Towards the automated extraction and refactoring of NoSQL schemas from application code

Sustainability starts with the consumer: How green consumption shapes business model transformation a COM-B analysis of green purchase behavior in Vietnam’s agriculture

This paper concerns a particular property of the basic instrumental variable (IV) estimator that is useful for multiple-input multiple-output (MIMO) modeling problems where it is not obvious how to partition the available signals between the sets of inputs and outputs. In general, a repartitioning of the input and output signals will result in a different model compared to the original input–output choice. It is important to distinguish cases where a repartitioning results in an algebraically equivalent model and cases where the resulting model transformation is more complex and depends also on particular system and signal properties. The latter situation typically occurs when models are estimated from data. We here show that the basic IV estimator is an exception since it provides algebraically equivalent estimates regardless of true system structure, noise properties, or amount of data. This equivalence result is illustrated in two simulation examples.

Enhanced AR-based simulation method for non-Gaussian wind pressure process using machine learning model and piecewise Johnson transformation model

The study addresses the transformation of traditional business models in automotive engineering under the impact of the Fourth and Fifth Industrial Revolutions. At the center of the analysis lies the emergence of a hybrid organizational model that combines high-tech customized production with a digital media platform. Using the “Mashinatory” project as empirical material, the paper reveals the logic of open innovation through which small engineering enterprises are able to offset resource constraints by engaging an external expert environment and by deliberately mobilizing social capital. Particular attention is given to the technological predictors of performance, including the use of metrological 3D scanning, additive technologies, and reverse-engineering tools. An empirical examination of the global automotive modification market demonstrates the resilience of demand for personalization, thereby creating the conditions for the expansion of socially oriented production models. The paper substantiates the argument that incorporating media content into the structure of the production cycle not only diversifies financial flows but also serves as a mechanism for verifying technological solutions. The study also proposes a theoretical framework synthesizing the resource-based approach and transaction cost theory in order to assess the efficiency of open innovation ecosystems in the small and medium-sized enterprise segment. The material has both applied and conceptual significance for engineering and design practice, the management of innovative start-ups, and the processes of industrial digital transformation.

Aim. To present the results of a review of dissertations defended in 2001–2025 in Russia in the field of educational forecasting, identifying thematic areas and gaps, as well as possible prospects for scientific developments. Methodology. The research is based on a descriptive approach, which involves recording and systematizing data and analyzing the current situation. Content analysis for the quantitative measurement of dissertations and thematic blocks, classification method for the purpose of systematizing the dissertations themselves by relevant keywords to obtain a sample corresponding to the research objective, thematic analysis method for the purpose of identifying thematic blocks in the data array, and critical analysis method for identifying thematic gaps and potential prospects for further research were used as research methods. The source for the analysis was an array of dissertations in the field of predictive studies of educational development, which were defended between 2001 and 2025 in Russia according to the pedagogical codes of scientific specialties of the nomenclature of the Higher Attestation Commission of the Russian Federation. In total, the sample included 164 dissertations, of which 137 were Cand. Sci. theses, and 27 Dr. Sci. theses. Results. The study revealed that a total of 136 theses were defended in the 2000s, 28 in the 2010s, and 0 in the first five years of 2020. The three most frequently occurring thematic blocks in dissertation research were forecasting as a skill (competence, ability, potential, skills), for example, for the development and implementation of projects, the prevention of deviant behavior, etc. (including among teachers), forecasting educational outcomes, learning success, or learning difficulties, a predictive model in the management of an educational organization (advanced development), monitoring based on forecasting and evaluation, often associated with strategic management, including in the region. The sample did not include dissertation studies devoted to the development of pedagogical phenomena, teaching aids, the transformation of professions and models of professional activity, or the impact of technology on education and training. These topics can form the basis for further research. Research implications . The scientific novelty of the research lies in its identification of thematic clusters of dissertations defended in 2001–2025 in the field of educational prognostics and the identification of thematic gaps. At a theoretical level, the study’s results expand our understanding of the current state of educational prognostics in the Russian scientific community, contributing to prognostic theory and the history of its development. Its practical significance lies in its formulation of recommendations for future research. Conclusions. Prognostic research on the future development of education in the form of dissertations is currently virtually nonexistent. Forecasting is increasingly becoming a method used in conjunction with other methods in other fields (economics, sociology). Foresight practices, which became popular in the 2010s, will undoubtedly remain in demand, as they, by evolving into more scalable and accessible formats for a wider audience, make the development of educational forecasting research as a field possible and influence educational policy.

