Information Flow Research Articles

Optimizing agricultural downstream supply chain: addressing information asymmetry and losses

PurposeThis study aims to understand the significant issues in the downstream supply chain of agricultural commodities and find out the improved strategy.Design/methodology/approachA value chain analysis on a downstream supply chain is performed to understand all ground-level issues related to information asymmetry and material losses. The study maps processes, actors, activities, product flow, information flow, material volume flow, technology adoption and value share. The study is performed by doing a qualitative survey using a semi-structured questionnaire and face-to-face interviews with 120 farmers, six aggregators, six traders in APMC mandis, six processors, six distributors and six retailers. A case study is performed on the mustard supply chain in Rajasthan to relate the results more comprehensively. After identifying the prominent issues, a cause-and-effect analysis is done to generate suggestions for improvement in the paper.FindingsThe study reveals that the downstream supply chain has 8–12% losses of agricultural produce, and 5–7% of that is at the farmer’s end as post-harvesting losses. Farmers cannot access all available options for marketing their produce because of poor information exchange and poor reach. It suggests farmer empowerment for the optimum benefit of the entire supply chain.Research limitations/implicationsThis is an exploratory study conducted by field visits and lacks statistical evidence for some findings. The dataset can be more extensive, diversified, and analyzed for various commodities.Practical implicationsThe study’s outcome will guide the stakeholders in finding more optimum options in the downstream agriculture supply chain. Research methodology can be used as a template for studying the supply chain of any agricultural commodity in different countries.Originality/valueThe study reveals the prominent issues, causes, effects and solutions throughout the agriculture downstream supply chain. The study is a bundle of foremost observations altogether. The study has been conducted in-depth in the field with actual scenarios that unlayered the hidden issues at the root. This study addresses a relatively underexplored area and provides actionable recommendations, which significantly contribute to the existing literature on the agriculture supply chain.

ІНФОРМАЦІЙНИЙ ШУМ ТА МАРКЕТИНГОВІ ІНСТРУМЕНТИ ДЛЯ ЙОГО ПОДОЛАННЯ

Introduction. In the modern conditions of the formation of the digital economy and the intensive growth of the volume of information to be processed by analysts, fundamentally new requirements are being formed for consumers and businesses. At the same time, data processing tools do not always provide adequate analytical information regarding changes occurring both in the global and local markets. The reason for this is information noise, which is an excessive flow of data that complicates the perception of important messages and affects the effectiveness of marketing strategies. All this requires the formation of a systematic approach to managing information flows and optimizing communications between the enterprise and the target audience. The purpose of the article. The purpose of the study is to analyze information noise as a barrier to the effective perception of marketing communications, as well as to identify tools and strategies that allow minimizing its impact on consumers and increasing the effectiveness of marketing activities. Methods. The research is based on the use of an interdisciplinary approach, which includes content analysis, abstract modeling methods for studying consumer perception of information, as well as empirical methods for studying the effectiveness of modern marketing technologies, based on the analysis of approaches to managing specialized marketing tools for interacting with consumers. Results. The article considers the conceptual aspects of information noise and its functional impact on consumer behavior. The effectiveness of using various marketing tools based on the automation of business processes, the use of communication platforms and data for analyzing consumer behavior is analyzed, which results in increasing the efficiency of communications and reducing noise effects. Key strategies for combating information noise are identified, which contribute to strengthening the communication interaction of the enterprise in the context of dynamic changes in market conditions. The need for implementing innovative approaches to developing advertising campaigns that take into account the specifics of modern media channels and consumer behavioral models is proven. Conclusions. It has been proven that minimizing information noise is an important factor in ensuring the long-term effectiveness of a company's marketing communications. It has been determined that the use of modern data analysis tools, process automation, and an individual approach to the consumer allows a company to increase the level of trust and loyalty from customers, which results in an increase in sales and profitability of the business entity.

