated negative binomial (ZTNB) regression method. To model survival , the Cox regression technique was used because it estimates proportional hazard rates for independent measures. Following a principal component analysis to reduce the dimensionality of the data, social, temporal and content factors of the tweet were used as predictors in both models. Given the likely emotive reaction caused by the event, we emphasize the influence of emotive content on propagation in the discussion section. From a sample of Twitter data collected following the event ( N  = 427,330) we report novel findings that identify that the sentiment expressed in the tweet is statistically significantly predictive of both size and survival of information flows of this nature. Furthermore, the number of offline press reports relating to the event published on the day the tweet was posted was a significant predictor of size, as was the tension expressed in a tweet in relation to survival. Furthermore, time lags between retweets and the co-occurrence of URLS and hashtags also emerged as significant.

Autism spectrum disorder (ASD) is a pervasive developmental disorder with severe cognitive impairment in social communication and interaction. Previous studies have reported that abnormal functional connectivity patterns within the default mode network (DMN) were associated with social dysfunction in ASD. However, how the altered causal connectivity pattern within the DMN affects the social functioning in ASD remains largely unclear. Here, we introduced the Liang information flow method, widely applied to climate science and quantum mechanics, to uncover the brain causal network patterns in ASD. Compared with the healthy controls (HC), we observed that the interactions among the dorsal medial prefrontal cortex (dMPFC), ventral medial prefrontal cortex (vMPFC), hippocampal formation, and temporo‐parietal junction showed more inter‐regional causal connectivity differences in ASD. For the topological property analysis, we also found the clustering coefficient of DMN and the In‐Out degree of anterior medial prefrontal cortex were significantly decreased in ASD. Furthermore, we found that the causal connectivity from dMPFC to vMPFC was correlated with the clinical symptoms of ASD. These altered causal connectivity patterns indicated that the DMN inter‐regions information processing was perturbed in ASD. In particular, we found that the dMPFC acts as a causal source in the DMN in HC, whereas it plays a causal target in ASD. Overall, our findings indicated that the Liang information flow method could serve as an important way to explore the DMN causal connectivity patterns, and it also can provide novel insights into the nueromechanisms underlying DMN dysfunction in ASD. Liang information flow method was used to construct the causal connectivity network. The dorsal medial prefrontal cortex (MPFC) and ventral MPFC are important causal sources in the default mode network (DMN) in ASD. The altered causal connectivity in the DMN is correlated with the clinical symptoms of ASD. The causal connectivity difference of DMN shows a hierarchical regulation model.

A prominent and robust finding in cognitive neuroscience is the strengthening of memories during nonrapid eye movement (NREM) sleep, with slow oscillations (SOs;<1Hz) playing a critical role in systems-level consolidation. However, NREM generally shows a breakdown in connectivity and reduction of synaptic plasticity with increasing depth: a brain state seemingly unfavorable to memory consolidation. Here, we present an approach to address this apparent paradox that leverages an event-related causality measure to estimate directional information flow during NREM in epochs with and without SOs. Our results confirm that NREM is generally a state of dampened neural communication but reveals that SOs provide two windows of enhanced large-scale communication before and after the SO trough. These peaks in communication are significantly higher when SOs are coupled with sleep spindles compared with uncoupled SOs. To probe the functional relevance of these SO-selective peaks of information flow, we tested the temporal and topographic conditions that predict overnight episodic memory improvement. Our results show that global, long-range communication during SOs promotes sleep-dependent systems consolidation of episodic memories. A significant correlation between peaks of information flow and memory improvement lends predictive validity to our measurements of effective connectivity. In other words, we were able to predict memory improvement based on independent electrophysiological observations during sleep. This work introduces a noninvasive approach to understanding information processing during sleep and provides a mechanism for how systems-level brain communication can occur during an otherwise low connectivity sleep state. In short, SOs are a gating mechanism for large-scale neural communication, a necessary substrate for systems consolidation and long-term memory formation.

Information flow analysis can effectively model the security behavior and security properties of hardware design. However, the existing gate level information flow analysis methods cannot deal with large-scale designs due to computing power and verification effectiveness, and the register transfer level (RTL) information flow analysis methods require formal languages to rewrite hardware designs. This paper proposes a RTL hardware design information flow modeling and security verification method. Based on the RTL functional model, this method develops an information flow tracking logical model to model security behavior and security properties of RTL hardware designs from the perspective of information flow. This method can be integrated into EDA flows and uses EDA testing and verification tools to capture security property violations and detect security vulnerabilities based on non-interference security policy. The results on experiments with Trust-Hub hardware Trojan benchmarks show that the proposed method can effectively detect hardware Trojans. 近年来, 已有大量研究证明信息流分析能够有效地对设计安全属性与安全行为进行建模。然而, 现有的门级抽象层次的信息流分析方法往往受制于算力和验证效力等因素难以应对大规模设计, 而RTL抽象层次的信息流分析方法需借助类型系统等形式化语言对硬件设计进行重新描述。因此, 提出了一种寄存器传输级硬件设计信息流建模与安全验证方法。该方法在寄存器传输级功能模型的基础上构建附加安全属性的信息流跟踪逻辑模型, 从信息流角度建模设计安全行为和安全属性, 并利用EDA测试验证工具, 以无干扰为策略捕捉违反安全策略的有害信息流, 检测硬件设计安全漏洞。以Trust-Hub硬件木马测试集为测试对象的实验结果表明: 所提方法能够有效检测设计内潜藏的硬件木马。

