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Governance and leadership are recognized as central to health system development in low- and middle-income countries, yet few existing studies consider the influence of multilevel governance systems. South Africa is one of the many (quasi-)federal states. Provincial governments have responsibility for managing health care delivery within national policy frameworks and norms. The early post-apartheid period saw country-wide efforts to address the apartheid legacy of health system inequity and inefficiency, but health system transformation subsequently stalled in many provinces. In contrast, the Western Cape provincial health department sustained service delivery reform and strengthened management processes over the period 1994–2016. This department can be considered a 'pocket of relative bureaucratic effectiveness': an organizational entity that, compared to others, is relatively effective in carrying out its functions in pursuit of the public good. This paper considers what factors enabled the development of the Western Cape health system in the period 1994–2016. Two phases of data collection entailed document review, participatory workshops, 43 in-depth interviews with purposively selected key informants from inside and outside the Western Cape and a structured survey testing initial insights (response rate 42%). Analysis included triangulation across data sets, comparison between the Western Cape and other provincial experience and deeper reflection on these experiences drawing on POE theory and public administration literature. The analysis highlights the Western Cape experience of stable and astute sub-national governance and leadership and the deepening of administrative and technical capacity over time—within a specific provincial historical and political economy context that sustained the separation of political and administrative powers. Multilevel governance systems can create the space for sub-national POEs to emerge in their mediation of wider political economy forces, generating spaces for skilled reform leaders to act in the public interest, support the emergence of distributed leadership and develop robust management processes. [ABSTRACT FROM AUTHOR]

Data democratization (DD) is a new concept rapidly becoming a game-changer, enabling companies to innovate and maintain a competitive edge in a data-driven world. This paper explores the evolution of data accessibility, from the early days of manual record-keeping to the sophisticated data management systems of today. The evolution from transactional databases to data warehouses marked a shift toward centralized data management and specialized teams, supporting the standard principles of DD contexts such as data governance (DG), privacy, management, usability, accessibility, and literacy. This paper provides an overview of the evolution of data access, from manual record-keeping to the modern data management systems of today, focusing on the challenges related to data privacy and security, integration of legacy systems, and the cultural shift required to embrace a data-driven mindset. This paper also explores both universal and holistic approaches to DD, assessing the challenges, benefits, and possibilities of their applications. An overview of industry-specific cases is included in the paper to provide practical insights that would contribute to understanding the most effective approach to data democratization. [ABSTRACT FROM AUTHOR]

It has been argued that we have now entered the Anthropocene, a proposed epoch in which humans are having a dominant impact on the Earth system. While some geologists have sought to formalize the Anthropocene as beginning in the mid-twentieth century, its social, geophysical, and environmental roots undoubtedly lie deeper in the past. In this review, we highlight the ways in which human activities across the major biomes of our planet significantly altered parts of the Earth system prior to the Industrial Age. We demonstrate ways in which novel, multidisciplinary approaches can provide detailed insights into long-term human–environment–Earth system interactions. We argue that there is clear evidence for lasting Earth system legacies of pre-Industrial human societies and that archaeology, paleoecology, and historical ecology can provide important, practical insights to help navigate current and future relationships with the planet in more equitable and sustainable ways. [ABSTRACT FROM AUTHOR]

In legacy industrial systems, discovering joinable information between database tables is important for applications such as data integration and data analysis. Locality-Sensitive Hashing-based methods have been proven to be capable of handling chaotic and diverse table relationships, but these methods often rely on an incorrect assumption—that the similarity of table columns in the database directly reflects their joinability, causing problems related to the accuracy of their results. To solve this problem, this study proposes a dynamic data-driven time-slicing Locality-Sensitive Hashing method for joinable table discovery. This method introduces database log information and within different time slices, uses the co-occurrence matrix of data tables to determine their joinability. Specifically, it first performs a MinHash dimensionality reduction on database columns and then uses Locality-Sensitive Hashing to calculate the static similarity. Next, it identifies business modular time slices through database logs, calculates the dynamic similarity of the slice time data, and builds a co-occurrence matrix between tables. Finally, the joinability between data tables is calculated using the static similarity, dynamic similarity, and co-occurrence matrix. The experimental results demonstrate that this method effectively excludes tables that only have similarity but no business relationship for data cleaning, and its accuracy exceeds that of methods that only depend on similarity. [ABSTRACT FROM AUTHOR]

Space technology is integral to modern critical systems, including navigation, communication, weather, financial services, and defence. Despite its significance, space infrastructure faces unique cyber resilience challenges exacerbated by the size, isolation, cost, persistence of legacy systems, and lack of comprehensive cyber resilience engineering standards. This paper examines the engineering challenges associated with incorporating cyber resilience into space design, drawing on insights and experiences from industry experts. Through qualitative interviews with engineers, cybersecurity specialists, project managers, and testers, we identified key themes in engineering methodologies, cybersecurity awareness, and the challenges of integrating cyber resilience into space projects. Participants emphasised the importance of incorporating cybersecurity considerations from the earliest stages of design, advocating for principles such as zero-trust architecture and security by design. Our findings reveal that experts favour Model-Based Systems Engineering (MBSE) and Agile methodologies, highlighting their synergy in developing flexible and resilient systems. The study also underscores the tension between principles-based standards, which offer flexibility but can lead to inconsistent implementation, and compliance-based approaches, which provide clear measures but may struggle to adapt to evolving threats. Additionally, the research recognises significant barriers to achieving cyber resilience, including insider threats, the complexity of testing and validation, and budget constraints. Effective stakeholder engagement and innovative funding models are crucial for fostering a culture of cybersecurity awareness and investment in necessary technologies. This study highlights the need for a comprehensive cyber resilience framework that integrates diverse engineering methodologies and proactive security measures, ensuring the resilience of space infrastructure against emerging cyber threats. [ABSTRACT FROM AUTHOR]

The long-term interaction between the environment and humans has significantly shaped contemporary landscapes worldwide. As a heritage landscape, the polder is essential for understanding the impact of the past human activities on contemporary landscape on low-lying area of land. In this case study, we reconstructed the regional spatial distribution of the polders in the early twentieth century and examined their coherence over the past century using historical maps and other materials in the Jianghan Plain. The results revealed several key findings: (1) In the early twentieth century, there were 1571 polders covering an area of 11,826.1 km2 with a perimeter of 17,035.9 km. (2) These polders exhibited a J-shaped distribution, with few large-scale polders despite their substantial areas. (3) The shape of these polders reflected an ability to balance human needs and the natural environment. (4) Considerable regional differences in these polders were attributed to centuries-old interactions between humans and the environment. (5) The remnants of these early twentieth century polders have had a lasting influence on the contemporary landscape, particularly in rural areas. This case study underscores how taking into account a broader historical perspective can enhance our understanding of present-day landscapes and, more importantly, the role that past human actions have played in shaping them. [ABSTRACT FROM AUTHOR]

