Information Flow Control Research Articles

A dynamic management model for sustainable drug supply chain in hospital pharmacies in Iran.

Sustainable supply chain management encompasses the strategic coordination and control of material, information, and financial flows, as well as the collaborative efforts among the entities engaged in the medicinal supply chain. This research proposes a dynamic and sustainable supply chain management model tailored explicitly for the inpatient pharmacies of Medical Centers and Hospitals affiliated with Iran University of Medical Sciences. This is a quantitative study in terms of research objective and a qualitative study based on the stages in the conceptual development of the model. Therefore, the current study can be considered a mixed-methods approach. After identifying the key factors influencing the sustainability of the medicine supply chain, we conducted a dynamic analysis of the problem using system dynamics methodology. In order to simulate the system's behavior over 24 months, we utilized a combination of existing documentary information and expert opinions. The developed model was implemented using Vensim PLE software, allowing us to simulate and analyze the impact of various policies on the system. Medicine disposal exhibited an upward trend, particularly during the second 12-month period. Conversely, the trend of medicine expirations remained relatively stable in the initial months but showed an upward trajectory after that. The cost associated with disposed medicine experienced a consistent increase, with a higher rate observed during the second 12-month period. In contrast, sales of low-consumable medicine experienced a significant initial surge followed by a slower growth rate. Finally, the pharmacy's profit demonstrated an overall increasing trend, although the rate of increase was higher during the first 12 months. Among the various scenarios considered, namely "increasing the adequacy of human resources," "increasing the speed of response," and "utilizing pharmacists in the drug prescribing team," it was found that these interventions had a substantial effect on both enhancing the pharmacy's profit and reducing medication waste. Therefore, these scenarios were identified as having the most significant impact. The proposed model can serve as a valuable tool for forecasting and informing policy-making, providing insights into addressing the challenges associated with the sustainable drug supply chain in hospital pharmacies.

Discovering API usage specifications for security detection using two-stage code mining

An application programming interface (API) usage specification, which includes the conditions, calling sequences, and semantic relationships of the API, is important for verifying its correct usage, which is in turn critical for ensuring the security and availability of the target program. However, existing techniques either mine the co-occurring relationships of multiple APIs without considering their semantic relationships, or they use data flow and control flow information to extract semantic beliefs on API pairs but difficult to incorporate when mining specifications for multiple APIs. Hence, we propose an API specification mining approach that efficiently extracts a relatively complete list of the API combinations and semantic relationships between APIs. This approach analyzes a target program in two stages. The first stage uses frequent API set mining based on frequent common API identification and filtration to extract the maximal set of frequent context-sensitive API sequences. In the second stage, the API relationship graph is constructed using three semantic relationships extracted from the symbolic path information, and the specifications containing semantic relationships for multiple APIs are mined. The experimental results on six popular open-source code bases of different scales show that the proposed two-stage approach not only yields better results than existing typical approaches, but also can effectively discover the specifications along with the semantic relationships for multiple APIs. Instance analysis shows that the analysis of security-related API call violations can assist in the cause analysis and patch of software vulnerabilities.

Beyond the great firewall: EU and US responses to the China challenge in the global digital economy

ABSTRACT China’s Great Firewall was designed to control information flows to keep citizens in and the wider world out. Consequently, until recently China’s economic ascendancy was not mirrored in the global digital economy. However, China’s increasing digital footprint and championing of national technology firms present numerous economic and security challenges for the EU and US as the dominant digital powers. This paper investigates how growing perceptions of China-based threats in the digital domain have shaped EU (including Member State-level) and US policies. Two pertinent cases – Huawei (hardware provider) and TikTok (digital platform) – illustrate how threat perceptions have catalysed securitization moves to restrict Chinese firms’ roles within national digital economies. Western policymakers are increasingly concerned about China’s capacity to weaponize technologically interdependent relationships by exploiting its firms’ presence within Western networks. Yet significant differences between EU Member States’ perceptions precluded both a uniform EU-level response and more extensive transatlantic cooperation.