This study examines the transformation of the government's collaborative innovation model in handling street children, focusing on the TRC Saribattang (Rapid Reaction Team) innovation in Makassar City. It is a team initiated by the government and formed by the Social Service of Makassar City to maximize performance related to handling street children, beggars, and homeless people in Makassar City. This research investigates the relative advantages, compatibility, complexity, trialability,and observability of the TRC Saribattang innovation in the context of street children management. Data was collected from 12 participants involved in street children management in Makassar City. The study analyzed their perceptions of the TRC Saribattang innovation. This qualitative phenomenological study explored Makassar’s TRC Saribattang innovation for handling street children. Results show simplified processes, stronger cross-sector coordination, and improved access to information, with significant reductions in the number of street children. The implementation's complexity is perceived as low. Observability is evident in the reduction of street children, although discrepancies exist between field reality and reported data. Challenges such as resource limitations, social resistance, and institutional coordination barriers persist. The TRC Saribattang innovation presents a simplified and more coordinated approach to managing street children. The findings of this study are based on a limited number of participants in one city and do not include comparisons with other areas. Explore the long-term impact of the TRC Saribattang innovation, investigate strategies to overcome the identified challenges, and examine the applicability of a pentahelix model for a more integrative approach.

Deep learning-based synthetic contrast imaging has been proposed as an alternative to iodinated and gadolinium-based contrast agents in CT and MRI. This scoping review aimed to provide a cross-modality overview of current evidence, including study characteristics and validation strategies. Following PRISMA-ScR guidelines, PubMed, Embase, Scopus, and Web of Science were searched from inception to September 2025. Eligible studies applied deep learning to synthesize contrast-enhanced CT/MRI from non-contrast or modified-contrast inputs and reported reference-based validation. Extracted variables included modality, anatomy, input type, model type, dataset scale, validation category, and four predefined evaluation metrics. Fifty-six studies met the inclusion criteria (25 CT, 31 MRI). The brain was the most frequent target, followed by head and neck, breast, and liver applications. Non-contrast inputs were used in 71% of studies, with the remainder using modified-contrast strategies. Generative adversarial networks were the predominant model class, while diffusion and transformer models appeared after 2023. Dataset sizes ranged from 10 to 7306 (median, 218), and 57% of studies were single-center. Quantitative fidelity was evaluated in 88% of studies, reporting structural similarity index values of 0.73-0.99 and peak signal-to-noise ratios of 22-51 dB. Task-based performance was assessed in 39% of studies, radiologist-rated image quality in 54%, and diagnostic performance in 30%, with sensitivities of 72-92% and specificities of 59-95%. Deep learning-based synthetic contrast imaging shows high quantitative and perceptual fidelity, but evidence supporting diagnostic interchangeability and routine clinical use remains limited. Question Reducing contrast media use in CT and MRI is a clinical priority, and deep learning-based synthetic imaging is emerging as a potential alternative. Findings Deep learning generated synthetic contrast-enhanced images with high quantitative fidelity and acceptable image quality, but diagnostic performance was evaluated in only limited studies. Clinical relevance This review outlines the capabilities and limitations of deep learning-based synthetic contrast imaging, informing the development of contrast-minimizing strategies for safer CT and MRI practice.