Considering Industry 4.0 directions, followed by recent Industry 5.0 principles, interest in integrating legacy systems in industrial manufacturing has emerged. Due to the continuous evolution of the Internet of Things (IoT) and the Industrial Internet of Things (IIoT), as well as the rapid extension of the scope and adoption of broader technologies, such integration has become feasible. Even though newly developed equipment provides easier interoperability, the replacement of legacy systems highly impacts cost and sustainability, which usually extends to the entire production process, the operators and the maintenance team, and sometimes even the robustness of the production process. Ensuring the interoperability of legacy systems is a problematic task, being dependent on technologies and development techniques and specific industrial domain particularities. This paper considers strategies to ensure the interoperability of legacy systems in a building-management system scenario where local structures are approached using both industrial protocols and web-based contexts. The solution is built following the Industry 5.0 pillars (sustainability, human focus, resilience) and conceives the entire data acquisition and supervisory solution to be flexible, open-source, resilient, and under the control of company engineers. The chosen environment for interfacing and supervision is Node-RED, enabling IoT and IIoT tools, together with a complete orientation toward digital transformation. This way, it is possible to construct a final result that enhances security while bridging outdated protocols and technologies, eliminating compatibility risks in the context of the evolutionary IIoT, ensuring critical process functions are possible, and aiding operators in complying with regulations governing building-management system (BMS) operations, thus solving the challenges that arise in the complex task of adopting the IoT backbone of digital transformation in relation to the integration of legacy equipment. The obtained solution is tested in an automotive industry building-management system, and the results demonstrate its performance, reliability, and high customizability in a context of openness and low cost. [ABSTRACT FROM AUTHOR]

Research into blockchain technology has expanded significantly due to its potential to enhance transparency and trust in business operations, yet adoption is limited by technical challenges like scalability, consensus mechanisms, and security, as well as non-technical barriers such as governance and standardization. This study synthesizes findings from 21 systematic literature reviews (SLR) published between January 2009 and January 2024, focusing on the technical performance of blockchain and its integration with technologies like cloud computing and machine learning. The analysis highlights advancements in scalability, security, privacy, and smart contracts alongside areas needing more research, such as interoperability and integration with legacy systems. This comprehensive review aims to map current technological developments, identify ongoing limitations, and identify specific technical challenges that impede broader adoption. The findings direct future research towards overcoming these barriers, facilitating the smoother implementation of blockchain across various business sectors. [ABSTRACT FROM AUTHOR]

Source code complexity of legacy object-oriented (OO) software has a trickle-down effect over the key activities of software development and maintenance. Package-based OO design is widely believed to be an effective modularization. Recently, theories and methodologies have been proposed to assess the complementary aspects of legacy OO systems through package-modularization metrics. These package-modularization metrics basically address non-API-based object-oriented principles, like encapsulation, commonality-of-goal, changeability, maintainability, and analyzability. Despite their ability to characterize package organization, their application towards cost-effective fault-proneness prediction is yet to be determined. In this paper, we present theoretical illustration and empirical perspective of non-API-based package-modularization metrics towards effort-aware fault-proneness prediction. First, we employ correlation analysis to evaluate the relationship between faults and package-level metrics. Second, we use multivariate logistic regression with effort-aware performance indicators (ranking and classification) to investigate the practical application of proposed metrics. Our experimental analysis over open-source Java software systems provides statistical evidence for fault-proneness prediction and relatively better explanatory power than traditional metrics. Consequently, these results guide developers for reliable and modular package-based software design. [ABSTRACT FROM AUTHOR]

This research paper explores the transformative impact of Artificial Intelligence (AI) and Machine Learning (ML) on financial markets and services. The rapid evolution of AI and ML technologies has reshaped how financial institutions operate, enabling enhanced decision-making processes, risk management, and customer engagement. The paper examines the application of AI and ML in various sectors of finance, including algorithmic trading, fraud detection, credit scoring, and personalized financial services. By analyzing recent advancements, the research highlights how AI-driven predictive analytics and automation streamline operations, reduce costs, and improve accuracy in financial forecasting. Furthermore, the paper discusses the implications of AI and ML on regulatory compliance and risk assessment, emphasizing the need for a robust framework to address ethical considerations and data privacy concerns. It also addresses the challenges faced by financial institutions in adopting these technologies, such as integration with legacy systems, the requirement for skilled personnel, and the evolving regulatory landscape. Ultimately, this paper provides a comprehensive overview of the dual role of AI and ML in enhancing operational efficiency while also posing challenges that require strategic management. The findings suggest that as financial markets continue to evolve, the integration of AI and ML will not only redefine service delivery but also establish new paradigms in financial practices, paving the way for future innovations. By fostering a deeper understanding of these technologies, stakeholders can better navigate the complexities of modern finance, driving growth and sustainability in an increasingly digital economy. [ABSTRACT FROM AUTHOR]

Blockchain technology has emerged as a transformative innovation in the financial sector, offering enhanced security, transparency, and operational efficiency. This research paper reviews the various use cases of blockchain technology in finance, including cryptocurrencies, cross-border payments, smart contracts, supply chain finance, central bank digital currencies, and regulatory compliance. Utilizing a qualitative research design, data was gathered from industry reports and peer-reviewed academic papers, and analyzed using content analysis facilitated by NVivo software. The key findings reveal that blockchain significantly improves transaction efficiency and reduces costs. However, major challenges such as regulatory uncertainty, scalability issues, data privacy concerns, and integration with legacy systems hinder widespread adoption. The study underscores the need for clear regulatory frameworks, technological advancements, and strategic collaboration among stakeholders. The broader implications suggest that addressing these challenges is crucial for harnessing the full potential of blockchain technology to revolutionize financial services. [ABSTRACT FROM AUTHOR]

The agrifood system faces a great many economic, social and environmental challenges. One of the biggest practical challenges has been to achieve greater data sharing throughout the agrifood system and the supply chain, both to inform other stakeholders about a product and equally to incentivise greater environmental sustainability. In this paper, a data sharing architecture is described built on three principles (a) reuse of existing semantic standards; (b) integration with legacy systems; and (c) a distributed architecture where stakeholders control access to their own data. The system has been developed based on the requirements of commercial users and is designed to allow queries across a federated network of agrifood stakeholders. The Ploutos semantic model is built on an integration of existing ontologies. The Ploutos architecture is built on a discovery directory and interoperability enablers, which use graph query patterns to traverse the network and collect the requisite data to be shared. The system is exemplified in the context of a pilot involving commercial stakeholders in the processed fruit sector. The data sharing approach is highly extensible with considerable potential for capturing sustainability related data. [ABSTRACT FROM AUTHOR]