The Types and Motives of Maxim Infringement in Agatha Christie’s Murder on The Orient Express Novel (Pragmatic Approach)

This study performs a pragmatic analysis of Agatha Christie’s "Murder on the Orient Express," focusing on the intentional types and motives behind Grice’s conversational maxims infringement strategies. This research is descriptive qualitative that uses document analysis from the dialogues where the characters strategically infringe the maxims and find the motive(s) behind every infringement. The motives are namely withholding information, providing misleading details, using ambiguity, and creating communicative effects. This research uses domain, taxonomy, componential analysis, and cultural theme as well as inductive technique for the data analysis technique. The results show that there are 118 infringements of the maxims that fulfill the types of all the maxim of quality, quantity, manner, and relevance as well as the motives This research finds out that the story is dominated by the infringement of the maxim of relevance to control information flow which aligns with the novel’s purpose as a crime-solving detective novel.

Structure-inclusive similarity based directed GNN: a method that can control information flow to predict drug-target binding affinity.

Exploring the association between drugs and targets is essential for drug discovery and repurposing. Comparing with the traditional methods that regard the exploration as a binary classification task, predicting the drug-target binding affinity can provide more specific information. Many studies work based on the assumption that similar drugs may interact with the same target. These methods constructed a symmetric graph according to the undirected drug similarity or target similarity. Although these similarities can measure the difference between two molecules, it is unable to analyze the inclusion relationship of their substructure. For example, if drug A contains all the substructures of drug B, then in the message-passing mechanism of the graph neural network, drug A should acquire all the properties of drug B, while drug B should only obtain some of the properties of A. To this end, we proposed a structure-inclusive similarity (SIS) which measures the similarity of two drugs by considering the inclusion relationship of their substructures. Based on SIS, we constructed a drug graph and a target graph, respectively, and predicted the binding affinities between drugs and targets by a graph convolutional network-based model. Experimental results show that considering the inclusion relationship of the substructure of two molecules can effectively improve the accuracy of the prediction model. The performance of our SIS-based prediction method outperforms several state-of-the-art methods for drug-target binding affinity prediction. The case studies demonstrate that our model is a practical tool to predict the binding affinity between drugs and targets. Source codes and data are available at https://github.com/HuangStomach/SISDTA.

Agrochemical control of gene expression using evolved split RNA polymerase. II.

Agrochemical inducible gene expression system provides cost-effective and orthogonal control of energy and information flow in bacterial cells. However, the previous version of Mandipropamid inducible gene expression system (Mandi-T7) became constitutively active at room temperature. We moved the split site of the eRNAP from position LYS179 to position ILE109. This new eRNAP showed proximity dependence at 23 °C, but not at 37 °C. We built Mandi-T7-v2 system based on the new eRNAP and it worked in both Escherichia coli and Agrobacterium tumefaciens. We also induced GFP expression in Agrobacterium cells in a semi-in vivo system. The modified eRNAP when combined with the leucine zipper-based dimerization system, behaved as a cold inducible gene expression system. Our new system provides a means to broaden the application of agrochemicals for both research and agricultural application. Portions of this text were previously published as part of a preprint (https://www.biorxiv.org/content/10.1101/2024.04.02.587689v1).

γ1 GABAA Receptors in Spinal Nociceptive Circuits.

GABAergic neurons and GABAA receptors (GABAARs) are critical elements of almost all neuronal circuits. Most GABAARs of the CNS are heteropentameric ion channels composed of two α, two β, and one γ subunits. These receptors serve as important drug targets for benzodiazepine (BDZ) site agonists, which potentiate the action of GABA at GABAARs. Most GABAAR classifications rely on the heterogeneity of the α subunit (α1-α6) included in the receptor complex. Heterogeneity of the γ subunits (γ1-γ3), which mediate synaptic clustering of GABAARs and contribute, together with α subunits, to the benzodiazepine (BDZ) binding site, has gained less attention, mainly because γ2 subunits greatly outnumber the other γ subunits in most brain regions. Here, we have investigated a potential role of non-γ2 GABAARs in neural circuits of the spinal dorsal horn, a key site of nociceptive processing. Female and male mice were studied. We demonstrate that besides γ2 subunits, γ1 subunits are significantly expressed in the spinal dorsal horn, especially in its superficial layers. Unlike global γ2 subunit deletion, which is lethal, spinal cord-specific loss of γ2 subunits was well tolerated. GABAAR clustering in the superficial dorsal horn remained largely unaffected and antihyperalgesic actions of HZ-166, a nonsedative BDZ site agonist, were partially retained. Our results thus suggest that the superficial dorsal horn harbors functionally relevant amounts of γ1 subunits that support the synaptic clustering of GABAARs in this site. They further suggest that γ1 containing GABAARs contribute to the spinal control of nociceptive information flow.

