Information Flow Research Articles

Access@tour by Action: A Platform for Improving Accessible Tourism Conditions

Accessible tourism has become relevant, generating significant economic and social impacts. Even though the accessible tourism market is rising and presents an excellent business opportunity, this market is largely ignored, as it is challenging to stimulate the flow of accessibility information. Accessible technologies, such as tourism information systems, can be a potential solution, increasing accessibility through communication. However, these solutions are few and tend to fail the integration of users upon development processes. This research aims to present a technological platform to improve accessibility in the tourism industry. The name of this accessible and adaptable technological solution is access@tour by action, and it was created following a user-centered design methodology. This development involved a requirement engineering process based on three crucial stakeholders in accessible tourism: educational institutions, supply agents, and demand agents. The design phase was achieved with the help of a conceptual model based on a unified modeling language. The initial prototype of the solution, created in Adobe XD, implements a wide range of informational and accessibility requirements. Some access@tour by action interfaces outline the design, content, and primary functionalities. By linking technological development, tourism, and social inclusion components, this study highlights the relevance and interdisciplinarity of processes in developing accessible information systems.

An Information Ecosystem Map of Resources Supporting the Mobilization and Discovery of Paleontological Specimen Data

Over the last decade, the United States paleontological collections community has invested heavily in the digitization of specimen-based data, including over 10 million USD funded through the National Science Foundation’s Advancing Digitization of Biodiversity Collections program. Fossil specimen data—9.0 million records and counting (Global Biodiversity Information Facility 2024)—are now accessible on open science platforms such as the Global Biodiversity Information Facility (GBIF). However, the full potential of this data is far from realized due to fundamental challenges associated with mobilization, discoverability, and interoperability of paleontological information within the existing cyberinfrastructure landscape and data pipelines. Additionally, it can be difficult for individuals with varying expertise to develop a comprehensive understanding of the existing landscape due to its breadth and complexity. Here, we present preliminary results from a project aiming to explore how we might address these problems. Funding from the US National Science Foundation (NSF) to the University of Colorado Museum of Natural History, Smithsonian National Museum of Natural History, and Arizona State University will result in, among other products, an “ecosystem map” for the paleontological collections community. This map will be an information-rich visualization of entities (e.g. concepts, systems, platforms, mechanisms, drivers, tools, documentation, data, standards, people, organizations) operating in, intersecting with, or existing in parallel to our domain. We are inspired and informed by similar efforts to map the biodiversity informatics landscape (Bingham et al. 2017) and the research infrastructure landscape (Distributed System of Scientific Collections 2024), as well as by many ongoing metadata cataloging projects, e.g. re3data and the Global Registry of Scientific Collections (GRSciColl). Our strategy for developing this ecosystem map is to model the existing information and systems landscape by characterizing entities, e.g. potentially in a graph database as nodes with relationships to other nodes. The ecosystem map will enable us to provide guidance for communities working across different sectors of the landscape, promoting a shared understanding of the ecosystem that everyone works in together. We can also use the map to identify points of entry and engagement at various stages of the paleontological data process, and to engage diverse members within the paleontological community. We see three primary user types for this map: people new(er) to the community, people with expertise in a subset of the community, and people working to integrate initiatives and systems across communities. Each of these user types needs tailored access to the ecosystem map and its community knowledge. By promoting shared knowledge with the map, users will be able to identify their own space within the ecosystem and the connections or partnerships that they can utilize to expand their knowledge or resources, relieving the burden on any single individual to hold a comprehensive understanding. For example, the flow of taxonomic information between publications, collections, digital resources, and biodiversity aggregators is not straightforward or easy to understand. A person with expertise in collections care may want to use the ecosystem map to understand why taxonomic identifications associated with their specimen occurrence records are showing up incorrectly when published to GBIF. We envision that our final ecosystem map will visualize the flow of taxonomic information and how it is used to interpret specimen occurrence data, thereby highlighting to this user where problems may be happening and whom to ask for help in addressing them (Fig. 1). Ultimately, development of this map will allow us to identify mobilization pathways for paleontological data, highlight core cyberinfrastructure resources, define cyberinfrastructure gaps, strategize future partnerships, promote shared knowledge, and engage a broader array of expertise in the process. Contributing domain-based evidence FAIRly*2 requires expertise that bridges the content (e.g. paleontology) and the mechanics (e.g. informatics). By centering the role of humans in open science cyberinfrastructure throughout our process, we hope to develop systems that create and sustain such expertise.