This research investigates the synergistic relationship between Lean Manufacturing and Industry 4.0, crucial paradigms in modern industry, through a literature review of 60 scientific articles published from January 2013 to November 2023. The study reveals a tripartite relationship: Industry 4.0 technologies amplify Lean Manufacturing's efficiency; Lean Manufacturing principles facilitate Industry 4.0 adoption; and their interaction fosters mutual enhancement, indicating a dynamic interplay. The results show three main trends: combining Industry 4.0 technologies, especially the Internet of Things, with Lean Manufacturing principles to make things more efficient and productive; highlighting the important role of Lean Manufacturing and Industry 4.0 in tackling sustainability issues and encouraging eco-friendly practices; and combining Lean principles with Industry 4.0 as a step toward Industry 5.0. Challenges include the need for a cultural transformation to align Lean's simplicity with Industry 4.0's complexity, resolve compatibility issues between legacy systems and new technologies, enhance data management and cybersecurity, address skill deficiencies through staff training, and ensure supplier and partner alignment. This research enriches academic and industrial discourse by presenting a novel outlook on the convergence of lean manufacturing and Industry 4.0, highlighting its significance for future works. [ABSTRACT FROM AUTHOR]

Customer relationship management (CRM) is a strategic approach to manage an organization's interaction with current and potential customers. Artificial Intelligence (AI) can analyze huge volume of data without human intervention. The integration of AI with existing legacy CRM system in the business to customer (B2C) relationship makes sense given the massive potential for growth of AI integrated CRM system. Failure to plan AI-CRM technology implementation in an organization could lead some to success and others to failure. The Contingency theory states that it is not possible for organizations to take decisions without a contingency plan and the optimal course of action depends on the internal and external circumstances. The Dynamic Capability View theory emphasizes the organizational ability to react adequately in a timely manner to any external changes and combines multiple capabilities of the organization, including organizational CRM and AI capabilities. Against this background, the purpose of this study is to examine the success and failure of implementation of AI integrated CRM system in an organization from B2C perspective using Contingency theory and Dynamic Capability View theory. The study finds that information quality, system fit, and organizational fit significantly and positively impact the implementation of AI-CRM for B2C relationship management. Also, there is a moderating impact of technology turbulence on both acceptance and failure of AI-CRM capability in the organization. [ABSTRACT FROM AUTHOR]

As a new software design paradigm, microservices structure an application as a collection of services that are independently deployable and loosely coupled. A key step of migrating non‐microservices‐based systems to microservices‐based systems is the identification of microservices in the target application. Traditional approaches to identify microservices, however, usually suffer from lack of full automation and low effectiveness. This paper puts forward an expert system to identify microservices automatically from legacy systems by leveraging the similarity of RESTful APIs. The system consists of three major parts. The first part calculates the candidate topic similarity and the response message similarity of APIs, and the overall similarity is obtained through their combination. Afterwards, the second part constructs a graph of API similarities with API as the node and the overall similarity as the weight. The third part employs a graph‐based clustering algorithm to identify candidate microservices from the API similarity graph. Experiments conducted on open‐source projects demonstrate the effectiveness of our system. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) and blockchain technology have emerged as increasingly prevalent and influential elements shaping global trends in Information and Communications Technology (ICT). Namely, the synergistic combination of blockchain and AI introduces beneficial, unique features with the potential to enhance the performance and efficiency of existing ICT systems. However, presently, the confluence of these two disruptive technologies remains in a rather nascent stage, undergoing continuous exploration and study. In this context, the work at hand offers insight regarding the most significant features of the AI and blockchain intersection. Sixteen outstanding, recent articles exploring the combination of AI and blockchain technology have been systematically selected and thoroughly investigated. From them, fourteen key features have been extracted, including data security and privacy, data encryption, data sharing, decentralized intelligent systems, efficiency, automated decision systems, collective decision making, scalability, system security, transparency, sustainability, device cooperation, and mining hardware design. Moreover, drawing upon the related literature stemming from major digital databases, we constructed a timeline of this technological convergence comprising three eras: emerging, convergence, and application. For the convergence era, we categorized the pertinent features into three primary groups: data manipulation, potential applicability to legacy systems, and hardware issues. For the application era, we elaborate on the impact of this technology fusion from the perspective of five distinct focus areas, from Internet of Things applications and cybersecurity, to finance, energy, and smart cities. This multifaceted, but succinct analysis is instrumental in delineating the timeline of AI and blockchain convergence and pinpointing the unique characteristics inherent in their integration. The paper culminates by highlighting the prevailing challenges and unresolved questions in blockchain and AI-based systems, thereby charting potential avenues for future scholarly inquiry. [ABSTRACT FROM AUTHOR]

The increasing global population and reliance on electrical devices for daily life resulted in sharply rising energy consumption. Also, this leads to higher household electricity bills. As a result, there is a growing demand for energy monitoring systems that can accurately estimate energy usage to help save power, especially for older home appliances that are difficult or expensive to update with monitoring sensors. However, current energy monitoring systems have some drawbacks, such as the inability to detect different types of appliances and the deployment complexity. Moreover, such systems are too costly to use in older power infrastructures. To address this issue, we proposed a centralized smart energy monitoring system designed for legacy home appliances, aiming to address the limitations of current energy monitoring systems by avoiding costly infrastructure upgrades to calculate the power consumption of legacy home appliances. The proposed system employs a two-layered architecture comprising hardware (Emontx device, Analog-to-Digital Converters (ADC), and Current Transformer (CT) sensors) and a software layer that includes Artificial Intelligence (AI) predictors using a pre-defined set of rules and K Nearest Neighbours (KNN) algorithms. We conducted three experiments on real home appliances to evaluate the proposed work. The accuracy of the proposed system showed positive results after several modifications and hard tuning of several parameters in devices, specifically for Jordanian power plants. [ABSTRACT FROM AUTHOR]

The Work System Theory (WST) is a foundation theory that enables analysis of systems in organizations. It encompasses a set of concepts that help describing, analyzing, designing and evaluating purposeful systems that perform work. A WST-based method guides a work system's analysis through the identification of problems/opportunities, summarizing the As-Is and To-Be versions of a system. Motivated by a Design Science project running in an academic institution, we explore in this paper the application of graph-based semantic technologies to specify and analyze work systems and to bridge their design-time view with run-time data found in legacy systems. Our contribution is twofold (1) we propose an ontological schema that informs RDF-based knowledge graph building with a Work System perspective; (2) we demonstrate some benefits of having work systems represented as Knowledge Graphs that are linked to operational data and further subjected to semantic queries and deductive reasoning. The Design Science artifact is iteratively developed in the host institution of the first authors and builds on previous development of a knowledge graph that has been lifted from legacy databases. The graph-based approach is viable to bridge an inherent conceptual gap between the work systems conceptualization and operational data schemas, thus adding value both to decision-making and to run-time systems that will be later built to benefit from the semantic distinctions present in the resulting graph. [ABSTRACT FROM AUTHOR]