Application and Optimization of Multi-Factor Authentication and Biometric Technology in Power Grid Energy Network Access Control

With the sudden development in electronic and network technology, the power grid has emerged in several evolved nations and other areas with faster development. Energy storage can enhance the stability, resiliency and efficiency of the electric smart grid. The power grid transfers energy via bidirectional control of energy and information flow. In the power grid, a smart meter is an important component that is deployed on the user side to determine the consumption of energy regularly. However, the data about the usage of electricity leads to the leakage of the private information of the user, which threatens the privacy of the user. The attackers can change the in-transit message induce wrong information and sometimes cause disastrous action. Hence, it is important to assure data security and privacy of users by conducting authentication in power grid communication. An optimization technique named Puzzle Optimization Algorithm (POA) is designed for the efficient privacy-preserving aggregation approach with multi-factor authentication and a biometric scheme. Also, optimal key-based Elliptic Curve Cryptography (ECC) is used to encrypt the sensitive data that are securely stored. Authentication offers high security and texture analysis. In the registration phase, the privacy information of the person with their biometrics is the important feature point of the individual unique identification. Additionally, the designed approach is compared with other existing optimizations to analyse the functionality of the implemented technique. The simulation outcome defined that the suggested technique enhances user privacy and information security than other standard approaches.

Proton Conducting Neuromorphic Materials and Devices.

Neuromorphic computing and artificial intelligence hardware generally aims to emulate features found in biological neural circuit components and to enable the development of energy-efficient machines. In the biological brain, ionic currents and temporal concentration gradients control information flow and storage. It is therefore of interest to examine materials and devices for neuromorphic computing wherein ionic and electronic currents can propagate. Protons being mobile under an external electric field offers a compelling avenue for facilitating biological functionalities in artificial synapses and neurons. In this review, we first highlight the interesting biological analog of protons as neurotransmitters in various animals. We then discuss the experimental approaches and mechanisms of proton doping in various classes of inorganic and organic proton-conducting materials for the advancement of neuromorphic architectures. Since hydrogen is among the lightest of elements, characterization in a solid matrix requires advanced techniques. We review powerful synchrotron-based spectroscopic techniques for characterizing hydrogen doping in various materials as well as complementary scattering techniques to detect hydrogen. First-principles calculations are then discussed as they help provide an understanding of proton migration and electronic structure modification. Outstanding scientific challenges to further our understanding of proton doping and its use in emerging neuromorphic electronics are pointed out.

Patchy energy landscapes promote stability of small groups of active particles

Group formation and coordination are fundamental characteristics of living matter, essential for performing tasks and ensuring survival. Interactions between individuals play a key role in group formation, and the impact of resource distributions is a vibrant area of research. As of now, an understanding of how patchy resource distributions determine group dynamics is not yet fully understood. Studying active particles in controlled optical landscapes as energy sources, we demonstrate a non-monotonic dependency of group size on landscape patchiness, with the smallest groups forming when the patches match the active particles’ size. A similar relationship is observed in terms of group stability, evidenced by a reduced rate of individual exchange in patchy environments compared to homogeneous conditions. Reduced group sizes can be beneficial to optimise resources in heterogeneous environments and to control information flow within populations. Our results provide insights into the role of patchy landscapes and uneven energy distributions in active matter and hold implications for refining swarm intelligence algorithms, enhancing crowd management techniques, and tailoring colloidal self-assembly.