EFFICIENCY AND ACCURACY OF CONVOLUTIONAL AND FOURIER TRANSFORM LAYERS IN NEURAL NETWORKS FOR MEDICAL IMAGE CLASSIFICATION

In an era where information flow is moving at a rapid pace, image data processing is becoming increasingly important as technology advances, including in healthcare. Convolutional Neural Network (CNN) has been a common approach in image classification, but the larger the volume of data and the complexity of the task, the more expensive the computational cost of CNN. With the rapid growth in the amount of image data, efficiency in data processing is becoming increasingly important. In this study, the performance of neural network models using the convolution layer and Fourier transform layer in medical image data classification was compared. The results show that models with a Fourier transform layer tend to provide higher accuracy and better Area Under Curve (AUC) compared to models using a convolution layer. In addition, the model with the Fourier transform layer also shows faster execution time per epoch, which indicates efficiency in data processing. However, the convolution layer has an advantage in terms of model size, although it is not significantly different from the Fourier transform layer. In conclusion, the Fourier transform layer has an advantage in the classification of medical image data.

Enhancing Information Exchange in Ship Maintenance through Digital Twins and IoT: A Comprehensive Framework

This article proposes a comprehensive framework for enhancing information exchange in ship maintenance through the integration of Digital Twins (DTs) and the Internet of Things (IoT). The maritime industry faces significant challenges in maintaining ships due to issues like data silos, delayed information flow, and insufficient real-time updates. By leveraging advanced technologies such as DTs and IoT, this framework aims to optimize maintenance processes, improve decision-making, and increase the operational efficiency of maritime vessels. Digital Twins create virtual replicas of physical assets, allowing for continuous monitoring, simulation, and prediction of ship conditions. Meanwhile, IoT devices enable real-time data collection and transmission from various ship components, facilitating a seamless flow of information. This integrated approach enhances predictive maintenance capabilities, reduces downtime, and improves resource allocation. The article will delve into the architecture of the proposed framework, implementation steps, and potential challenges, supported by case studies that demonstrate its practical application and benefits. By addressing these aspects, the framework aims to provide a robust solution for modernizing ship maintenance operations and ensuring the longevity and reliability of maritime assets.

Deep Learning for Epileptic Seizure Detection Using a Causal-Spatio-Temporal Model Based on Transfer Entropy.

Drug-resistant epilepsy is frequent, persistent, and brings a heavy economic burden to patients and their families. Traditional epilepsy detection methods ignore the causal relationship of seizures and focus on a single time or spatial dimension, and the effect varies greatly in different patients. Therefore, it is necessary to research accurate automatic detection technology of epilepsy in different patients. We propose a causal-spatio-temporal graph attention network (CSTGAT), which uses transfer entropy (TE) to construct a causal graph between multiple channels, combining graph attention network (GAT) and bi-directional long short-term memory (BiLSTM) to capture temporal dynamic correlation and spatial topological structure information. The accuracy, specificity, and sensitivity of the SWEZ dataset were 97.24%, 97.92%, and 98.11%. The accuracy of the private dataset reached 98.55%. The effectiveness of each module was proven through ablation experiments and the impact of different network construction methods was compared. The experimental results indicate that the causal relationship network constructed by TE could accurately capture the information flow of epileptic seizures, and GAT and BiLSTM could capture spatiotemporal dynamic correlations. This model accurately captures causal relationships and spatiotemporal correlations on two datasets, and it overcomes the variability of epileptic seizures in different patients, which may contribute to clinical surgical planning.

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.

A strategic evaluation of BIM-driven information management in the context of ISO 19650-2 standard

PurposeThis paper presents an in-depth evaluation of BIM-driven information management in the context of ISO 19650-2. The study aimed to understand and evaluate the existing mechanisms and the recommended activities and tasks of collaborative information management, particularly as the Turkish AEC organizations are currently implementing ISO 19650-2.Design/methodology/approachThe study employed a qualitative research design based on information management stages of ISO 19650-2. Semi-structured interviews were conducted with purposefully selected professionals. The framework analyses were made with MAXQDA through conceptual coding to identify current drivers, challenges and incentives for BIM-driven information management. The frequencies of common themes, required tasks and semantic relationships were reported systematically and interpreted to provide a conceptual implementation map, practical insights and actionable recommendations.FindingsThe study results encompass strategies for establishing new tasks and implementation procedures to uphold business value by aligning information management standards with evolving project requirements, providing detailed and responsive EIRs, early use of CDEs, compliant production methods, clarity of roles and responsibilities, increased organizational capacity and capabilities, sustained planning and production quality and leveraging lessons learned.Originality/valueThe study provides evidence-based arguments and strategic insights for BIM-driven information management, focusing on the current implementation compatibilities with ISO 19650-2. Findings and discussions highlight the necessity of industry-wide transformations, reengineered activities/tasks and compliant information flows, which are crucial for adopting ISO 19650-2 and the formulation of national annex documents.