Due to increasing urbanization, nowadays, cities are facing challenges spanning multiple domains such as mobility, energy, environment, etc. For example, to reduce traffic congestion, energy consumption, and excessive pollution, big data gathered from legacy systems (e.g., sensors not conformant with modern standards), geographic information systems, gateways of public administrations, and Internet of Things technologies can be exploited to provide insights to assess the current status of a city. Moreover, the possibility to perform what-if analyses is fundamental to analyzing the impact of possible changes in the urban environment. The few available solutions for scenario definitions and analyses are limited to addressing a single domain and providing proprietary formats and tools, with scarce flexibility. Therefore, in this paper, we present a novel scenario model and editor integrated into the open-source Snap4City.org platform to enable several processing and what-if analyses in multiple domains. Different from state-of-the-art software, the proposed solution responds to a series of identified requirements, implements NGSIv2-compliant data models with formal descriptions of the urban context, and a scenario versioning method. Moreover, it allows us to carry out analyses on different domains, as shown with some examples. As a case study, a traffic congestion analysis is provided, confirming the validity and usefulness of the proposed solution. This work was developed in the context of CN MOST, the National Center on Sustainable Mobility in Italy, and for the Tourismo EC project. [ABSTRACT FROM AUTHOR]

Aged information systems are commonly referred to as legacy information systems or just merely as legacy systems. Typically, these are mission-critical systems developed years ago that significantly resist evolution. Many organizations have lived with these systems for a long time and consider them a burden because they hold back their businesses and cause unreasonably high evolution and operating costs. However, they also cannot easily be discarded and replaced by modern solutions. Despite their relevance, no universally accepted concept exists that explains these systems or the mechanisms behind them. Accordingly, the properties and challenges associated with legacy systems vary significantly depending on their respective author and intentions. This needs to be improved, and this is also where our research comes in. To this end, we first describe the causes of information systems’ aging, typical symptoms by which legacy systems are often recognized, and the consequences of using outdated solutions. Based on this empirical point of view, we introduce a holistic model to explain legacy systems objectively as well as the concept of business-technological age to distinguish between chronological age and actual obsolescence of a deployed system. This approach provides practitioners and researchers with a foundation for stringently explaining the hitherto fuzzy legacy phenomenon, promoting a common understanding of legacy systems, and simplifying communication by employing specific concepts. Practitioners can use this approach to better understand their stock application systems in terms of aging and improve their decision-making regarding evolution.

6G will connect heterogeneous intelligent agents to make them natively operate complex cooperative tasks. When connecting intelligence, two main research questions arise to identify how artificial intelligence and machine learning models behave depending on (i) their input data quality, affected by errors induced by interference and additive noise during wireless communication; (ii) their contextual effectiveness and resilience to interpret and exploit the meaning behind the data. Both questions are within the realm of semantic and goal-oriented communications. With this paper, we investigate how to effectively share communication spectrum resources between a legacy communication system (i.e., data-oriented) and a new goal-oriented edge intelligence one. Specifically, we address the scenario of an enhanced Mobile Broadband (eMBB) service, i.e., a user uploading a video stream to a radio access point, interfering with an edge inference system, in which a user uploads images to a Mobile Edge Host that runs a classification task. Our objective is to achieve, through cooperation, the highest eMBB service data rate, subject to a targeted goal effectiveness of the edge inference service, namely the probability of confident inference on time. We first formalize a general definition of a goal in the context of wireless communications. This includes the goal effectiveness, (i.e., the goal achievability rate, or the probability of achieving the goal), as well as goal cost (i.e., the network resource consumption needed to achieve the goal with target effectiveness). We argue and show, through numerical evaluations, that communication reliability and goal effectiveness are not straightforwardly linked. Then, after a performance evaluation aiming to clarify the difference between communication performance and goal effectiveness, a long-term optimization problem is formulated and solved via Lyapunov stochastic network optimization tools to guarantee the desired target performance. Finally, our numerical results assess the advantages of the proposed optimization and the superiority of the goal-oriented strategy against baseline 5G-compliant legacy approaches, under both stationary and non-stationary communication (and computation) environments. [ABSTRACT FROM AUTHOR]

The South African Basic Education System does little to provide skills, needed to survive outside the schooling system, for those who do not wish to pursue post-secondary school education. The education system produces secondary school graduates who are poised, as content carriers and an un-employable labour force. A research question grappled with in this paper is whether this secondary school curriculum design inherited from the colonial formal education system is beneficial for South African learners? The paper argues that the continuation of the provision of this curriculum is tantamount to contributing very little to the national educational strategic goals of creating a skilled workforce needed to run the South African economy. Additionally, this also contributes to the exacerbation of the unemployment in South Africa. From a decolonial point of view, the paper provides a comprehensive overview of the basic education curriculum from the colonial period until the contemporary period and shows how it has contributed to huge numbers of jobseekers visa-versa employees and subservient of the curriculum design and schooling system. Methodologically, this paper is informed by a qualitative research approach in the form of document review. The research revealed four major important elements in decolonization of the school curriculum namely; history lesson, social justice and self-determination, formulation and execution of the protection of indigenous knowledge systems and promoting the significance of indigenous languages and use. [ABSTRACT FROM AUTHOR]

Background: In software engineering, each software product has a life cycle that at some point results in a decision being made with regard to extending its maintenance or upgrading the system to a new platform and architecture via a re-engineering or migration process. However, sometimes this decision is a non-starter; the technical dept accumulates, and platforms cease to exist, meaning that there will always be a time when extending the life support of a legacy system is no longer simply an option, and the service must be modernized. Aim: In this paper, we focus on the migration processes, where a legacy system is updated to a microservice architecture, to understand the current state-of-the-art, applied industry practices and potential pitfalls or research gaps in the topic domain. The study aims to explore previous research to find related trends and expose gaps in the literature. Method: We conducted a systematic mapping study on the research trends within the topic of redesign and re-engineering projects related to microservice architectures to understand what we know about microservices, what the current research trends in the area are, and if possible, what the common nominators for successful migration processes are. Results: Our observations reveal that most microservice migration research is confined to journal articles and conference proceedings. However, a severe fragmentation in publication venues exists within the field. Furthermore, the focus of the research field is primarily on the transformation phase of the re-engineering process, with the majority of the contributions being managerial in nature, particularly of the process type. Additionally, over 50% of the research conducted is empirical in nature. Conclusion: Based on the results, microservice migration research is maturing well; most of the research is empirical. The research field is scattered. There are notable technical, managerial, and organizational challenges and differing motivations. To better understand the motivations and challenges of the practitioners, we are going to conduct survey and interview studies within this field. [ABSTRACT FROM AUTHOR]

