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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]

This study employs bibliometric analysis to systematically explore the Visual Basic (VB) education research landscape, identifying significant trends, influential authors, and future research directions. Utilizing data from Scopus-indexed journals, we examined 529 papers published between 1994 and October 2023, identified through the keywords "visual basic," "visualbasic," "teaching," and "learning." These papers were analyzed using Biblioshiny to generate a bibliometric map, following four steps: data harvesting, data screening, data visualization, and data analysis. Our research reveals critical VB programming trends from 1994 to 2023, with academic output peaking in 2010 and declining since 2007. Ongoing interest is noted due to legacy system applications. Global publication reach facilitates cross-border information exchange, and top affiliations and authors underscore extensive and influential participation in this field. The research emphasizes incorporating fundamental and advanced themes in educational curricula. It suggests future research focusing on new programming paradigms, longitudinal studies, and VB relevance to technological advancements and industrial needs. Enhanced collaboration, interdisciplinary research, and attention to global trends are essential for maintaining the relevance of VB programming education, optimizing legacy systems, improving educational practices, and preparing students for modern programming environments. [ABSTRACT FROM AUTHOR]

Circa 2023, after receiving much hype and investment, two agri-food technologies touted for their world-changing potential, bioengineered animal protein substitutes and vertical (indoor) farms, began to falter economically. Tech sector observers attributed the fall to typical hype cycle dynamics; this paper provides a deeper read. Based on research involving over ninety interviews with agri-food tech sector actors and observation at nearly 100 industry events, we show an unrealized socioecological fix as first conceptualized by Ekers and Prudham. As attempts at preemption, these technologies were able to attract excess capital to an area believed to be in need of fixing, and their backers anticipated and in some cases tried to promote the devaluation of legacy production systems. But the technologies on offer failed to become cost competitive in a timely way since legacy production systems continued to be productive and profitable. It was these new companies that became uncompetitive and overvalued, which in turn turned investments in them into bad ones. Ironically, the agri-food tech sector has eschewed a path which might have made their products competitive, which is social regulation of legacy production systems. Instead they effectively speculated that such systems would implode under their own contradictions. [ABSTRACT FROM AUTHOR]

This consensus statement is the outcome of comprehensive collaboration through an international working group on the disparities in the legacies of major sporting events, specifically for communities and individuals from disadvantaged backgrounds (CIDBs). The workshop brought together scholars to discuss current challenges and develop four propositions and recommendations for event leveraging, policy stakeholders, and researchers. The propositions included (1) the nature of "disadvantage" needs to be recognized and the specific targeted CIDBs in each event context must be carefully identified or clearly defined; (2) CIDBs should be considered as an integral part of the whole event hosting cycle to ensure legacy inclusivity; (3) dedicated event leverage, sufficient financial backing, and resource commitments for CIDBs are needed; and (4) it is critical to establish a system of legacy governance for CIDBs. The recommendations aim to inform change in practice and ensure lasting positive legacies for the communities that need them most. [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]

This case study discusses the concept of big data analytics, which involves managing and analyzing large, complex data to discover patterns and insights. Implementing big data analytics has many benefits, such as improving decision-making processes, enhancing accounting processes and compliance, boosting marketing campaigns, and providing better credit risk assessment procedures. This case study aims for the student to understand the challenges of big data analytics implementation in a Malaysian commercial bank. Students take on the role of a Senior IT Manager who needs to consider solutions to the implementation problems. Although the team proposed using cloud-based services as a solution to these issues, the CEO expressed concerns about security risks and requested other alternative solutions. The team has also been requested to come up with solutions to the data quality issues experienced by the case study bank. [ABSTRACT FROM AUTHOR]