Examining Market Quality on the Egyptian Exchange (EGX): An Intraday Liquidity Analysis

This study examines the intraday dynamics of liquidity and trading activity on the Egyptian Exchange (EGX) to assess its market quality. Using reconstructed five-minute limit order book data, this study measures liquidity dimensions and explores anomalies through interval-of-day and day-of-week models. Key findings reveal an inverted J-shaped pattern in spreads due to information asymmetry, a U-shaped pattern in total depth, and a J-shaped market depth pattern. Additionally, significant day-of-week effects are observed, with Sundays showing the lowest liquidity and Thursdays the highest trading activity. These patterns highlight the impact of the EGX’s unique microstructure, including tick sizes and a preference for limit orders. This study underscores the influence of market structure on liquidity, trading efficiency, and cost, emphasizing the need for tailored regulatory and trading strategies. It provides valuable insights for investors optimizing trading strategies and policymakers seeking to enhance market integrity. Concluding, this research offers a foundation for understanding intraday liquidity patterns in emerging markets like the EGX and proposes future exploration of how information flows and trading mechanisms affect price discovery and market efficiency.

Finding Information Leaks with Information Flow Fuzzing - RCR Report

This is the Replicated Computational Results (RCR) Report for our ACM TOSEM paper, ”Finding Information Leaks with Information Flow Fuzzing,” in which we propose information flow fuzzing . This approach directs fuzzers toward detecting information leaks . We introduce a novel leak oracle and employ information flow as guidance for the fuzzer to identify information leaks effectively. As part of this RCR, we provide a replication package that enables the complete replication of all our results and simplifies the reuse of our FLOWFUZZ fuzzer.

A novel multi-user collaborative cognitive radio spectrum sensing model: Based on a CNN-LSTM model

Cognitive Radio (CR) technology enables wireless devices to learn about their surrounding spectrum environment through sensing capabilities, thereby facilitating efficient spectrum utilization without interfering with the normal operation of licensed users. This study aims to enhance spectrum sensing in multi-user cooperative cognitive radio systems by leveraging a hybrid model that combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. A novel multi-user cooperative spectrum sensing model is developed, utilizing CNN’s local feature extraction capability and LSTM’s advantage in handling sequential data to optimize sensing accuracy and efficiency. Furthermore, a multi-head self-attention mechanism is incorporated to improve information flow, enhancing the model’s adaptability and robustness in dynamic and complex environments. Simulation experiments were conducted to quantitatively evaluate the performance of the proposed model. The results demonstrate that the CNN-LSTM model achieves low sensing error rates across various numbers of secondary users (16, 24, 32, 40, 48), with a particularly low sensing error of 9.9658% under the 32-user configuration. Additionally, when comparing the sensing errors of different deep learning models, the proposed model consistently outperformed others, showing a 12% lower sensing error under low-power conditions (100 mW). This study successfully develops a CNN-LSTM-based cooperative spectrum sensing model for multi-user cognitive radio systems, significantly improving sensing accuracy and efficiency. By integrating CNN and LSTM technologies, the model not only enhances sensing performance but also improves the handling of long-term dependencies in time-series data, offering a novel technical approach and theoretical support for cognitive radio research. Moreover, the introduction of the multi-head self-attention mechanism further optimizes the model’s adaptability to complex environments, demonstrating significant potential for practical applications.

Autonomous demon exploiting heat and information at the trajectory level

We propose an electronic bipartite system consisting of a working substance, in which a refrigeration process is implemented, and of a nonthermal resource region, containing a combination of different thermal baths. In the working substance, heat is extracted from the coldest of two electronic reservoirs (refrigeration) via heat transport and particle transport through a quantum dot. This quantum dot of the working substance is capacitively coupled to the resource region. In such a setup, a finite cooling power can be obtained in the working substance, while the energy exchange with the resource region exactly cancels out, on average. At the same time, is always exchanged, even on average, due to the capacitive coupling between the two parts of the bipartite system. The proposed system therefore implements an autonomous demon with fully vanishing heat extraction from the resource. Unlike macroscopic machines, nanoscale machines exhibit large fluctuations in performance, so precision becomes an important performance quantifier. We give a comprehensive description of the thermodynamic performance of the proposed autonomous demon in terms of stochastic trajectories and of full counting statistics and demonstrate that the precision of the cooling power strongly depends on the operation principle of the device. More specifically, the interplay of information flow and counterbalancing heat flows dramatically impacts the trade-off between cooling power, efficiency, and precision. We expect this insight to be of relevance for guiding the design of energy-conversion processes exploiting nonthermal resources. Published by the American Physical Society 2025

Optimization of the Effect of Network Information Flow Advertising Based on User Behavior Data Encryption and Deep Learning