This article discusses the potential benefits of inhibiting the renin-angiotensin system (RAS) in preventing hypertension. Hypertension is a global health problem that increases the risk of cardiovascular diseases. The RAS plays a crucial role in the development of hypertension, and pharmacological inhibition of the RAS can lower blood pressure. However, long-term treatment with RAS inhibitors can lead to side effects and poor compliance. The article explores the epigenetic mechanisms behind the long-term effects of RAS inhibition and identifies potential gene targets for future treatments. While animal studies have shown promising results, clinical trials have not yet confirmed the same effects in humans. Further research is needed to fully understand and utilize the potential of RAS inhibition in preventing hypertension. [Extracted from the article]

With a continuous stream of emerging technologies, organizations are increasingly seeking to digitally transform their business towards ever efficient, intelligent, and self-learning business models. As the recognized head of IT, the Chief Information Officer (CIO) is often tasked with the responsibility of successfully implementing Digital Transformation (DT) initiatives. While there are independent studies on CIO role, and on the impact of DT, the role of CIO is not sufficiently examined in the DT context. This study fills this gap by conducting multiple case study of six organizations engaged in DT. Following the role theory, the study emphasizes the CIO as an actor focused on digital strategy and empirically delineates the multidimensional task demands associated with DT regarding the CIO role under the new strategic realities of the digital age. We find that while traditional CIO roles still apply, the DT context adds more nuances in such roles. Moreover, some new roles (e.g., legacy systems integration, risk management and IT security) have become crucial that are not adequately captured in extant literature on CIO roles. [ABSTRACT FROM AUTHOR]

Despite the serious threat of global warming caused by carbon emissions, commercial refrigeration systems that generate cooling are unable to adjust their usage time. Therefore, it is essential to enhance the energy efficiency of refrigeration systems themselves. Recently developed refrigeration systems offer improved energy consumption efficiency as they utilize inverter-type outdoor units that can communicate with indoor units. However, traditional legacy refrigeration systems still suffer from poor energy efficiency because the existing indoor units cannot communicate with inverter-type outdoor units. Hence, this paper introduces a heuristic energy efficiency improvement algorithm for commercial refrigeration systems using legacy indoor units when an inverter-type outdoor unit is installed. In particular, to reduce the computational complexity of the process of selecting the optimal temperature difference and target low-pressure variables that can enhance energy efficiency, the algorithm employs the Monte Carlo method. Finally, the performance of the proposed heuristic energy efficiency improvement algorithm was evaluated in a laboratory environment to confirm its applicability. [ABSTRACT FROM AUTHOR]

Background: Investment in the implementation of hospital ePrescribing systems has been a priority in many economically-developed countries in order to modernise the delivery of healthcare. However, maximum gains in the safety, quality and efficiency of care are unlikely to be fully realised unless ePrescribing systems are further optimised in a local context. Typical barriers to optimal use are often encountered in relation to a lack of systemic capacity and preparedness to meet various levels of interoperability requirements, including at the data, systems and services levels. This lack of systemic interoperability may in turn limit the opportunities and benefits potentially arising from implementing novel digital heath systems. Methods: We undertook n = 54 qualitative interviews with key stakeholders at nine digitally advanced hospital sites across the UK, US, Norway and the Netherlands. We included hospitals featuring 'standalone, best of breed' systems, which were interfaced locally, and multi-component and integrated electronic health record enterprise systems. We analysed the data inductively, looking at strategies and constraints for ePrescribing interoperability within and beyond hospital systems. Results: Our thematic analysis identified 4 main drivers for increasing ePrescribing systems interoperability: (1) improving patient safety (2) improving integration & continuity of care (3) optimising care pathways and providing tailored decision support to meet local and contextualised care priorities and (4) to enable full patient care services interoperability in a variety of settings and contexts. These 4 interoperability dimensions were not always pursued equally at each implementation site, and these were often dependent on the specific national, policy, organisational or technical contexts of the ePrescribing implementations. Safety and efficiency objectives drove optimisation targeted at infrastructure and governance at all levels. Constraints to interoperability came from factors such as legacy systems, but barriers to interoperability of processes came from system capability, hospital policy and staff culture. Conclusions: Achieving interoperability is key in making ePrescribing systems both safe and useable. Data resources exist at macro, meso and micro levels, as do the governance interventions necessary to achieve system interoperability. Strategic objectives, most notably improved safety, often motivated hospitals to push for evolution across the entire data architecture of which they formed a part. However, hospitals negotiated this terrain with varying degrees of centralised coordination. Hospitals were heavily reliant on staff buy-in to ensure that systems interoperability was built upon to achieve effective data sharing and use. Positive outcomes were founded on a culture of agreement about the usefulness of access by stakeholders, including prescribers, policymakers, vendors and lab technicians, which was reflected in an alignment of governance goals with system design. [ABSTRACT FROM AUTHOR]

Background: Electronic health record (EHR) system transitions are challenging for healthcare organizations. High-volume, safety–critical tasks like barcode medication administration (BCMA) should be evaluated, yet standards for ensuring safety during transition have not been established. Objective: Identify risks in common and problem-prone medication tasks to inform safe transition between BCMA systems and establish benchmarks for future system changes. Design: Staff nurses completed simulation-based usability testing in the legacy system (R1) and new system pre- (R2) and post-go-live (R3). Tasks included (1) Hold/Administer, (2) IV Fluids, (3) PRN Pain, (4) Insulin, (5) Downtime/PRN, and (6) Messaging. Audiovisual recordings of task performance were systematically analyzed for time, navigation, and errors. The System Usability Scale measured perceived usability and satisfaction. Post-simulation interviews captured nurses' qualitative comments and perceptions of the systems. Participants: Fifteen staff nurses completed 2–3-h simulation sessions. Eleven completed both R1 and R2, and seven completed all three rounds. Clinical experience ranged from novice (< 1 year) to experienced (> 10 years). Practice settings included adult and pediatric patient populations in ICU, stepdown, and acute care departments. Main Measures: Task completion rates/times, safety and non-safety-related use errors (interaction difficulties), and user satisfaction. Key Results: Overall success rates remained relatively stable in all tasks except two: IV Fluids task success increased substantially (R1: 17%, R2: 54%, R3: 100%) and Downtime/PRN task success decreased (R1: 92%, R2: 64%, R3: 22%). Among the seven nurses who completed all rounds, overall safety-related errors decreased 53% from R1 to R3 and 50% from R2 to R3, and average task times for successfully completed tasks decreased 22% from R1 to R3 and 38% from R2 to R3. Conclusions: Usability testing is a reasonable approach to compare different BCMA tasks to anticipate transition problems and establish benchmarks with which to monitor and evaluate system changes going forward. [ABSTRACT FROM AUTHOR]