This study evaluates the effectiveness of common engineering processes in conducting system assessments, with a focus on legacy system assessments, which are currently a barrier to legacy system evolution. These processes include reverse systems engineering (RSE), which remains in its infancy, and more established processes such as software and hardware reverse engineering. The study assesses these methods to better understand their ability to define systems of interest and meet the criteria set by the four horsemen of traditional systems engineering, as defined by Rebovich and White, and the International Council of Systems Engineering. Additionally, RE techniques are reviewed to ensure that a formal, systems‐level process—with detailed steps and diagrams—is documented and accessible to systems or reverse engineers. Lastly, this investigation elucidates the ability of each reverse engineering process to discover, recover, or reproduce information pertaining to the systems of interest that is missing, incomplete, or outdated in documentation relevant to the development or operations of that system. This information can be used to better understand the areas of RSE that still need to be developed and aid in the maturation of a repeatable process that is applicable to legacy systems. The results of this study are expected to contribute to the further development of the Enterprise Lifecycle Model proposed by Terry and Chandrasekar. [ABSTRACT FROM AUTHOR]

The purpose of this article is to examine the relationships and impact of organizational agility and crisis management in insurance companies by measuring them. The significance of this study is as follows: As there is no quantitative study in the management literature that addresses organizational agility and crisis management in insurance companies, this study contributes to filling this gap in the literature. The objective of this study is to investigate the impact of organizational agility on crisis management processes within the insurance industry. The methodology employed in this research comprises the collection and analysis of data through a review of the relevant literature and the administration of surveys or interviews to companies operating within the insurance sector. In the context of the dynamic environment of the insurance industry, the role of organizational agility is of significant importance in the effective management of crises and the development of corporate resilience. The principles of organizational agility can be manifested in a variety of strategies, each tailored to the specific risks present in the insurance industry. The implementation of agile practices may be impeded by factors such as resistance to change, the presence of legacy systems, and regulatory constraints. Overcoming these obstacles therefore depends on the presence of strong leadership, the investment of resources in employee training, and the establishment of a culture of learning. Managers in the insurance industry can enhance their organizations' agility during crises by strengthening their teams, fostering a culture of adaptability, and prioritizing continuous improvement. This study examines the impact of organizational agility on crisis management in the insurance sector. In order to achieve these objectives, the findings were obtained by applying a survey technique to 392 employees in the insurance sector in Istanbul. The findings indicate a positive correlation between organizational agility and crisis management in the insurance sector. [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]

Organizations undergo significant change in the pursuit of digital transformation. Among those changes is replacing legacy information systems (LIS) with updated systems that integrate data, platforms, and software across all operational functions. The development of a broad legacy replacement system requires extensive resourses and a core development team composed of technical professionals with development oversight and operational experts representing the diverse interests of the functional units. Within the team, replacing LIS raises task conflicts in the distribution of system resources across operational departments and perception differences in the replacement system's alignment with the organization's needs. Prior studies suggest deploying collaborative strategies to resolve task-related conflicts on resource allocation and design preferences without contextual consideration. We built a model rooted in conflict survival theory to consider conflicts arising in the design of the replacement system, identify appropriate design team mechanisms, and examine conflict resolution strategies targeted to the context. A mixed-methods study of survey results from 119 legacy replacements and interviews of legacy replacement project managers indicates that teams adapt responses to the different conflict types inherent to legacy replacement design and should further consider selecting a conflict resolution strategy. Identifying appropriate actions and distinct conflict resolution strategies adds to task conflict theory and advises practice selecting the more appropriate activities and strategies specific to replacement system design. [ABSTRACT FROM AUTHOR]

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Operational resilience is the preparedness and existence of resilient IT systems, processes and resources to provide services continuously during a disruption. This study examines the operational resilience implementation challenges and opportunities for UK building societies and wider financial institutions. The key challenges are resource constraints, understanding regulatory requirements, supplier concerns, domain knowledge, legacy systems and manual-driven processes. Some challenges are being addressed with awareness and training, improved governance and risk management, recruitment and industry support networks. The opportunities include realising the actual state of IT systems, adopting cloud solutions, resources and capabilities, as well as improved domain knowledge and a better understanding of services and markets. Regulators need to be more explicit about their expectations and requirements, while the industry needs to collaborate on setting up common frames of reference for operational resilience frameworks. Lastly, the study provides a holistic view of operational resilience, including its implementation and management challenges, control measures and emerging opportunities. [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]