A decentralized model for usage and information flow control in distributed systems

Data usage control enables data owners to enforce policies for their data, by defining authorizations, but also obligations, which are actions to be performed before, during or after being granted access such as accepting web cookies, and conditions bearing on the system and environment attributes, e.g., the time. Usage control is often coupled with information flow control to monitor how data are propagated. While usage control is well established and modeled in centralized systems, the literature has only partially addressed usage control for distributed systems, for instance by distributing the usage control system components. However, when it comes to assigning policy to certain data, it is always enforced by a central authority. This paper proposes an extended usage control model to integrate decentralized information flow control (DIFC), which enables users to decide collectively which policy to apply to their common data. Functions to handle connection status aspects are also considered, for dynamic Internet of Things (IoT) or peer-to-peer networks where parts of the distributed network can be disconnected. Architectural aspects and formal definitions to enable decentralized policies for shared data are proposed as a novelty, resulting from the integration of DIFC. We used the TLA+ formal specification language on the proposed model and its attached model checker TLC to detect potential issues. We detected potential deadlocks due to the new connection functions as well as temporal ordering issues then suggested mitigations accordingly. A privacy analysis is provided using a car-sharing scenario to highlight the benefits of usage control.

Cocoon: Static Information Flow Control in Rust

Information flow control (IFC) provides confidentiality by enforcing noninterference, which ensures that high-secrecy values cannot affect low-secrecy values. Prior work introduces fine-grained IFC approaches that modify the programming language and use non-standard compilation tools, impose run-time overhead, or report false secrecy leaks—all of which hinder adoption. This paper presents Cocoon, a Rust library for static type-based IFC that uses the unmodified Rust language and compiler. The key insight of Cocoon lies in leveraging Rust’s type system and procedural macros to establish an effect system that enforces noninterference. A performance evaluation shows that using Cocoon increases compile time but has no impact on application performance. To demonstrate Cocoon’s utility, we retrofitted two popular Rust programs, the Spotify TUI client and Mozilla’s Servo browser engine, to use Cocoon to enforce limited confidentiality policies

Analysis of information flow security using software implementing business logic based on stored database program blocks

Objectives. Verification of software security is typically performed using dynamic and static analysis tools. The corresponding types of analysis do not usually consider the business logic of the software and do not rely on data access control policies. A modern approach to resolving this problem is to implement language-based information flow control. Despite a large amount of research, mechanisms for information flow control in software are not widely used in practice. This is because they are complex and impose increased demands on developers. The aim of the work is to transfer information flow control from the language level to the level of formal verification. This will enable the functions of controlling data integrity and confidentiality in software to be isolated into a separate task, which can be resolved by information security analysts.Methods. The research is based on general formal security methods for computer systems and formal verification methods. The algorithm developed by the author for checking security specifications and resolving security violations uses temporal logic of actions.Results. The technology is presented as a step-by-step approach to resolving specific tasks, including the following: designing a database (DB) for storing and processing sensitive information; analyzing dependencies and identifying relevant sets of program blocks in the DB; generating TLA+ specifications for the identified program blocks; labeling specifications according to global security policy rules and additional constraints; applying the specification verification algorithm, and resolving security violations while providing recommendations for software developers. The procedure also involves analyzing labeled data, in order to control the spread of verified program block output values in external software modules.Conclusions. The technology presented herein does not require developers to include redundant annotations describing security policy rules. The function of analyzing information flows with reference to predefined access restrictions is moved to a separate stage of the software development life cycle.

Decision-Making Engagement Preferences among Older Adults with CKD.