Clinical Data Flow in Botswana Clinics: Protocol for a Mixed-Methods Assessment.

Botswana has made significant investments in its health care information infrastructure, including vertical programs for child health and nutrition, HIV care, and tuberculosis. However, effectively integrating the more than 18 systems in place for data collection and reporting has proved to be challenging. The Botswana Health Data Collaborative Roadmap Strategy (2020-24) states that "there exists parallel reporting systems and data is not integrated into the mainstream reports at the national level," seconded by the Botswana National eLearning strategy (2020), which states that "there is inadequate information flow at all levels, proliferation of systems, reporting tools are not synthesized; hence too many systems are not communicating." The objectives of this study are to (1) create a visual representation of how data are processed and the inputs and outputs through each health care system level; (2) understand how frontline workers perceive health care data sharing across existing platforms and the impact of data on health care service delivery. The setting included a varied range of 30 health care facilities across Botswana, aiming to capture insights from multiple perspectives into data flow and system integration challenges. The study design combined qualitative and quantitative methodologies, informed by the rapid assessment process and the technology assessment model for resource limited settings. The study used a participatory research approach to ensure comprehensive stakeholder engagement from its inception. Survey instruments were designed to capture the intricacies of data processing, sharing, and integration among health care workers. A purposive sampling strategy was used to ensure a wide representation of participants across different health care roles and settings. Data collection used both digital surveys and in-depth interviews. Preliminary themes for analysis include perceptions of the value of health care data and experiences in data collection and sharing. Ethical approvals were comprehensively obtained, reflecting the commitment to uphold research integrity and participant welfare throughout the study. The study recruited almost 44 health care facilities, spanning a variety of health care facilities. Of the 44 recruited facilities, 27 responded to the surveys and participated in the interviews. A total of 75% (112/150) of health care professionals participating came from clinics, 20% (30/150) from hospitals, and 5% (8/150) from health posts and mobile clinics. As of October 10, 2023, the study had collected over 200 quantitative surveys and conducted 90 semistructured interviews. This study has so far shown enthusiastic engagement from the health care community, underscoring the relevance and necessity of this study's objectives. We believe the methodology, centered around extensive community engagement, is pivotal in capturing a nuanced understanding of the health care data ecosystem. The focus will now shift to the analysis phase of the study, with the aim of developing comprehensive recommendations for improving data flow within Botswana's health care system. DERR1-10.2196/52411.

Toward a more comprehensive understanding of network centrality disruption in amnestic mild cognitive impairment: a MEG multilayer approach

BackgroundAlzheimer’s Disease (AD) is the most common form of dementia. Its early stage, amnestic Mild Cognitive Impairment (aMCI), is characterized by disrupted information flow in the brain. Previous studies have yielded inconsistent results when using electrophysiological techniques to investigate functional connectivity changes in AD, and a contributing factor may be the study of brain activity divided into frequencies.MethodsOur study aimed to address this issue by employing a cross-frequency approach to compare the functional networks of 172 healthy subjects and 105 aMCI patients. Using magnetoencephalography, we constructed source-based multilayer graphs considering both intra- and inter-frequency functional connectivity. We then assessed changes in network organization through three centrality measures, and combined them into a unified centrality score to provide a comprehensive assessment of centrality disruption in aMCI.ResultsThe results revealed a noteworthy shift in centrality distribution in aMCI patients, both in terms of spatial distribution and frequency. Posterior brain regions decrease synchrony between their high-frequency oscillations and other regions’ activity across all frequencies, while anterior regions increase synchrony between their low-frequency oscillations and other regions’ activity across all frequencies. Thus, posterior regions reduce their relative importance in favor of anterior regions.ConclusionsOur findings provide valuable insights into the intricate changes that occur in functional brain networks during the early stages of AD, demonstrating that considering the interplays between different frequency bands enhances our understanding of AD network dynamics and setting a precedent for the study of functional networks using a multilayer approach.