The article provides a comprehensive analysis of the socio-economic aspects of the development of an inclusive economy in Ukraine in the context of contemporary transformational processes. It examines theoretical approaches to interpreting the concept of “inclusive economy” and substantiates its importance as a tool for ensuring sustainable and balanced socio-economic growth. Particular attention is paid to the analysis of social dimensions of inclusive development, including employment, social protection, and the reduction of income inequality. The study identifies the economic drivers of the formation of an inclusive economy, among which the key ones are the development of human capital, support for small and medium-sized enterprises, the promotion of social entrepreneurship, and the digitalisation of economic processes. Special emphasis is placed on the transformation of the inclusivity model in Ukraine, particularly in the context of digital and just economic transformation, as well as on the application of the RISE and SPIRIT tools to ensure socio-economic resilience. The paper analyses the key problems and challenges associated with implementing an inclusive economy in Ukraine, including regional disparities, institutional constraints, and limited financial resources. It substantiates the priority directions of socio-economic policy aimed at fostering an inclusive economic environment and integrating the inclusivity principles into Ukraine’s post-war recovery strategies. The article also examines the socio-economic relations that emerge in the process of developing an inclusive economy in Ukraine under conditions of structural transformation and post-war challenges. The author substantiates the theoretical foundations of the inclusive economy and identifies priority directions for its development in Ukraine, taking into account relevant socio-economic factors. The study presents significant research findings that can be used in the formulation and implementation of socio-economic policies aimed at creating an inclusive economic environment, as well as in the development of post-war recovery and sustainable development strategies in Ukraine. It is emphasised that the integration of inclusivity principles into state economic policy is a necessary prerequisite for enhancing social cohesion and ensuring the long-term competitiveness of the national economy.

The article examines the interrelation between decentralization and glocalization processes in public administration as key drivers of the transformation of contemporary regional development models. It is argued that decentralization, while retaining its role as a fundamental institutional mechanism for the redistribution of powers and resources, is no longer limited to a purely administrative reform. Instead, it is increasingly embedded in broader governance transformations characterized by multi-level interaction among national, regional, and local actors. The study demonstrates that the expansion of governance practices, the emergence of network-based forms of interaction, and the growing impact of global factors necessitate a reconsideration of decentralization within a wider analytical framework. In this context, decentralization is interpreted not as an isolated process but as an integral component of complex structural changes in public administration. It has been proven that the interaction between decentralization and glocalization facilitates the transition from hierarchical to network-based models of public governance, in which coordination, partnership, and the integration of different levels of government play a key role. The expediency of developing new models of regional development based on the combination of local resources with global opportunities, innovation, openness, and resilience to external challenges has been substantiated. It has been established that their effectiveness depends on the ability of territories to engage in strategic planning, attract investment, develop human capital, and implement modern management tools. It is concluded that the effectiveness of such models depends on the ability of regions to strengthen institutional capacity, enhance strategic planning, attract investment, and implement modern governance tools. The findings highlight the importance of aligning governance approaches across different levels and developing new mechanisms of interaction that ensure both global integration and the preservation of regional specificity.

Industrial electric motors are the backbone of modern manufacturing, necessitating high-reliability maintenance strategies to prevent costly downtime. While acoustic monitoring offers a non-intrusive alternative to traditional vibration or temperature analysis, conventional handcrafted features such as Mel Frequency Cepstral Coefficients (MFCCs) and Spectral Centroids often struggle to capture the complex temporal-spectral dependencies of real-world industrial settings. These "shallow" features are frequently compromised by environmental noise and fluctuations in machine load, leading to poor generalization across diverse fault types like gearbox anomalies or fan imbalances. To overcome these limitations, this study proposes a novel diagnostic framework leveraging Waveform-based Language Model (WavLM) transformer embeddings. Unlike classical methods, WavLM extracts rich, contextaware representations from raw audio, providing superior noise tolerance and structural depth. These high-dimensional embeddings were evaluated across multiple classifiers, including Categorical Boosting (CatBoost) and Decision Trees. Experimental results demonstrate that the proposed Histogram-Based Gradient Boosting (HGB) classifier achieves a state-of-the-art accuracy of 95%, significantly outperforming traditional machine learning pipelines. To facilitate industrial adoption, a graphical user interface (GUI) was developed to streamline data processing and real-time fault prediction. The research offers a scalable, high-precision solution for predictive maintenance, proving that self-supervised audio representations can dramatically enhance diagnostic robustness in noisy industrial ecosystems.

