Search Results (38)

Ukraine’s reconstruction, estimated at $524 billion over the next decade, presents an unprecedented opportunity to embed circular economy principles into industrial rebuilding, but the financial architecture currently deployed for reconstruction is structurally blind to circular outcomes. This paper examines how digital tools and innovative financing mechanisms can channel investment toward circular industrial reconstruction in Ukraine, drawing on Germany’s National Circular Economy Strategy (NCES, adopted December 2024) as a reference model. A comparative institutional analysis combines a documentary review of Ukrainian reconstruction policy frameworks (Ukraine Plan 2024–2027, RDNA4, Ukraine Facility) and German NCES instruments with the construction of a financing−technology pathway typology. Five pathways are proposed: circular bond issuance with Digital Product Passport integration; blended finance with blockchain impact verification; EU Facility conditionality with AI-driven resource management; war risk insurance with circular construction standards; and SME digitalisation credit with circular economy competency building. Each pathway is assessed against five criteria: investment scale, risk mitigation, circular measurement, digital readiness, and institutional feasibility, and applied to four industrial corridors (Dnipro region, Zaporizhzhia region, Kharkiv region, and Donetsk region). The analysis reveals that no single pathway is sufficient; a layered strategy differentiating by region is required. Digital tools, particularly the Digital Product Passport and blockchain traceability, serve as partial substitutes for institutional trust in post-conflict settings, reducing information asymmetry between investors and project operators. The paper contributes a practically oriented framework at the under-theorised intersection of post-conflict reconstruction finance and circular economy scholarship. Full article

National legislative frameworks governing prescribing, pricing, reimbursement, and dispensing play a decisive role in shaping access to medicines. This study examines the financial availability of medicines in Bulgaria and North Macedonia through a comparative review of national pharmaceutical legislation, pricing mechanisms, reimbursement models, and digitalisation policies, assessed in relation to European Union standards. The findings indicate that access to medicines in both countries is shaped by the combined effects of multiple regulatory and financial instruments rather than by individual policy measures. Both systems apply strict control of prescribing and dispensing, external reference pricing, and positive reimbursement lists, reflecting alignment with international recommendations. However, significant differences in policy design lead to divergent access outcomes. Bulgaria’s more advanced digitalisation of prescribing and reimbursement, including mandatory electronic prescribing for selected therapeutic groups, enhances regulatory oversight and expenditure control but is associated with higher patient out-of-pocket expenditure, partly due to the application of the standard value-added tax on medicines. In contrast, North Macedonia combines lower taxation with capped patient co-payments, higher regulated pharmacy margins, and fixed pharmacy remuneration per prescription, contributing to improved financial affordability for patients while supporting pharmacy sustainability. Additional instruments, such as the Generics without Co-Payment List, further strengthen patient financial protection. The study provides comparative evidence relevant to pharmaceutical policy reforms and highlights the importance of balanced regulatory approaches that promote affordability, system sustainability, and equitable access to medicines. Full article

Ensuring the crashworthiness of heavy-duty truck cabs is essential for reducing occupant fatalities and improving passive safety in commercial vehicles. Regulatory frameworks such as UNECE Regulation No. 29 (R29) define structural integrity requirements through full-scale destructive impact tests, which are costly and limit iterative design. In this study, an integrated experimental–numerical methodology is presented for the impact assessment of a real Iveco Eurocargo 120E18 truck cab reconstructed using high-resolution 3D scanning. The scanned geometry was used to generate a dimensionally accurate CAD model of the load-bearing cab structure, which was analysed using explicit finite element simulations in ANSYS Academic Mechanical and CFD Teaching package under impact conditions compliant with UNECE R29 and implemented according to the Swedish regulation VVFS 2003:29. In parallel, a full-scale physical pendulum impact test was performed on the same cab using a cylindrical impactor with a diameter of 580 mm, a length of 1800 mm, and a mass of approximately 1000 kg, impacting the upper region of the A-pillar. The experimental setup was instrumented using high-speed optical measurements and an accelerometer to capture impact kinematics and structural response. The numerical predictions showed good agreement with experimental results in terms of acceleration–time histories, absorbed energy evolution, and structural deformation, with differences generally below 6%. Critical regions susceptible to local buckling and plastic collapse were consistently identified in both approaches, while preservation of the driver survival space was confirmed. The results demonstrate that scan-based finite element models, when properly calibrated and validated, can reliably reproduce certification-level impact behaviour. The proposed workflow provides a robust and cost-effective framework for regulatory pre-validation, structural optimisation, and digitalisation of crashworthiness assessment for heavy-duty truck cabs. Full article