HAGCN: A hybrid-order brain network-based graph convolution learning framework with multi-head attention for brain disorder classification

Brain networks provide essential insights in diagnosing many brain disorders, such as autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD). However, current works often fail to fully capture the potential interaction characteristics of inter-regional functional connectivity within the brain network. To address this limitation, we propose a Hybrid-Attention Graph Convolution Network (HAGCN) for brain disorder classification. The learning framework designs a hybrid-order effective network (graph) for representing the lower-order and higher-order spatial interaction relations between brain regions, proposes a prior graph based on statistical tests to eliminate the noisy edges effectively, presents a dual-channel graph convolution module for simultaneously capturing information flow features of each brain region in out-degree and in-degree directions, and implement a multi-head attention module to focus and discern the significant features from the multidimensional interaction relations for improving the expressiveness of the model. Experiments on ADHD-200 and ABIDE-I datasets show the effectiveness and advantages of our proposed method, achieving the highest average accuracy values of 77.95 % and 78.17 %, respectively. The results demonstrate that integrating multidimensional and directional interaction characteristics can enhance the network’s capacity and improve learning efficiency. Additionally, our approach identifies and analyzes several key brain regions linked to brain disorders.

Impact of Enterprise Supply Chain Digitalization on Cost of Debt: A Four-Flows Perspective Analysis Using Explainable Machine Learning Methodology

Rising costs, complex supply chain management, and stringent regulations have created significant financial burdens on business sustainability, calling for new and rapid strategies to help enterprises transform. Supply chain digitalization (SCD) has emerged as a promising approach in the context of digitalization and globalization, with the potential to reduce an enterprise’s debt costs. Developing a strategic framework for SCD that effectively reduces the cost of debt (CoD) has become a key academic challenge, critical for ensuring business sustainability. To this end, under the perspective of four flows, SCD is deconstructed into four distinct features: logistics flow digitalization (LFD), product flow digitalization (PFD), information flow digitalization (IFD), and capital flow digitalization (CFD). To precisely measure the four SCD features and the dependent variable, COD, publicly available data from Chinese listed manufacturing enterprises such as annual report texts and financial statement data are collected, and various data mining technologies are also used to conduct data measurement and data processing. To comprehensively investigate the impact pattern of SCD on CoD, we employed the explainable machine learning methodology for data analysis. This methodology involved in-depth data discussions, cross-validation utilizing a series of machine learning models, and the utilization of Shapley additive explanations (SHAP) to explain the results generated by the models. To conduct sensitivity analysis, permutation feature importance (PFI) and partial dependence plots (PDPs) were also incorporated as supplementary explanatory methods, providing additional insights into the model’s explainability. Through the aforementioned research processes, the following findings are obtained: SCD can play a role in reducing CoD, but the effects of different SCD features are not exactly the same. Among the four SCD features, LFD, PFD, and IFD have the potential to significantly reduce CoD, with PFD having the most substantial impact, followed by LFD and IFD. In contrast, CFD has a relatively weak impact, and its role is challenging to discern. These findings provide significant guidance for enterprises in furthering their digitalization and supply chain development, helping them optimize SCD strategies more accurately to reduce CoD.

Recurrence resonance - noise-enhanced dynamics in recurrent neural networks

Understanding how neural networks process information is a fundamental challenge in neuroscience and artificial intelligence. A pivotal question in this context is how external stimuli, particularly noise, influence the dynamics and information flow within these networks. Traditionally, noise is perceived as a hindrance to information processing, introducing randomness and diminishing the fidelity of neural signals. However, distinguishing noise from structured input uncovers a paradoxical insight: under specific conditions, noise can actually enhance information processing. This intriguing possibility prompts a deeper investigation into the nuanced role of noise within neural networks. In specific motifs of three recurrently connected neurons with probabilistic response, the spontaneous information flux, defined as the mutual information between subsequent states, has been shown to increase by adding ongoing white noise of some optimal strength to each of the neurons. However, the precise conditions for and mechanisms of this phenomenon called ‘recurrence resonance’ (RR) remain largely unexplored. Using Boltzmann machines of different sizes and with various types of weight matrices, we show that RR can generally occur when a system has multiple dynamical attractors, but is trapped in one or a few of them. In probabilistic networks, the phenomenon is bound to a suitable observation time scale, as the system could autonomously access its entire attractor landscape even without the help of external noise, given enough time. Yet, even in large systems, where time scales for observing RR in the full network become too long, the resonance can still be detected in small subsets of neurons. Finally, we show that short noise pulses can be used to transfer recurrent neural networks, both probabilistic and deterministic, between their dynamical attractors. Our results are relevant to the fields of reservoir computing and neuroscience, where controlled noise may turn out a key factor for efficient information processing leading to more robust and adaptable systems.