This is a systematic literature review that show casesthe centrality of Business Analysis (BA) as a strategic force in a successful organisational digitalisation effort. Although the modern-day digital transformation can be considered through the prism of technology, this paper is aimed to change the emphasis on technology while focusing on the analytical mindset that is necessary to convert digital ambition into quantifiable business value. Through a thorough review of literatures, the paper comes up with a strong conclusion that Business Analysis is a bridge and a dynamic organizational resource that is needed to cope with the challenges of business model innovation and process re-engineering needed to transform a modern enterprise in a world driven by fast technological innovation. The results of the research and analysis indicate that having an effective Business Analysis practice within an enterprise resolves the long-running alignment gap as digital initiatives are clearly connected to both the strategic goals and the needs of stakeholders within and outside the enterprise. A strategic sensing, in which Business Analysis makes it possible to detect digital opportunities, operational efficiency, in which the strict value stream mapping achieves business model transformation, structured frameworks, like the Compass Method, have been identified as key drivers. The research finds that the absence of the translator role that the business analyst plays causes the digitalisation efforts to continue being both technical and operational cost centers and not strategic assets. The study, therefore, highlights the need to incorporating Business Analysis as an essential, critical, and strategic driver of digital transformation journeys to swiftly create value and reduce the risks of misaligned, not fit for purpose implementation while ensuring that competitive advantage is established in an increasingly automated world.

In the context of increasingly fierce competition in the retail industry, traditional supermarkets face multiple challenges such as declining performance, increasing financial pressure and business model transformation, resulting in its financial risks constantly increasing. Establishing a scientific financial risk early warning system helps enterprises identify potential crises in advance, which is of great practical value for optimizing financial decisions and ensuring business safety. The article analyzes the financial data of Yonghui Superstores over the past five years (2020-2024) based on Altman's z-score for early warning of financial risks. The research shows that the z-score of Yonghui Superstores has generally been on a downward trend and has entered the gray warning zone, indicating the existence of several potential risks requiring attention. On this basis, the article analyzes in more detail the main factors leading to these risks from the perspectives of the external environment and internal business operations,and proposes corresponding risk prevention suggestions.

In the context of rapid development of digital technologies and the increasing turbulence of the external environment, traditional approaches to program and project management demonstrate limitations: low flexibility, weak adaptability, and limited integration with digital platforms. The purpose of the study is to systematize the methodological foundations of program and project management digitalization and analyze the management models transformation in digital economy. It has been shown that the classical models (Waterfall, cascade, and hybrid) are effective only in a stable environment, but lose relevance when conditions are highly variable. Modern methodologies (Agile, Scrum, and Kanban) provide flexibility and iterativity, but require integration with digital tools. Special attention has been paid to business intelligence, collaboration systems, artificial intelligence technologies, and digital twins as key elements of digital management. The methodological basis of the study has been made up of systematic, functional-analytical, and comparative approaches, which made it possible to identify contradictions between traditional schemes and the digital environment requirements. It has been established that successful digitalization of program and project management involves proactive risk management, real-time monitoring of metrics, end-to-end transparency of processes, and adaptive management architecture. The study results can be used in developing conceptual models of digital program and project management, forming digital transformation strategies in government and corporate structures, as well as improving management practices in the face of constant change.