Older adults with kidney failure face preference-sensitive decisions regarding dialysis initiation. Despite recommendations, few older patients with kidney failure experience shared decision-making. Clinician uncertainty about the degree to which older patients prefer to engage in decision-making remains a key barrier. Mixed-methods explanatory, longitudinal, sequential design at 4 diverse U.S. centers with patients (English-fluent, aged ≥70 years, CKD stages 4-5, nondialysis) from 2018 to 2020. Patient preferences for engagement in decision-making were assessed using the Control Preference Scale (CPS), reflecting the degree to which patients want to be involved in their decision-making: active (patient prefers to make final decision), collaborative (patient wants to share decision-making with clinician), or passive role (patient wants clinician to make final decision). Semi-structured interviews about engagement and decision-making were conducted in two waves (2019, 2020) with purposively sampled patients and clinicians. Descriptive statistics and ANOVA were used for quantitative analyses; thematic and narrative analyses were used for qualitative data. Among 363 patient-participants, mean age was 78 ± 6 years, 42% were female, and 21% had a high school education or less. CPS responses reflected that patients preferred to engage actively (48%) or collaboratively (43%) versus passively (8%). Preferred roles remained stable at three-month follow-up. Seventy-six participants completed interviews (45 patients, 31 clinicians). Four themes emerged: Control preference roles reflect levels of decisional engagement; Clinicians control information flow, especially about prognosis; Adapting clinical approach to patient preferred roles; and Clinicians' responsiveness to patient preferred role supports patients' satisfaction with shared decision-making. Most older adults with advanced CKD preferred a collaborative or active role in decision-making. Appropriately matched information flow with patient preferences was critical for satisfaction with shared decision-making.

Effective community detection with topic modeling in article recommender systems using LS-SLM and PCC-LDA

This paper introduces an innovative approach, the LS-SLM (Local Search with Smart Local Moving) technique, for enhancing the efficiency of article recommendation systems based on community detection and topic modeling. The methodology undergoes rigorous evaluation using a comprehensive dataset extracted from the “dblp. v12.json” citation network. Experimental results presented herein provide a clear depiction of the superior performance of the LS-SLM technique when compared to established algorithms, namely the Louvain Algorithm (LA), Stochastic Block Model (SBM), Fast Greedy Algorithm (FGA), and Smart Local Moving (SLM). The evaluation metrics include accuracy, precision, specificity, recall, F-Score, modularity, Normalized Mutual Information (NMI), betweenness centrality (BTC), and community detection time. Notably, the LS-SLM technique outperforms existing solutions across all metrics. For instance, the proposed methodology achieves an accuracy of 96.32%, surpassing LA by 16% and demonstrating a 10.6% improvement over SBM. Precision, a critical measure of relevance, stands at 96.32%, showcasing a significant advancement over GCR-GAN (61.7%) and CR-HBNE (45.9%). Additionally, sensitivity analysis reveals that the LS-SLM technique achieves the highest sensitivity value of 96.5487%, outperforming LA by 14.2%. The LS-SLM also demonstrates superior specificity and recall, with values of 96.5478% and 96.5487%, respectively. The modularity performance is exceptional, with LS-SLM obtaining 95.6119%, significantly outpacing SLM, FGA, SBM, and LA. Furthermore, the LS-SLM technique excels in community detection time, completing the process in 38,652 ms, showcasing efficiency gains over existing techniques. The BTC analysis indicates that LS-SLM achieves a value of 94.6650%, demonstrating its proficiency in controlling information flow within the network.

Description of Paralocks language semantics in TLA+

One of the basic aspects of information flow control in applications is security policy language. Such language should allow to define security policies for evaluation environment elements in coherence with higher level access control rules. So the language is expected to be flexible because there may be different access control paradigms implemented on the system level: mandatory, role-based etc. An application may also have its own specific restrictions. Finally it is also desirable that the language support declassification (controlled release of information) during the computation. One of such languages is Paralocks. The research is devoted to the logical semantics of modified version of Paralocks realized in TLA+. Paralocks represents a language basis for the perspective information flow control platform PLIF which is being developed for the analysis of PL/SQL program blocks with author’s participation. It includes the proofs of the partial order and lattice defined on the set of security policy expressions.