Pedagogical Impacts of Kindergartens’ Response to Covid-19

This study explores the pedagogical impacts of the infection control measures implemented in Norwegian kindergartens after the Covid-19 lockdown in the spring of 2020. The data is based on a survey study with three repeated measures following kindergartens from the first week of reopening in April, continuing until June. Reflexive thematic analysis is used in the analysis of data from answers to open questions. The themes generated were: (1) limited time to prepare, (2) concerns about the unknown, (3) structuring the kindergartens according to the guidelines, (4) securing a safe place for children within the new rules and routines; (5) closer relationships but reduced child democracy, (6) spending time more time outside and in nature, and (7) reduced collaboration and information flow. Framed by a crisis management perspective and the concepts of structural and process quality, we discuss what we can learn from the kindergartens’ experiences in the spring of 2020.

Bounded Rational Decision Networks with Belief Propagation.

Complex information processing systems that are capable of a wide variety of tasks, such as the human brain, are composed of specialized units that collaborate and communicate with each other. An important property of such information processing networks is locality: there is no single global unit controlling the modules, but information is exchanged locally. Here, we consider a decision-theoretic approach to study networks of bounded rational decision makers that are allowed to specialize and communicate with each other. In contrast to previous work that has focused on feedforward communication between decision-making agents, we consider cyclical information processing paths allowing for back-and-forth communication. We adapt message-passing algorithms to suit this purpose, essentially allowing for local information flow between units and thus enabling circular dependency structures. We provide examples that show how repeated communication can increase performance given that each unit's information processing capability is limited and that decision-making systems with too few or too many connections and feedback loops achieve suboptimal utility.

Specifying and Verifying Information Flow Control in SELinux Configurations

Security Enhanced Linux (SELinux) is a security architecture for Linux implementing Mandatory Access Control. It has been used in numerous security-critical contexts ranging from servers to mobile devices. However, its application is challenging as SELinux security policies are difficult to write, understand, and maintain. Recently, the intermediate language CIL was introduced to foster the development of high-level policy languages and to write structured configurations. Despite CIL’s high level features, CIL configurations are hard to understand as different constructs interact in non-trivial ways. Moreover, there is no mechanism to ensure that a given configuration obeys desired information flow policies. To remedy this, we enrich CIL with a formal semantics, and we propose IFCIL, a backward compatible extension of CIL for specifying fine-grained information flow requirements. Using IFCIL, administrators can express confidentiality, integrity, and non-interference properties. We also provide a tool to statically verify these requirements and we experimentally assess it on ten real-world policies.

Optimization method of cable structure demolition driven by digital twin evolution model

The large-span cable structure has the problems of complex component connection, cumbersome demolition process and difficult process coordination, which lead to high construction safety risk and low efficiency. Moreover, the optimization of the construction process scheme is difficult, and the convergence and visualization of the finite element analysis are insufficient, which hinders the construction efficiency. This study proposes a cable structure demolition optimization method driven by the digital twin (DT) evolution model. From the perspective of virtual-real mapping, the DT evolution process of cable structure demolition is clarified. The virtualization method of physical demolition process is formed by integrating visual modeling, simulation modeling and data modeling. Taking full account of the temporal and spatial evolution of the demolition process, a DT evolution model of the demolition process is established. Starting from the information flow, component flow, control flow and their relationship, the problems existing in the process of demolition execution are analyzed. Driven by the twin evolution process, a data mapping method for the demolition process is proposed. The safety risk is characterized by the mechanical properties (MPs) of the structure, and the expression language of the data association relationship is established. The particle swarm optimization back propagation neural network (PSO-BPNN) is improved, and an intelligent analysis method of MPs in the whole process of demolition is proposed. The best construction scheme is obtained by integrating the changes in MPs between the various demolition steps. The information of the demolition evolution process is divided into functional modules, and a DT platform is developed to efficiently guide the actual construction. Taking the demolition process of a cable truss experimental model as an example, the effectiveness of the proposed method is verified. The research results show that the DT evolution model can accurately map the actual demolition process. The deep learning method analyzes the mechanical properties of the structure and obtains the best construction scheme quickly and accurately. The development and application of the twin platform improves the degree of visualization and realizes the efficient guidance of the virtual model to the actual construction.