The article is devoted to a comprehensive theoretical and legal study of the participation of civil society in the formation and implementation of Ukraine’s veteran policy in the conditions of prolonged armed aggression and transformation of the social state model. The relevance of the chosen topic is due to the increase in the number of war veterans, the complexity of their social, psychological and legal needs, as well as the limited institutional capacity of the state to independently ensure the effective implementation of veteran policy without the involvement of non-state actors. The article substantiates that veteran policy in modern conditions goes beyond the traditional administrative and social approach and acquires the features of a comprehensive public policy, the implementation of which is impossible without the systematic participation of civil society institutions. Public and veteran organizations are considered as full-fledged subjects of public policy, performing the functions of representing the interests of veterans, advocacy, providing social and rehabilitation services, as well as public control over the activities of public authorities. The current legislation of Ukraine in the field of veteran policy and civil society participation was analyzed, revealing its fragmentary and mostly declarative nature. It is emphasized that the normative model of veteran policy is oriented mainly on a paternalistic understanding of the role of the state, while the institutional mechanisms of partnership with civil society remain insufficiently regulated. Particular attention is paid to the problem of inconsistency between the actual role of public organizations in the implementation of veteran policy and their formal legal status. The article analyzes the main institutional forms of civil society participation in the formation of veteran policy, in particular public consultations, the activities of advisory bodies, participation in working groups and advocacy processes. It is substantiated that the dominance of consultative mechanisms does not ensure real public influence on strategic decision-making and reduces the legitimacy of state veteran policy. The role of civil society at the stage of policy implementation, where non-governmental organizations actually compensate for the institutional failure of the state in the areas of psychological rehabilitation, social adaptation and legal assistance to veterans, is separately revealed. The author identifies key problems and risks of civil society participation in veterans’ policy, in particular the asymmetry of responsibility between the state and civil society organizations, the instability of funding, the fragmentation of the veterans’ environment, and the risk of instrumentalization of public structures. As a result, the need for a transition from a declarative to an institutionally entrenched partnership model of veterans’ policy, based on the principles of legal certainty, subsidiarity, and joint public responsibility of the state and civil society, is substantiated.

Enterprise resource planning (ERP) systems are evolving to support organisations in addressing their climate impact. Yet, there is a paucity of empirical and cross-sectoral data on how these solutions can mitigate organisations’ negative climate impact through changes in business models. By focusing on a dataset of ERP-related patents published between 2020 and 2024, within a climate change classification, this study aims to shed light on how ERP-based solutions can enable business model transformation to improve environmental performance and to investigate Industry 4.0 technologies that facilitate such mitigations. Through a pragmatic inductive approach employing mixed methods, the study uncovers three main areas of business model transformation for climate change mitigation: production optimisation, sustainability management and monitoring, and supply chain performance improvement. While most of the examined patents prioritise production optimisation, the findings reveal the emergence of novel applications designed to enhance organisational sustainability management and monitoring. Furthermore, the research emphasises unexploited opportunities to enhance ERPs through the integration of Industry 4.0 technologies. This study provides a substantial contribution to the existing literature by focusing on a significant yet underexplored area: ERP-based solutions designed to enable business model transformation to mitigate climate change, with implications for researchers, organisational adopters, and system developers.

Abstract Transitioning to a sustainable economy requires firms to transform their business models in accordance with circular economy principles. Circular economy scholarship has predominantly examined resource-rich large firms and circular startups, leaving established, resource-constrained small-to-medium-sized enterprises (SMEs) underexplored. Facing capability constraints regarding resources, knowledge, and organizational capacity, government policy intervention plays an important role. Through interviews with 15 experts and analysis of seven government programs, we reveal the transition dynamics that shape SME circular engagement and how government intervention can reinforce the optimization of linear business models or facilitate moving toward circular business model transformation.