Existing network information flow advertising mostly relies on user behavior data, which is easy to cause user privacy leakage and low precision of advertising delivery. This article combines user behavior data encryption and deep learning technology to propose a method for optimizing the effect of network information flow advertising. The user behavior data is encrypted using Paillier homomorphic encryption technology. Then, the stacked auto encoder (SAE) is used to extract high-order nonlinear features, and the shallow features are automatically combined based on Field-aware Factorization Machines (FFM) to construct a SAE-FFM comprehensive model. At the same time, dropout is used to reduce model overfitting, and the advertising effect is optimized through two-stage pre-training and fine-tuning. The experimental results show that when the number of impressions is 10,000, the average advertising click through rate and conversion rate of the SAE-FFM model are 4.50% and 0.59% respectively, and the information leakage rate is 0.052%. The results show that the SAE-FFM model can greatly improve the click through rate and conversion rate of advertisements, prevent privacy leakage, and help the development of the information flow advertising industry.

A Deep Multi-Task Learning Model for OD Traffic Flow Prediction Between Highway Stations

The rapid development of highways greatly affects the flow of people, finance, goods, and information between cities, and monitoring the OD flow of travel has become a very important task for intelligent transportation systems (ITS). The temporal dynamics and complex spatial correlations of OD traffic distribution, as well as the sparsity and incompleteness of data caused by uneven traffic distribution, make OD traffic prediction complex and challenging. This paper proposes a multi-task prediction model for OD traffic between highway stations. The model adopts a hard parameter shared multi-task learning network structure, which is divided into sub-task learning inflow trend modules, sub-task learning outflow trend modules, and main task learning modules for OD traffic. At the same time, the attraction intensity matrix between stations is constructed using the population density data as the external feature of the sub-task module for outlet outflow flow, and stronger constraints between tasks are introduced to achieve better fitting results. Finally, an OD flow prediction case experiment was conducted between stations on highways in Sichuan Province. The experimental results showed that the proposed model not only had higher accuracy in predicting results than other baseline models, but also had better effectiveness and robustness.

First bathymetric studies of the Paragominas stream, urban stretch, in the municipality of the same name, Pará, Brazil

Streams in urban areas undergo physical and structural changes that need to be studied. The Paragominas creek has a gap in this type of study, and this justified this research. Its aim is to generate bathymetric information (width, length, depth, cross-sectional area and flow) in five areas of the urban stretch, from the Constantino Pereira do Sacramento highway (A1) to Padre Carvalho street (A5), where the Paragominas stream flows into the Uraim river. The methodology used was investigative, qualitative-quantitative and observational in nature. The primary data (width, length, depth, cross-sectional area and flow) was obtained in accordance with the proposals of the National Water and Water Resources Agency. A 1000 mL polyethylene terephthalate float was used to measure water flow velocity. The data obtained and analyzed indicated that the highest average values for the variables measured were identified in A1 - Rodovia Constantino Pereira do Sacramento (cross-sectional area: 12.0 ± 2.3; width: 1.8 ± 0.3; depth: 1.8 ± 0.2); A2 - Avenida Selecta (flow: 11.0 ± 8.4) and A4 - Lago Verde - (length: 100 ± 0.0). The data also showed that as the areas analyzed were urbanized, the average values decreased, as did the flow rate (1.7 ± 0.7) and depth (0.7 ± 1.3). In A3 - Camboatã. Urbanization therefore directly affects the bathymetry of urban streams. This data can help in the development of actions against the floods that occur in the areas analyzed.