Information and Communication Technologies are driving the improvement of industrial processes. According to the Industry 4.0 (I4.0) paradigm, digital systems provide real-time information to humans and machines, increasing flexibility and efficiency in production environments. Based on the I4.0 Design Principles concept, Virtual Assistants can play a vital role in processing production data and offer contextualized and real-time information to the workers in the production environment. This systematic review paper explored Virtual Assistant applications in the context of I4.0, discussing the Technical Assistance Design Principle and identifying the characteristics, services, and limitations regarding Virtual Assistant use in the production environments. The results showed that Virtual Assistants offer Physical and Virtual Assistance. Virtual Assistance provides real-time contextualized information mainly for support, while Physical Assistance is oriented toward task execution. Regarding services, the applications include integration with legacy systems and static information treatment. The limitations of the applications incorporate concerns about information security and adapting to noisy and unstable environments. It is possible to assume that the terminology of Virtual Assistants is not standardized and is mentioned as chatbots, robots, and others. Besides the worthy insights of this research, the small number of resulting papers did not allow for generalizations. Future research should focus on broadening the search scope to provide more-significant conclusions and research possibilities with new AI models and services, including the emergent Industry 5.0 concept. [ABSTRACT FROM AUTHOR]

Integrating third-party and legacy systems has become a critical necessity for companies, driven by the need to exchange information with various entities such as banks, suppliers, customers, and partners. Ensuring data integrity, keeping integrations up-to-date, reducing transaction risks, and preventing data loss are all vital aspects of this complex task. Achieving success in this endeavor, which involves both technological and business challenges, necessitates the implementation of a well-suited architecture. This article introduces an architecture known as the Dynamic Canonical Data Model through Agnostic Messages. The proposal addresses the integration of loosely coupled software units, mainly when dealing with internal and external data integration. To illustrate the architecture's components, a case study from the Mexican Logistics Company Paquetexpress is presented. This organization manages integrations across several platforms, including SalesForce and Oracle ERP, with clients like Amazon, Mercado Libre, Grainger, and Afull. Each of these incurs costs ranging from USD 30,000 to USD 36,000, with consultants from firms such as Quanam, K&F, TSOL, and TekSi playing a crucial role in their execution. This consumes much time, making maintenance costs considerably high when clients request data transmission or type changes, particularly when utilizing tools like Oracle Integration Cloud (OIC) or Oracle Service Bus (OSB). The article provides insights into the architecture's design and implementation in a real-world scenario within the delivery company. The proposed architecture significantly reduces integration and maintenance times and costs while maximizing scalability and encouraging the reuse of components. The source code for this implementation has been registered in the National Registry of Copyrights in Mexico. [ABSTRACT FROM AUTHOR]

Background: In software engineering, each software product has a life cycle that at some point results in a decision being made with regard to extending its maintenance or upgrading the system to a new platform and architecture via a re-engineering or migration process. However, sometimes this decision is a non-starter; the technical dept accumulates, and platforms cease to exist, meaning that there will always be a time when extending the life support of a legacy system is no longer simply an option, and the service must be modernized. Aim: In this paper, we focus on the migration processes, where a legacy system is updated to a microservice architecture, to understand the current state-of-the-art, applied industry practices and potential pitfalls or research gaps in the topic domain. The study aims to explore previous research to find related trends and expose gaps in the literature. Method: We conducted a systematic mapping study on the research trends within the topic of redesign and re-engineering projects related to microservice architectures to understand what we know about microservices, what the current research trends in the area are, and if possible, what the common nominators for successful migration processes are. Results: Our observations reveal that most microservice migration research is confined to journal articles and conference proceedings. However, a severe fragmentation in publication venues exists within the field. Furthermore, the focus of the research field is primarily on the transformation phase of the re-engineering process, with the majority of the contributions being managerial in nature, particularly of the process type. Additionally, over 50 % of the research conducted is empirical in nature. Conclusion: Based on the results, microservice migration research is maturing well; most of the research is empirical. The research field is scattered. There are notable technical, managerial, and organizational challenges and differing motivations. To better understand the motivations and challenges of the practitioners, we are going to conduct survey and interview studies within this field.

The use of blockchain technology is expanding in various industries, including finance, supply chain management, food, energy, IoT, and healthcare. The article aims to address the challenges of complex medical procedures, large-scale medical data management, and cost optimization in the healthcare industry. By employing blockchain technology, the article aims to enhance data security and privacy while ensuring the integrity and efficiency of the healthcare system. This article focuses on the application of blockchain technology in the healthcare system by reviewing the existing literature and proposing multiple workflows for better data management. These workflows were implemented using the Ethereum blockchain platform and involve complex medical procedures such as surgery and clinical trials, as well as managing a large amount of medical data. The feasibility of the proposed system is analyzed in terms of associated costs, and a model-driven engineering approach is used to recover the architecture of traditional healthcare systems. The aim is to provide stakeholders in the healthcare system with better healthcare services and cost optimization. The solution being proposed automates interactions between different parties involved. Smart contracts were created using Solidity language, and their functions were tested using the Remix IDE. This paper illustrates that our smart contract code was designed to avoid common security vulnerabilities and attacks. To test the framework, a prototype of the smart contract was deployed on an Ethereum TESTNET blockchain in a Windows environment. This study found that the proposed approach is both practical and efficient. [ABSTRACT FROM AUTHOR]

The industry is in continuous evolution in the context of the Industrial Internet of Things (IIoT) and Industry 4.0 requirements and expected benefits. Some sectors allow higher reconfiguration dynamics considering the interference capabilities and process/equipment renewals, but others have considerable inertia that is many times justified. In most encountered situations, the reality confirms that the industry is struggling with new demands such as interoperation and efficiency improvements. The water industry makes no difference, being a sector with critical infrastructures and highly varied subsystems, where invasive interference in legacy solutions tends to be avoided. Following previous successful footsteps in researching a proactive decentralized historian, the current work focuses on a case study that refers to a water treatment and distribution facility that has been operated for several years, and the current operating regime was established by local operators following accumulated observations, restrictions, and response strategies. The proactive historian was tailored for the current case study, and it was applied and tested in the suboptimal functioning scenario where the water sources configuration was manually selected and used for water availability and energy efficiency, but without assuming current/future failures or different water demands. The proposed low-cost historian targeted to improve the functioning and operation of the water facility considering energy efficiency and other impacting outcomes of the current strategy and to establish an automatic functioning regime in a completely non-invasive manner towards the local legacy solution. The results were satisfactory, proving that the historian is able to adapt to a particular and suboptimal functioning real industrial scenario, establish recipes in a process-aware manner, and interoperate with the local legacy solution in order to apply improving actions. [ABSTRACT FROM AUTHOR]