The transition from an error-prone, slower, and extremely high-volume legacy system like monolithic system to a faster, lighter, and error-free microservices based system is not always so simple. Microservices are independently deployable and allow for a better team autonomy. In this work, several migration efforts to migrate from a legacy based monolithic system to a pure distributed microservicesbased system has been tested and deployed in keeping two DevOps principles, the software code build and deployment time and latency in monolithic and microservices. Some real-time projects are considered to measure the performance and the time taken to execute the experiments. To measure the total build and deployment time and latency, Jenkins, Prometheus, and JMeter are installed which are industryrecommended softwares. It is observed that there is a total of 7 seconds taken to build and deploy at containers for 10 microservices whereas 10 monolith applications took almost 260 seconds to be built and deployed to the application server. While increasing more requests per second it is observed that upto 3000 requests per second, it impacted the response time of monolith applications but microservices stays the same. The main conclusion is that microservices are rarely impacted in response time with respect to requests per second. [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]

This article focuses on discussions of territorial rights and self-determination in the last 15–20 years. Theories of territorial jurisdiction typically combine two elements. First, they offer an account of foundational title: What gives a particular set of people a claim to be located in an area, including the right to form political institutions to govern that space? Second, they offer an account of legitimate jurisdiction: What is the moral basis of a state's right to govern the area and its population? This article begins by reconstructing prominent accounts of foundational title and legitimate jurisdiction. After canvassing these views, I highlight three areas where further work is needed, particularly as the territorial states system begins to be challenged by climate change: the appropriate balance of sovereignty and international authority in a world of global interdependence, how to rectify imperial and colonial legacies in the states system, and the just distribution of territory. [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]

Three Baltic States (3Bs), like most eastern NATO countries, treat the war in Ukraine as a strong national interest: if Russia is stopped in Ukraine, it will not be capable of inflicting any harm to them. As a result, national decision-makers are making hard choices on which military capabilities need to be transferred to Ukraine and what must remain for national needs. The results indicate that 3Bs Defence Ministers understand the importance of supporting Ukraine with lethal and non-lethal items. 3Bs acquire major arms from various countries with different spending levels and have limited ability for combined procurement opportunities, although they seek to develop similar military capabilities. It is assessed that 3Bs will not deliver newly acquired major arms to Ukraine and will continue delivering reliable legacy systems. Therefore, allocating funds for combined major arms procurements implemented by the third party like the U.K.-led International Fund for Ukraine, is the most viable option to continue supporting Ukraine. [ABSTRACT FROM AUTHOR]

Requirements traceability (RT) is the ability to link requirements to other software development artifacts. In pre-requirements (pre-RS) traceability, requirements are linked to their origin, such as interviews with stakeholders, meeting protocols, or legacy systems. Compared with post-RS traceability, which links requirements to source code and other later artifacts, pre-RS traceability has seen much less research. This article presents a systematic literature review of pre-RS traceability based on 77 articles published between 1992 and 2022, aiming to provide a comprehensive overview of its use cases, benefits, problems, and solutions. Through the analysis of existing literature, this review identifies gaps for future research and establishes a foundation for future investigations in the field of pre-RS traceability. [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]

Background and aims: Crop cultivation practices and soil legacies are intrinsically linked and are hypothesized to influence direct and indirect plant defences against phytophagous insects. In this study, we tested how soils conditioned by push-pull (maize (Zea mays)- Desmodium spp.- Brachiaria spp. intercrop) or maize monoculture (non-push-pull) affect maize phytochemistry and subsequent resistance to fall armyworm (Spodoptera frugiperda Smith, Lepidoptera: Noctuidae). We hypothesised that conditioning soil with push-pull positively impacted maize growth, metabolism, and subsequent direct and indirect resistance to an invasive herbivore pest. Methods: Maize was grown in soils collected from push-pull and maize monoculture fields. We compared maize growth, herbivore larval feeding, production of defense secondary metabolites on maize grown in soils conditioned by push-pull and non-push-pull cropping. As a proxy for indirect defence effects, we also measured behavioural responses of egg-larval parasitoid Chelonus bifoveolatus Szpligeti (Hymenoptera: Braconidae) to maize volatiles from plants planted in soils conditioned by each cropping system. Results: Maize plants grown in soil conditioned by push-pull had a higher biomass accumulation and plant height. Higher quantities and more diverse volatile and non-volatiles metabolites were observed in maize grown in push-pull soil in comparison to those grown in maize monoculture soil. Behavioural assays showed that S. frugiperda neonate fed more on leaf tissue from maize plants planted in soil conditioned by maize monoculture than those planted in push-pull conditioned soil. Parasitoid wasps were more attracted to volatiles from maize planted in push-pull conditioned soils than those planted in non-push-pull soils. Conclusion: Our results indicate that conditioning soil with polyculture push-pull enhances maize growth, alters phytochemistry and subsequent direct and indirect resistance to S. frugiperda. [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]