Unlocking the application of AI for portfolio maximization investment decision

Purpose of research The aim of the study is to examine the transformative role of artificial intelligence (AI) in maximizing portfolio investment decision-making process. AI application tools specifically focus on data processing & analysis, portfolio optimization, algorithmic trading & automated execution, chatbots & robotic advisory services, and predictive analytics. The purpose of study is to provide how these technologies can maximize the investment strategies by improving the precision of forecasting, return optimization and risk management. Moreover, conceptual framework that shows the dynamic interaction between AI application tools, investors, and financial markets. Design/Methodology The conceptual approach evaluates the integration of AI application tools into investment decisions. The analysis of current applications of AI in finance develops detailed conceptual model that maps the flow of information between investors, AI application tools, in financial markets. The study uses secondary data from existing literature, research papers, and case studies to validate the efficiency of AI-driven investment strategies. Results/Findings The results show that AI significantly improves investment decision-making processes accuracy and efficiency. The conceptual framework developed in this study establishes how AI-driven application such as portfolio optimization and predictive analytics directly contribute to maximizing returns on investments while minimizing risks. Furthermore, the role of chatbots and robotic advisory services in providing tailored investment advice, which eventually results in more strategic investment decisions. Practical Implications Artificial intelligence (AI) can help investors, financial institution that make specific investment plans, and financial advisory services. The findings of this research indicate that AI tools significantly improve investment portfolio performance by providing real-time database management system. The conceptual framework aids in understanding the association between AI application tools and financial markets. This study concludes that combining AI application tools into investment strategies signifies a key innovation for achieving competitive edge in a dynamic financial market.

Dinamika Regulasi Komunikasi Publik dalam Sistem Sosial: Perspektif Autopoiesis Luhmann

This study aims to analyze the dynamics of public communication regulation in the social system from the perspective of Luhmann's autopoiesis. This study uses a qualitative method with a library research approach. The data of this study is sourced from relevant literature. Data analysis is carried out by reducing data, presenting data, and drawing conclusions. The validity of the data in this study was carried out through a credibility test. The results of this study show that in the dynamics of public communication regulation in the social system based on Luhmann's autopoiesis perspective, public communication regulation functions to maintain a balance between information disclosure, control, and system adaptability to rapid social change. Luhmann emphasized that public communication systems must continue to produce and reproduce elements that ensure their functions continue to function, such as transparency, trust, and responsiveness. Adaptive regulations allow the government to interact effectively with the public, especially in the digital era which presents challenges such as misinformation, disinformation, and rapid information flow. Cross-sector collaboration and regulatory flexibility are needed to maintain social stability and strengthen the relationship between the government and society, with information disclosure as a key pillar. However, the regulation must also include privacy protection and control over the spread of invalid information, so that public communication systems remain relevant, accountable, and able to adapt to evolving social dynamics.

Altered brain functional connectivity in patients with tension-type headache.

To evaluate whether patients with tension-type headache (TTH) exhibit abnormal brain functional connectivity compared to healthy controls. TTH is one of the most prevalent headache disorders throughout the world. The present study delves into brain functional connectivity in patients with TTH to enhance the understanding of its underlying pathophysiology. A cross-sectional study was conducted, enrolling patients with TTH diagnosed in line with the International Classification of Headache Disorders, 3rd edition beta criteria and a cohort of healthy controls (HCs). We used four metrics-global brain functional connectivity, functional connectivity, Granger causality analysis, and dynamic functional connectivity-to evaluate alterations of functional connectivity patterns in patients with TTH from both static and dynamic perspectives. Furthermore, correlational analyses were performed to explore the relationships between abnormal brain activities and clinical characteristics. A total of 33 patients with TTH (mean age = 42.3; 13 males/20 females) and 30 HCs (mean age = 37.1; 13 males/17 females) were included in the current study. Compared to HCs, patients with TTH showed altered global brain functional connectivity in the right dorsolateral superior frontal gyrus (SFGdor, t = 4.60). Abnormal functional connectivity was also detected between the right SFGdor and the right superior temporal gyrus (t = 4.56). Furthermore, the right SFGdor exhibited altered information flow with several brain regions, including the left precuneus (t = 5.16), right middle temporal gyrus (MTG, t = 4.72/-4.41), right inferior temporal gyrus (t = 4.64), right caudate nucleus (t = 4.09), and right thalamus (THA, t = -4.04). In terms of dynamic functional connectivity, disconnection was observed between the right SFGdor and the right MTG (t = -3.10), right Rolandic operculum (ROL, t = 3.60), left opercular inferior frontal gyrus (t = -3.48), and left medial superior frontal gyrus (t = -3.00). In addition, the correlation analyses revealed that activities in the MTG (r = 0.48), THA (r = -0.38), and ROL (r = 0.36) were significantly correlated with disease duration, while THA activity was associated with Visual Analogue Scale scores (r = 0.50). This study revealed alterations in both static and dynamic brain functional connectivity in patients with TTH within regions implicated in sensory perception, emotional processing, cognition, and pain regulation. These results may promote the understanding of the neural networks involved in TTH and potentially inform future therapeutic approaches for the condition.