The rehabilitation of legacy mines continues to be a big challenge because of the difficulties in returning them to safe and stable conditions and ensuring that the mined-out areas become productive to support the economic activity of the host community. Previous efforts are often focused on purely technical and environmental aspects, leading to resistance from the local community due to their exclusion from the rehabilitation process. To address the issues associated with legacy mines and lack of participation of the community, we have developed a project, Biodiversity Positive Mining For The Net Zero Challenge (Bio + Mine), focusing on the abandoned Sto. Niño copper mine (Benguet, Philippines). The mine was closed in 1982 without a plan involving local stakeholders and leaving a significant ongoing negative legacy. Using the social-ecological-technological system framework, we will explore the intersections of the structure and functions of socio-economicdemographic, ecological, and technological data useful in devising a more inclusive mitigation strategy for the reconstruction of the supporting ecosystem. We aim to develop a site-specific system, underpinned by the local community's knowledge and practices, that can be a model for wider implementation in other legacy and active mines worldwide. [ABSTRACT FROM AUTHOR]

Due to the characteristics of global coverage, on-demand access, and large capacity, the low earth orbit (LEO) satellite communication (SatCom) has become one promising technology to support the Internet-of-Things (IoT). However, due to the scarcity of satellite spectrum and the high cost of designing satellites, it is difficult to launch a dedicated satellite for IoT communications. To facilitate IoT communications over LEO SatCom, in this paper, we propose the cognitive LEO satellite system, where the IoT users act as the secondary user to access the legacy LEO satellites and cognitively use the spectrum of the legacy LEO users. Due to the flexibility of code division multiple access (CDMA) in multiple access and the wide use of CDMA in LEO SatCom, we apply CDMA to support cognitive satellite IoT communications. For the cognitive LEO satellite system, we are interested in the achievable rate analysis and resource allocation. Specifically, considering the randomness of spreading codes, we use the random matrix theory to analyze the asymptotic signal-to-interference-plus-noise ratios (SINRs) and accordingly obtain the achievable rates for both legacy and IoT systems. The power of the legacy and IoT transmissions at the receiver are jointly allocated to maximize the sum rate of the IoT transmission subject to the legacy satellite system performance requirement and the maximum received power constraints. We prove that the sum rate of the IoT users is quasi-concave over the satellite terminal receive power, based on which the optimal receive powers for these two systems are derived. Finally, the resource allocation scheme proposed in this paper has been verified by extensive simulations. [ABSTRACT FROM AUTHOR]

Climate change implications are a major concern for urban areas. They are vulnerable to major disasters and pandemics for the sheer number of people depending on the limited infrastructure in a confined area. Responding to this conundrum will require directing significant investments and resources towards the right solutions in the coming years. It is also imperative for us to consider whether our current infrastructure investment strategy is adequate. Governments and urban planners have so far favoured grey or traditionally engineered infrastructure for wastewater treatment. These legacy systems need to change to cater to current needs. Nature-based solutions (NbS) and blue-green infrastructure (BGI) are an alternative to restoring and managing a diverse integrated ecosystem, with the least amount of construction or interference with nature. They are a multidisciplinary, integrated approach towards a regenerative urban system that enables a healthy environment and has significant ecosystem benefits. The incorporation of circular economy criteria for evaluating NbS solutions in the urban domain is vital to ensure its sustainability. The paper will assess the NbS applications and blue green infrastructure concerning wastewater in urban areas and their effectiveness and scope towards promoting a circular economy. [ABSTRACT FROM AUTHOR]

Transpilers refer to a special type of compilation that takes source code and translates it into target source code. This type of technique has been used for different types of implementations in scientific studies. A review of the research areas related to the use of transpilers allows the understanding of the direction in this branch of knowledge. The objective was to carry out an exhaustive and extended mapping of the usage and implementation of transpilers in research studies in the last 10 years. A systematic mapping review was carried out for answering the 5 research questions proposed. The PSALSAR method is used as a guide to the steps needed for the review. In total, from 1181 articles collected, 683 primary studies were selected, reviewed, and analyzed. Proposals from the industry were also analyzed. A new method for automatic data tabulation has been proposed for the mapping objective, using a relational database and SQL language. It was identified that the most common uses of transpilers are related to performance optimizations, parallel programming, embedded systems, compilers, testing, AI, graphics, and software development. In conclusion, it was possible to determine the extent and identification of research sub-areas and their impact on the usage of the transpilers. Future research could be considered about the usage of transpilers in transactional software, migration strategies for legacy systems, AI, math, multiplatform games and apps, automatic source code generation, and networking. [ABSTRACT FROM AUTHOR]

The onset of a musical note is the earliest time at which a note can be reliably detected. Detection of these musical onsets pose challenges in the presence of ornamentation, such as vibrato, bending, and if the attack of the note transient is slower. The legacy systems such as spectral difference or flux and complex domain functions suffer from the addition of false positives due to ornamentation posing as viable onsets. We propose that this can be solved by appropriately improving the resolution of the onset strength signal (OSS) and smoothening it to increase true positives and decrease false positives, respectively. An appropriate peak picking algorithm that works well in unison with the OSS generated is also desired. Since onset detection is a low-level process upon which many other tasks are built, computational complexity must also be reduced. We propose an onset detection algorithm that is a combination of short-time spectral average-based OSS estimation, chirp group delay-based smoothening, and valley–peak distance-based peak-picking. This algorithm performs on par with the state of the art, SuperFlux and convolutional neural networks-based onset detection, with an average F 1 score of 0.88, across three datasets. Subsets from the IDMT-SMT-Guitar, Guitarset, and Musicnet datasets that fit the scope of the work are used for evaluation. It is also found that the proposed algorithm is computationally 300% more efficient than SuperFlux. The positive effects of smoothening an OSS, in determining the onset locations, are established by refining the OSS produced by legacy algorithms, where consistent improvement in onset detection performance is observed. To provide insights into the performance of the proposed algorithms when different ornamentation styles are present in the recording, three levels of results are computed, by selecting different subsets of the IDMT dataset. [ABSTRACT FROM AUTHOR]