Similar to software systems, programming languages evolve substantially over time. Indeed, the community has more recently seen the release of new versions of mainstream languages in shorter and shorter time frames. For instance, the C++ working group has begun to release a new version of the language every 3 years, which now has a greater number of modern C++ features and improvements in modern standards (C++11, C++14, C++17, and C++ 20). Nonetheless, there is little empirical evidence on how developers are transitioning to use modern C++ constructs in legacy systems, and not understanding the trends and reasons for adopting these new modern C++ features might hinder software developers in conducting rejuvenation efforts. In this paper, we conduct an in‐depth study to understand the development practices of KDE contributors to evolve their projects toward the use of modern C++ features. Our results show a trend in the widespread adoption of some modern C++ features (lambda expressions, auto‐typed variables, and range‐based for) in KDE community projects. We also found that developers in the KDE community are making large efforts to modernize their programs using automated tools, and we present some modernization scenarios and the benefits of adopting modern C++ features of the C++ programming language. Our results might help C++ software developers, in general, to evolve C++ legacy systems and tools builders to implement more effective tools that could help in rejuvenation efforts. [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.

Learning analytics (LA) is an emerging area that has had extensive development in higher education in recent years, focused both on the learning process of students within subjects and on monitoring their trajectories in training programmes. However, most of the developments remain in the pilot phase without reaching institutional adoption. This paper reports the results of a systematic review of the literature carried out with the aim of identifying the factors that influence the adoption of LA, as well as the existing strategies that facilitate such adoption in higher education institutions. The results show that factors for LA adoption are situated in multiple dimensions, including stakeholders at different levels, institutional and pedagogical processes, technical limitations and ethical considerations. This work contributes with a consolidated list of 14 critical factors to be considered to effectively adopt LA tools, addressing planning, strong leadership, collaboration and prioritizing senior management commitment, goal setting, cross‐organizational design, educational process redesign, system integration and legacy system linkage. On the other hand, a variety of frameworks, models and approaches are distinguished in the literature to help the adoption of LA, however, none of them cover all the factors involved in such adoption. Therefore, we provide a compilation of strategies that have been used in the literature to reduce the gaps associated with the different factors described. Practitioner notesWhat is already known about this topic LA is a field of research that has had a lot of interest and a wide variety of tools have been developed. Still, it is criticized that they originate more from data availability than from the needs of students, teachers and decision makers.LA tools are mostly evaluated on their usability aspects and to a lesser extent on their usefulness to impact learning processes.The adoption processes of LA in higher education are oriented to specific aspects such as the relevance of the visualizations in the different contexts of application, but they do not address extensively other important aspects involved in such adoption.What this paper adds This article seeks to synthesize what has been investigated in the literature regarding the factors that affect the adoption of LA by higher education institutions.Describe the 14 critical factors identified from the literature to address adoption of multiple dimensions that include stakeholders at different levels, institutional contexts and ethical considerations.A compilation of strategies that have been used in the literature to reduce the gaps associated with the different factors are described, and the aspects or dimensions that are not addressed are highlighted.Implications for practice and/or policy The synthesis of LA adoption factors and the approach strategies present in the literature allow practitioners to focus their efforts on the key aspects to consider in their LA adoption processes.Future work could shift from a pure design perspective to strengthen an engineering perspective in the adoption practices.From the point of view of the policies associated with the adoption of LA in higher education institutions, it is concluded that it is necessary to approach the process from a comprehensive perspective considering all the dimensions involved. [ABSTRACT FROM AUTHOR]