The Impact of Family Determinants on the Media Coverage of Family Business Activism in Sustainability

ABSTRACTMedia coverage is a key factor in increasing the flow and accessibility of information among the firm's stakeholders. Drawing on the socioemotional wealth (SEW) perspective, we investigate the antecedents of the level of media coverage of family firms' activism in sustainability. Using hand‐collected data from a sample of public Italian family firms, we provide several novel contributions to the academic debate. First, we find that family involvement on the board of directors is negatively associated with the level of media coverage of the family firm's engagement in sustainability. Conversely, we find that later generational stages of the family business are positively associated with the level of corporate media coverage on sustainability. Our study advances the literature on both corporate sustainability issues and media coverage in the context of family businesses.

Implementation of an Integrated Framework of Lean Manufacturing and Industry 4.0 toward Sustainability: A Case Study of the Indian Ceramic Industry

The paper aims to show through a case study of the semi-process sector (ceramic Industry) that Lean manufacturing and Industry 4.0 technologies eliminate waste and improve value irrespective of the type of industry. This case study illustrates how to implement the Lean and Industry 4.0 principles in a labor-intensive, order-based, medium-scale manufacturing environment. This study also aims to demonstrate the usefulness of Lean and Industry 4.0 in reducing defects that cause the organization’s sustainable growth. For this purpose, ABC Ceramic Industry was selected, and analyzed the production process of tile manufacturing was analyzed. The industry needs help in fulfilling customer demand due to waste in the production system. To overcome some of these problems, Lean manufacturing and Industry 4.0 technologies are used to identify and reduce non-value-added activities from the production line. The study found that the processing time is reduced by 10%, the waiting time is reduced by 12.54%, and defects in pre-kiln, kiln, and post-kiln are reduced by 27.45%, 14.65%, and 21.83%, respectively. The broken tiles are minimized by 24.82%. At the same time, some qualitative benefits of skill upgradation, real-time information flow, value integration, and cultural changes were also noticed.

The level of cognitive functioning in school-aged children is predicted by resting EEG Directed Phase Lag Index

Quantifying cognitive potential relies on psychometric measures that do not directly reflect cortical activity. While the relationship between cognitive ability and resting state EEG signal dynamics has been extensively studied in children with below-average cognitive performances, there remains a paucity of research focusing on individuals with normal to above-average cognitive functioning. This study aimed to elucidate the resting EEG dynamics in children aged four to 12 years across normal to above-average cognitive potential. Our findings indicate that signal complexity, as measured by Multiscale Entropy (MSE), was not significantly predictive of the level of cognitive functioning. However, utilizing Directed Phase Lag Index (DPLI) as an effective connectivity measure, we observed consistent patterns of information flow between anterior and posterior regions. Fronto-parietal as well as local connectivity patterns were seen across most of the cognitive functions. Moreover, specific connectivity patterns were obtained for each intellectual quotient index (namely verbal comprehension, visuospatial, fluid reasoning, and processing speed indexes as well as full-scale intellectual quotient). These results underscore the presence of long-range connections and support fronto-parietal theories of cognitive abilities within the resting state brain dynamics of children.

Sistem Chabot Layanan Informasi Mahasiswa Menggunakan Algoritma Long Short-Term Memory

In the era of globalization and rapid information flow, the demand for efficient and accurate information, especially within academic institutions, is rising. Students often face challenges in accessing educational resources and real-time information, particularly outside official working hours. Existing online information services have limitations in providing continuous access. This research focuses on developing and evaluating a student information service chatbot system at Universitas Jenderal Achmad Yani Yogyakarta (UNJAYA) using the Long Short-Term Memory (LSTM) algorithm. The primary objective is to create a system that delivers real-time, accurate, and efficient information services to students. The Machine Learning Development Cycle (MLDC) is employed in the model development process, including stages such as data collection, processing, model training, evaluation, and implementation. The system's performance is tested using a questionnaire distributed to students, with responses measured on a Likert scale. The results demonstrate a chatbot with a 97.76% accuracy rate, 98.34% precision, and 97.76% recall. The overall system evaluation yielded an average score of 3.87, categorized as good. This research concludes that the LSTM-based chatbot successfully enhances information services at the Faculty of Engineering and Information Technology, providing an innovative solution to meet student needs in real-time