AI-Based System Automates Textual Classification of Daily Drilling Reports

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper OTC 32978, “Development and Implementation of an AI-Based System To Automate Textual Classification on Daily Drilling Reports,” by Stephan Perrout, Aliel F. Riente, and Guilherme S.F. Vanni, SPE, Petrobras, et al. The paper has not been peer reviewed. Copyright 2023 Offshore Technology Conference. Reproduced by permission. _ Structured daily drilling reports (DDRs) are a rich source of information that allows better planning, more-accurate risk analysis, and improved key performance indicators and contracts. However, such information is originally stored in a free-text and unstructured format, which becomes difficult for efficient data mining. With the advance of artificial intelligence (AI) technologies, particularly AI language models, applying such techniques over unstructured data has become critical to digital transformation. The complete paper presents an approach for automatic DDR classification that incorporates new techniques of AI. Introduction This work addresses the complex task of automatic classification of DDRs according to a newly proposed ontology. The ontology follows a hierarchical model that classifies actions into three or four levels depending on the intervention, considering drilling, completion, and abandonment. Each event has an ontology built and reviewed by experts in oil and gas. Classifying DDR constitutes a demanding task, and effectively exploiting AI-based models represents a promising solution. This work bridges the gap by proposing a classifier based on transformers along with recurrent neural networks (RNNs) to classify reported events described in unstructured text related to drilling, completion, and abandonment interventions. A large number of DDRs was used for training and validation of the proposed classifier, yielding promising results for key processes in the company. Neural-Network Techniques Bidirectional Long Short-Term Memory. Early neural-network models are characterized by inputs of fixed length. This is a drawback when working with texts, however, because sentences vary in their number of words. To overcome such an issue and to process data sequentially, RNNs were proposed. The RNNs are characterized by a set of parameters inherent from the early models plus an internal memory (a hidden or internal state) responsible for storing the context of the sequence being processed. Long short-term memory (LSTM) is a variation of RNN proposed to mitigate two problems: Information can be easily lost when processing very long sequences, and the gradient can become quite low because of the high number of mathematical operations performed during the processing while remaining far from reaching the threshold. LSTM consists of a set of parameters called the input gate, forget gate, and output gate that control information flow through the network. This set of additional parameters helps to maintain only what is important for the internal state of the network besides controlling the output. BiLSTM is a variant of LSTM that comprises two LSTMs. One processes texts from left to right, and the second one processes texts from right to left. This feature allows “future” elements to be part of the model’s decision process for “past” elements. The final classification is the combination of the output of both LSTMs.

Multimodal consistency-specificity fusion based on information bottleneck for sentiment analysis

Sentiment analysis, a subtask of affective computing, endows machines with the ability to sense and comprehend emotions. Recently, research attention has shifted from traditional isolated modality to ubiquitous multi-modalities, requiring to model the complex relationships between modalities and extract the task-relevant information. However, current methods only focus on modality consistency neglecting the complementary relationship and specificity information within each modality. Furthermore, most studies regard multimodal fusion as a mere process of information integration without considering the control of information flow. We propose a variational model that explicitly decomposes unimodal representations into two types, capturing consistency and specificity information, respectively. Following the information bottleneck principle, the implementation optimizes the fusion representation by maximizing its mutual information with consistency and specificity representations while minimizing mutual information with raw inputs. With information integrity constraints and task label supervision, the fusion representation preserves task-relevant information and discards irrelevant noise. Finally, quantitative and qualitative experiments on two benchmark datasets show that our method achieves competitive performance compared to recent baselines, for multimodal sentiment analysis.

A Four-layer Cyber-physical Security Model for Electric Machine Drives considering Control Information Flow

A Four-layer Cyber-physical Security Model for Electric Machine Drives considering Control Information Flow

Pallet drop-size optimisation and its effect on the supply chain using a cost-to-serve model

The study explores the critical role of transportation within the Supply Chain, emphasising efficient planning, implementation, and control of goods and information flow from suppliers to customers. Focused on optimising the number of pallets per shipment and occurrence of the shipments to decrease secondary transport costs and environmental impact. The study is conducted with a leading Fast Moving Consumer Goods (FMCG) company in the UK by deploying the methods; Define, Measure, Analyse, Improve, Control (DMAIC) for data collection and Cost-to-Serve (CTS) for data analysis. Initially, a new Cost-to-Serve tool is developed to visualise customer performance and identify opportunities for improvement. Subsequently, a secondary tool is created to monitor the performance of identified customers. An Excel simulator is constructed to extract key performance indicators for logistics performance. Results reveal significant cost savings, with £207 K saved in the first five months of 2023 through shipment reduction of 9.09% compared to 2022. Even with inflation, the cost per pallet has remained steady, showing minimal variation from the average of 2022. This underlines the efficacy of the CTS tool in enhancing secondary transportation performance within the wider supply chain context.