Increased demand on rail, due to climate initiatives and passenger numbers, places significant pressure on existing railway operations; specifically on capacity, operational flexibility, and network robustness. These pressures are exacerbated by constraints, which prevent the construction of new track and infrastructure. This results in the need to use existing infrastructure and operating processes. One proposed solution is digitalization, which results in autonomous rail, where automated and connected intelligent transport systems facilitate smart traffic management. However, this generates the challenge of integrating autonomous trains and their associated technologies with existing infrastructure and operations. To understand this issue from an enterprise level, this paper has applied Brian Wilson's Soft Systems Methodology (a variation of Checkland's methodology) to the problem situation. This methodology explores and investigates the existing rail system in Great Britain (UK less Northern Ireland) and its stakeholders. The paper aims to propose a solution into how to transform the legacy system into one which incorporates autonomous operations and ultimately becomes fully autonomous. It culminates in a series of models that are relevant to those with a legitimate interest in the system. The models identify the activities required to analyze whether autonomy is worthwhile and if it is, how to successfully integrate it with legacy operations. Additionally, the models provide the basis for which a formal stakeholder analysis can take place. [ABSTRACT FROM AUTHOR]

Automatic detection of the precise occurrence and duration of an attack reflected in time-series logs generated by cyber-physical systems is a challenging problem. This problem is exacerbated when performing this analysis using logs with limited system information. In a realistic scenario, multiple and differing attack methods may be employed in rapid succession. Modern or legacy systems operate in multiple modes and contain multiple devices recording a variety of continuous and categorical data streams. This work presents a non-parametric Bayesian framework that addresses these challenges using the sticky Hierarchical Dirichlet Process Hidden Markov Model (sHDP-HMM). Additionally, we explore metrics for measuring the accuracy of the detected events, their timings and durations, and compare the computational efficiency of different inference implementations of the model. The efficacy of attack detection is demonstrated in two settings: an avionics testbed and a consumer robot. [ABSTRACT FROM AUTHOR]

In this commentary article, we outline research challenges and possible directions for the potential applications of AI in the judicial domain by specifically considering process analysis in the Italian context. Applying AI to process analysis poses several challenges, including information extraction from legacy information systems and analysis of legal documents, process modeling with a particular emphasis on temporal analysis, real-time process monitoring, conformance and compliance checking, predictive techniques for accurate predictions, and analysis of judges' workload. Solutions to these challenges include methods and tools for data identification and collection, innovative approaches to process modeling, reactive techniques for real-time monitoring, conformance checking with explainability, language models adapted to specific domains, and the identification of suitable indicators for the analysis of case handling efficiency and case classification. [ABSTRACT FROM AUTHOR]

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]

In facing digital transformation challenges, universities need to set up their data governance strategies. They include effective solutions to trace and value data about key assets (such as researchers, publications, courses, research projects) scattered across multiple legacy IT systems. As part of an overall solution to deal with the unavoidable data fragmentation and diversity, we provide the complete code of a simple and very efficient framework that can be employed by universities to develop their own knowledge graph, offering a comprehensive picture of the strategic data of the university, such that it can be consistently exploited by different digital services. [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]

In Erlang, behaviours are special forms of design patterns. There are many benefits to using behaviours. For example, behaviours can help abstract away the most common parts when solving similar problems. Design pattern recognition may help understand the source code of the software. It can provide structured information about the purpose of specific parts and the design decisions behind the implementation. For object-oriented languages, several tools exist that use different approaches and methods to identify design patterns. We present a method for identifying source code fragments in legacy Erlang systems amenable to transforming into client-server Erlang design patterns. In our analysis, we identify the base set of server candidates using concurrent process analysis and narrow down the result using further static analysis knowledge using the RefactorErl framework. [ABSTRACT FROM AUTHOR]