Preservation of Speculative Constant-Time by Compilation

Compilers often weaken or even discard software-based countermeasures commonly used to protect programs against side-channel attacks; worse, they may also introduce vulnerabilities that attackers can exploit. The solution to this problem is to develop compilers that preserve such countermeasures. Prior work establishes that (a mildly modified version of) the CompCert and Jasmin formally verified compilers preserve constant-time, an information flow policy that ensures that programs are protected against timing side-channel attacks. However, nothing is known about preservation of speculative constant-time, a strengthening of the constant-time policy that ensures that programs are protected against Spectre-v1 attacks. We first show that preservation of speculative constant-time fails in practice by providing examples of secure programs whose compilation is not speculative constant-time using GCC (GCC -O0 and GCC -O1) and Jasmin. Then, we define a proof-of-concept compiler that distills some of the critical passes of the Jasmin compiler and use the Coq proof assistant to prove that it preserves speculative constant-time. Finally, we patch the Jasmin speculative constant-time type checker and demonstrate that all cryptographic implementations written in Jasmin can be fixed with minimal impact.

Everyone Is Safe Now: Constructing the Meaning of Data Privacy Regulation in Vietnam

Abstract This article explores Vietnam’s distinctive approach to data privacy regulation and its implications for the established understandings of privacy law. While global data privacy regulations are premised on individual freedom and integrity of information flows, the recent Vietnamese Decree 13/2023/NĐ-CP on Personal Data Protection (herein PDPD) prioritise state oversight and centralised control over information flows to safeguard collective interests and cyberspace security. The fresh regulatory logic puts data privacy under the regulation of government agencies and moves the privacy law arena even further away from the already distant judicial power. This prompts an exploration of the nuances underlying the ways regulators and the regulated communities understand data privacy regulation. The article draws on social constructionist accounts of regulation and discourse analysis to explore the epistemic interaction between regulators and those subject to regulation during the PDPD’s drafting period. The process is highlighted by the dynamics between actors within a complex semantic network established by the state’s policy initiatives, where tacit assumptions and normative beliefs direct the way actors in various communities favour one type of thinking about data privacy regulation over another. The findings suggest that reforms to privacy laws may not result in “more privacy” to individuals and that divergences in global privacy regulation may not be easily explained by drawing merely from cultural and institutional variances.

The Shadow Economy of Effort: Unintended Consequences of Pass/Fail Grading on Medical Students' Clinical Education and Patient Care Skills.

The shift to pass/fail grading in undergraduate medical education was designed to reduce medical students' stress. However, this change has given rise to a "shadow economy of effort," as students move away from traditional didactic and clinical learning to engage in increasing numbers of research, volunteer, and work experiences to enhance their residency applications. These extracurricular efforts to secure a residency position are sub-phenomena of the hidden curriculum. Medical schools do not officially require all the activities students need to be most competitive for residency selection; therefore, students, as rational actors, participate in the activities they think will most help them succeed.Here, the authors frame residency application and selection as a complex adaptive system (CAS), which self-organizes without centralized control or hierarchical intent. Individuals in a CAS operate in environments marked by volatility, randomness, and uncertainty-all of which are abundant in the residency selection process. Outcomes in such systems, like the development of a shadow economy, are novel, emergent, and cannot always be anticipated. To address these challenges, the authors suggest the need for deep understanding of the system's elements, interrelationships, and dynamics, including feedback loops and emergent properties. Optimizing the results of a CAS requires incentivizing outcomes over activities, ensuring open information flow, and engaging in continuous monitoring and evaluation.The current pass/fail era and resultant shadow economy of effort risk creating a triple harm by devaluing clinical excellence, burning out medical students, and potentially producing superficial, or worse inauthentic, academic and community work. Medical educators must optimize residency application and selection for cooperative outcomes and design incentives to ensure the outputs of medical education align student, institutional, patient, and societal goals. Without a set of predictive "answers," the authors suggest a process of determining actions to advance this ultimate aim and reduce harm.

Causal Discovery for Rolling Bearing Fault Under Missing Data: From the Perspective of Causal Effect and Information Flow

Causal Discovery for Rolling Bearing Fault Under Missing Data: From the Perspective of Causal Effect and Information Flow