An immature, underdeveloped data culture limits a digitised strategy (which has been seen as the best approach to treat the complexities of e-commerce and ‘fast couriers’) and stands in the way of the Jamaica Customs Agency (JCA) developing its capacity as a truly SMART (Secure, Measurable, Automated, Risk management based, Technology driven) administration. A commitment to paper-centric processes, disjointed legacy systems, fear and mistrust of technology, a lack of regard for data integrity and an underdeveloped appreciation for the need for advanced technology that facilitates necessary cooperation and collaboration of internal and external data flows are evident oppositions to data proficiency, and a foothold on e-commerce. JCA’s information management and border professionals have shed light on the challenges that disruptive innovations in the form of e-commerce and ‘fast couriers’ present to border management, and expressly identifies big data as a hopeful strategy to overcome current challenges. However, there is an acceptance of the fact that stakeholders need to develop an appreciation for data and its relevance in modern customs operations, as this is the key to a proactive Customs that can conduct intelligent targeting and make decisions based on evidence. Current trends in cross-border e-commerce and the legal and technological adaptations that have been necessary in Europe, Africa, Middle East and parts of the Caribbean have been cited as models for policy adjustments that the JCA could consider. In addition to its development of local standards for data management and technology that would foster the appetite of internal stakeholders, the JCA also needs to look to national technocrats for policy provisions to facilitate data use and flows among Border Regulatory Agencies (BRAs). The JCA should also appeal to national technocrats to institute policies related to and provide the requisite technological infrastructure to facilitate big data management locally and cross-border. [ABSTRACT FROM AUTHOR]

In the next generation modernization plan, the automatic dependent surveillance-broadcast (ADS-B) system plays a pivotal role. However, the ADS-B's low level of security and its vulnerabilities have raised valid concerns. The main objectives of this paper are to highlight the limitations of legacy ADS-B systems and to assess the feasibility of using Format-preserving (F), Feistel-based encryption (F), with multiple implementation variances (X) (FFX) algorithms, for enhancing ADS-B's security. The offered solution is implemented in a standard software-defined radio (SDR) ADS-B to be utilized in real-time applications. Furthermore, a new proposed blockchain scheme is used as a secured database to manage the cipher key. The metric of message entropy is used to assess an algorithm's ability to confuse and diffuse predictable ADS-B messages; correlation and serial correlation of plain data and cipher data are deployed to evaluate the proposed method's security level. The authors provide both MATLAB simulations and flight test outcomes to demonstrate the feasibility of this approach. Based on our security analysis, ADS-B information can be kept confidential through our scheme. The performance evaluation results reveal that the proposed scheme is achievable, compatible, and efficient for the avionics industry. [ABSTRACT FROM AUTHOR]

Business rules extraction is an important activity in situations in which a software system becomes obsolete and needs to be replaced by a newer system, since the replacing system needs to satisfy the business rules embedded in the legacy software system. In this paper, we investigate an approach in which the computational business rules of a legacy software system can be extracted given previously generated output of the system and without requiring access to the system’s source code. Furthermore, extracted computational business rules are validated automatically with minimal involvement of domain experts through Test-Driven Development (TDD) such that test cases are constructed from historic output of the system. The proposed approach is applied to extract the computational business rules of a large-scale governmental payroll legacy software system. The study results demonstrate that the suggested approach extracted computational business rules van meet a substantial number of test cases. Thus, the efforts involving domain experts can be reduces to analyze such instances. [ABSTRACT FROM AUTHOR]

Information system integration during merger and acquisition (M&A) is a critical process when organizations are joining. The integration process consisting of several crucial activities for making organizations being able to work together. Organizations need to evaluate internal and external factors in order to make the process work. In this paper we evaluated critical success factors, challenges and information systems (IS integration methods from interviews with employees in a case study to identify the most critical activities for a successful IS integration. The investigated process stretches from the introduction of the M&A to the actual integration of two organizations IT-stacks. The identified crucial activities found were top management support, organizational culture, importance of competence, internal communication, maintaining information system security, communication with end-users and managing legacy system. Evaluating these topics and creating a short and a long-term plan on how to integrate and work with these are essential to succeed with the IS integration in any M&A. [ABSTRACT FROM AUTHOR]

Cyber-Physical Production Systems increasingly use semantic information to meet the grown flexibility requirements. Ontologies are often used to represent and use this semantic information. Existing systems focus on mapping knowledge and less on the exchange with other relevant IT systems (e.g., ERP systems) in which crucial semantic information, often implicit, is contained. This article presents an approach that enables the exchange of semantic information via adapters. The approach is demonstrated by a use case utilizing an MES system and an ERP system. [ABSTRACT FROM AUTHOR]

As radio frequency (RF) hardware continues to improve, many technologies that were traditionally impractical have suddenly become viable alternatives to legacy systems. Two-way ranging (TWR) is often considered a poor positioning solution for airborne and other vehicular navigation systems due to its low precision, poor angular resolution, and precise timing requirements. With the advent of modern RF hardware and advanced processing techniques, however, modern studies have experimentally demonstrated TWR systems with an unprecedented, sub-centimeter ranging precision with low size, weight, power, and cost (SWaP-C) consumer-grade hardware. This technique enables a new class of positioning, navigation, and timing (PNT) capabilities for urban and commercial aircraft but also instigates new system design challenges such as antenna placement, installation of new electronics, and design of supporting infrastructure. To inform these aircraft design decisions, we derive 2D and 3D Cramér–Rao lower bounds (CRLBs) on position and orientation estimation in a multi-antenna TWR system. We specifically formulate these bounds as a function of the number of antennas, platform geometry, and geometric dilution of precision (GDoP) to inform aircraft design decisions under different mission requirements. We simulate the performance of several classic position and orientation estimators in this context to validate these bounds and to graphically depict the expected performance with respect to these design considerations. To improve the accessibility of these highly theoretical results, we also present a simplified discussion of how these bounds may be applied to common airborne applications and suggest best practices for using them to inform aircraft design decisions. [ABSTRACT FROM AUTHOR]

The revolution generated by the Internet of Things (IoT) has radically changed the world; countless objects with remote sensing, actuation, analysis and sharing capabilities are interconnected over heterogeneous communication networks. Consequently, all of today's devices can connect to the internet and can provide valuable information for decision making. However, the data collected by different devices are in different formats, which makes it necessary to develop a solution that integrates comprehensive semantic tools to represent, integrate and acquire knowledge, which is a major challenge for IoT environments. The proposed solution addresses this challenge by using IoT semantic data to reason about actionable knowledge, combining next-generation semantic technologies and artificial intelligence through a set of cognitive components that enables easy interoperability and integration for both legacy systems and emerging technologies, such as IoT, to generate business value in terms of faster analytics and improved decision making. Thus, combining IoT environments with cognitive artificial intelligence services, COSIBAS builds an abstraction layer between existing platforms for IoT and AI technologies to enable cognitive solutions and increase interoperability across multiple domains. The resulting low-cost cross platform supports scalability and the evolution of large-scale heterogeneous systems and allows the modernization of legacy infrastructures with cognitive tools and communication mechanisms while reusing assets. [ABSTRACT FROM AUTHOR]