Purpose Facing an innovation crisis marked by escalating research and development (R&D) costs, declining R&D productivity and recurrent patent expirations. Big Pharma companies have undergone a profound business model transformation from the traditional blockbuster model to the specialty pharma model, centered on targeted therapies for complex and rare diseases. This study aims to examine how this transformation reshaped R&D organization, analyzing the opening of corporate R&D to external participants and the emergence of multi-stakeholder R&D ecosystems. Design/methodology/approach The paper adopts a document-based analysis of R&D practices of ten leading Big Pharma firms. Peer-reviewed empirical studies, industry reports and company disclosures are analyzed, compared and strategically interpreted to trace the evolution of open R&D in the context of business model change, with particular attention to the increased adoption of digital technologies in drug development. Findings The specialty pharma model required Big Pharma companies to access biotechnological and digital competencies complementary to their internal capabilities, making open R&D a central strategic response. Over time, these companies established a broad range of long-term collaborative R&D arrangements with diverse external partners. Across companies, a novel R&D organization structure in the form of multi-stakeholder R&D ecosystems is emerging around Big Pharma firms, coordinated through soft management mechanisms. Findings also indicate that digital technologies, particularly artificial intelligence, are expanding the pool of potential collaborators and enabling new forms of interaction and coordination across the R&D value chain. Originality/value The study reframes open R&D in Big Pharma not as an isolated strategic choice but as a business model-driven shift from acquisition-led integration and ad hoc partnering to the orchestration of R&D ecosystems, linking business model theory with open innovation and ecosystem literature in a pharma-specific frame.

The crisis of macroeconomic paradigms and core transformations of economic models bring to the fore the need for a reflection on the origins of the theoretical ideas about the regularities of economic development. The paper delves into the evolution of scientific ideas related to regional economics as an independent area of economic scholarship by providing a retrospective view of economic research within the Urals’ space. Based on the principal thrusts of the evolutionary-heuristic paradigm, the study particularly focuses on the origins of regional economic research and scholars’ contribution into the formation of scientific ideas about the Ural economy. The method of the study is a summarizing review of the literature coupled with comparative and historical analysis. The findings demonstrate that during the USSR period, regional research developed markedly – the soviet scholars promoted the theory and practice of long-term planning, justified the methodology of territorial-production complexes, investigated the problems of rational location of productive forces in the Urals. During the pre-war years, the state restored the awarding of academic degrees, including in economic sciences, which also stimulated the development of this scientific field. Regional economics as a new scientific discipline grew roots in the Ural region, extended its subject, and its research paradigm went far beyond the borders of material production and location of productive forces.

Based on an analysis of academic studies, analytical reports, journalistic retrospectives, and regulatory documents, the article proposes a social-historical periodization of the development of IT entrepreneurship in Ukraine from 1991 to the early 2020s. The research is grounded in the approach of social history, which makes it possible to consider IT entrepreneurship not only as an economic or technological activity, but as part of broader social transformations. The focus is placed not merely on quantitative indicators of industry growth, but primarily on changes in the organization of labor, professional practices, forms of interaction, and social perceptions. Within the proposed periodization, five main stages are identified, reflecting the sequential evolution of IT entrepreneurship in Ukraine. These stages encompass the transition from isolated initiatives and the first IT firms of the 1990s, which operated under conditions of institutional uncertainty and limited access to markets, to the formation in the 2010s of a structured industry with established organizational models, a developed community infrastructure, and a growing product segment. The study traces a shift in entrepreneurial logic-from service-oriented models to the emergence of companies that create their own technological products and integrate into global value chains. The analytical focus of the article is concentrated on several interrelated dimensions: the evolution of organizational structures of IT companies, the transformation of professional roles and labor models, the formation of an internal culture of the IT sector, as well as the development of external perceptions of IT professionals as a distinct social group. It is shown that the development of IT entrepreneurship in Ukraine was uneven and largely determined by a combination of global market influences and internal social factors, in particular professional self-organization and horizontal ties within the community. The proposed social-historical periodization does not claim to be definitive; however, it provides an analytical framework for further research on the history of IT entrepreneurship in Ukraine and may be used for in-depth analysis of specific stages, practices, and social transformations associated with the development of the digital economy.