Natural language processing unfolds information overtime as spatially separated, multimodal, and interconnected neural processes. Existing noninvasive subtraction‐based neuroimaging techniques cannot simultaneously achieve the spatial and temporal resolutions required to visualize ongoing information flows across the whole brain. Here we have developed rapid phase‐encoded designs to fully exploit the temporal information latent in functional magnetic resonance imaging data, as well as overcoming scanner noise and head‐motion challenges during overt language tasks. We captured real‐time information flows as coherent hemodynamic waves traveling over the cortical surface during listening, reading aloud, reciting, and oral cross‐language interpreting tasks. We were able to observe the timing, location, direction, and surge of traveling waves in all language tasks, which were visualized as “brainstorms” on brain “weather” maps. The paths of hemodynamic traveling waves provide direct evidence for dual‐stream models of the visual and auditory systems as well as logistics models for crossmodal and cross‐language processing. Specifically, we have tracked down the step‐by‐step processing of written or spoken sentences first being received and processed by the visual or auditory streams, carried across language and domain‐general cognitive regions, and finally delivered as overt speeches monitored through the auditory cortex, which gives a complete picture of information flows across the brain during natural language functioning. Practitioner Points Phase‐encoded fMRI enables simultaneous imaging of high spatial and temporal resolution, capturing continuous spatiotemporal dynamics of the entire brain during real‐time overt natural language tasks. Spatiotemporal traveling wave patterns provide direct evidence for constructing comprehensive and explicit models of human information processing. This study unlocks the potential of applying rapid phase‐encoded fMRI to indirectly track the underlying neural information flows of sequential sensory, motor, and high‐order cognitive processes. Phase‐encoded fMRI captures the step‐by‐step spatiotemporal brain dynamics of cognitive processes. When a sentence is heard, language information is carried across the auditory cortex in multiple streams of traveling waves. This provides direct evidence for and supplement the dual‐stream model of speech processing.

Partial information decompositions (PIDs) aim to categorize how a set of source variables provides information about a target variable redundantly, uniquely, or synergetically. The original proposal for such an analysis used a lattice-based approach and gained significant attention. However, finding a suitable underlying decomposition measure is still an open research question at an arbitrary number of discrete random variables. This work proposes a solution with a non-negative PID that satisfies an inclusion–exclusion relation for any f-information measure. The decomposition is constructed from a pointwise perspective of the target variable to take advantage of the equivalence between the Blackwell and zonogon order in this setting. Zonogons are the Neyman–Pearson region for an indicator variable of each target state, and f-information is the expected value of quantifying its boundary. We prove that the proposed decomposition satisfies the desired axioms and guarantees non-negative partial information results. Moreover, we demonstrate how the obtained decomposition can be transformed between different decomposition lattices and that it directly provides a non-negative decomposition of Rényi-information at a transformed inclusion–exclusion relation. Finally, we highlight that the decomposition behaves differently depending on the information measure used and how it can be used for tracing partial information flows through Markov chains.

The foreign exchange (FX) market has evolved into a complex system where locally generated information percolates through the dealer network via high-frequency interactions. Information related to major events, such as economic announcements, spreads rapidly through this network, potentially inducing volatility, liquidity disruptions, and contagion effects across financial markets. Yet, research on the mechanics of information flows in the FX market is limited. In this paper, we introduce a novel approach employing conditional transfer entropy to construct networks of information flows. Leveraging a unique, high-resolution dataset of bid and ask prices, we investigate the impact of an announcement by the European Central Bank on the information transfer within the market. During the announcement, we identify key dealers as information sources, conduits, and sinks, and, through comparison to a baseline, uncover shifts in the network topology.

This paper introduces a functional framework for modeling cyclical and feedback-driven information flow using a generalized family of derangetropy operators. In contrast to scalar entropy measures such as Shannon entropy, these operators act directly on probability densities, providing a topographical representation of information across the support of the distribution. The proposed framework captures periodic and self-referential aspects of information evolution through functional transformations governed by nonlinear differential equations. When applied recursively, these operators induce a spectral diffusion process governed by the heat equation, with convergence toward a Gaussian characteristic function. This convergence result establishes an analytical foundation for describing the long-term dynamics of information under cyclic modulation. The framework thus offers new tools for analyzing the temporal evolution of information in systems characterized by periodic structure, stochastic feedback, and delayed interaction, with potential applications in artificial neural networks, communication theory, and non-equilibrium statistical mechanics.