Agenda Setting Theory in The Age of Digital Media: An Analytical Perspective

This paper explores agenda-setting theory within digital media. It aims to evaluate changes in these paradigms due to digital platforms and their impact on mass communication theories. The discussion includes a historical overview of agenda-setting theory, grounded in foundational works and expanded by contemporary insights on user agency and information dissemination in the digital age. Using qualitative methods, the study incorporates thematic analysis, content analysis, and interviews with media professionals and users to collect comprehensive data. Key findings indicate that the traditional linear flow of information from media to the public has shifted to a multidirectional exchange influenced by user-generated content and social media dynamics. The study emphasizes the need for scholars to adapt traditional media theories to the participatory nature of modern platforms. This research enhances understanding of current media ecosystems and lays the groundwork for future studies on media influence in complex digital environments.

Textual Metafunction in The Choir Traditional Spiritual Song “Elijah Rock” By Moses Hogan

This research explores the textual metafunction of the choir song "Elijah Rock" by Moses Hogan through the lens of Systemic Functional Linguistics (SFL), employing Suzanne Eggins' model of analysis. By examining the song's lyrical structure, the study aims to uncover how thematic organization, cohesion, and information flow contribute to the overall meaning and emotional impact of the piece. The analysis identifies key thematic elements, such as the recurring motif of "Elijah," as topical theme; the use of other topical themes such as: “come on”, “tell”, “comin’ up”, “Satan”; “He”; and “I”; the use of textual theme “if” and “Oh” highlights the extensive use of repetition and parallelism, which enhance textual coherence. Through a qualitative content analysis, the research demonstrates how linguistic features not only create a unified text but also resonate with the cultural and historical significance of African American spirituals. The findings reveal that the interplay between textual structure and musical expression plays a vital role in conveying the song's message and engaging audiences. This study contributes to the growing body of literature on the intersection of linguistics and music, offering valuable insights for future research in both fields.

When two chains meet: Optimizing the design of blockchain-enabled supply chain networks

Applications of the blockchain technology are rapidly emerging across sectors. By redefining information flow, blockchain technology promises to instill trust among supply chain members, improve transaction efficiency, thus reshaping the structure and configuration of supply chains. The practical exploratory use of blockchain has shown immense potential in supply chain management. In order to explore the new changes brought by blockchain technology to supply chain network design issues, this study focuses on the supply chain network design problem in the context of blockchain technology. Specifically, we consider a supply chain network comprised of a number of distribution centers (DCs) and retailers. The operation of this physical network is supported by blockchain-based information infrastructure. As the interrelated relationship between these two networks, it is necessary to optimize them simultaneously to improve the profitability and information exchange level. We propose an objective function based on previous studies to maximize the profits of the entire supply chain network, with decision variables including DC locations, retailer assignment, inventory replenishment policy, and blockchain adoption level. At the same time, to characterize the interrelation between the two networks, the demand and certain cost parameters of the model become endogenous depending on the blockchain adoption level, which significantly increases the complexity of the solution algorithm. We reformulate the model and use the polymatroid cutting plane approach to address this issue. We conducted calculations and numerical analysis. The computational results show that the proposed method can solve practically sized problems, and integrating blockchain design reduces the system-wide cost. We discuss managerial insights based on a range of numerical studies and solve a case study using publicly available instance data.

A Survey on Enterprise Resource Planning and Building Information Modeling Integration: A Construction 4.0 Perspective

Managing the fragmented nature of information generated on construction sites and firms is the need of the hour. Building Information Modeling (BIM) and Enterprise Resource Planning (ERP) are the most prevalent information systems (ISs) used in the construction industry, with varied capabilities but also with significant overlap. It is essential to analyze the current state of Common Data Environments (CDEs) that support both BIM and ERP as separate systems, as well as those that facilitate their integration. This analysis will ultimately help establish a practical framework for implementing this integration, enabling smoother information flow and enhanced utilization in construction projects. This paper presents a bibliometric analysis and provides a global perspective on research developments in the field of construction ERP and its integrations with BIM. It consolidates publication data from various databases (e.g., Scopus and Web of Science) as per geographical spread, authors, and number of publications as per the keyword search. It observes key research themes in each phase of the analysis. This paper concludes that although the existing literature has established the benefits of BIM and ERP integration, research is still in its nascent stage and identifies research gaps that can be taken up for further research.