Digitalisation is reshaping shipyard production, yet its methodological foundations remain fragmented across simulation, optimisation, Artificial Intelligence (AI), and Digital Twin (DT) research streams. This paper presents a domain-specific methodological review of shipyard production modelling from 2010 to 2025, synthesising advances in Discrete-Event Simulation (DES), multi-objective optimisation, hybrid simulation–optimisation architectures, Machine Learning (ML), reinforcement learning (RL), and DT-enabled cyber-physical systems. Using an explicit evaluative framework based on integration depth, validation basis, and decision scope, the review differentiates between analytically mature but execution-decoupled DES/optimisation approaches and integration-rich yet variably validated DT and AI-driven systems. The analysis shows that hybrid DES-optimisation frameworks currently represent the most operationally credible class of methods, delivering measurable production improvements under structured conditions, whereas many DT and AI contributions prioritise architectural integration and data synchronisation over longitudinal yard-wide KPI validation. A comparative assessment of simulation platforms, optimisation engines, and manufacturing execution system/enterprise resource planning/product lifecycle management infrastructures highlights the central role of structured product–process–resource data and execution-layer connectivity, while severe confidentiality constraints and the scarcity of openly available industrial datasets continue to limit reproducibility and benchmarking. Overall, shipyard production research is progressing toward increasingly integrated and cyber-physical systems, but sustained yard-scale validation and shared benchmark development remain critical prerequisites for translating architectural sophistication into demonstrable operational impact. Full article

The rule of law, a foundational value of the European Union as enshrined in Article 2 of the Treaty on European Union, faces challenges in implementation due to historical and political factors that have evolved over the past decade, particularly within Member States in the administrative domain. While institutional backsliding in countries like Hungary and Poland has drawn significant political attention, less emphasis has been placed on the role of public administrations in upholding or undermining the rule of law on a day-to-day basis. This paper argues that the sustainability of the rule of law in the EU requires more than legal compliance mechanisms. These alone do not address the underlying administrative and cultural factors necessary for effective implementation. Instead, it requires closer attention to how rule-of-law principles are embedded in the everyday functioning of public administrations. This argument is informed by the authors’ systematic examination of recent EU monitoring practices and administrative reform instruments. Adopting a mixed conceptual-empirical methodology, the paper draws on primary data from EU Rule of Law Reports (2020–2024), the EU Justice Scoreboard, the Recovery and Resilience Facility (RRF), and the Technical Support Instrument (TSI), complemented by relevant OECD/SIGMA indicators. Several structural obstacles emerge from the analysis. These include symbolic compliance, whereby organisations adopt formal structures without corresponding behavioural change; weak institutional leadership that fails to drive reform momentum; and the absence of integrated performance metrics, which hampers meaningful accountability. Fragmented ownership of reform agendas, in turn, breeds inconsistency in implementation. These challenges point to the limitations of a technocratic or legalistic approach to rule-of-law governance. Strategic leadership and organisational flexibility emerge from the evidence as preconditions—not merely facilitators—of genuine internalisation, though the relationship is context-dependent. Digitalisation can reinforce these dynamics, yet its contribution depends on whether it is embedded within broader integrity-oriented reforms. The paper advocates for a shift from externalized compliance mechanisms to a model that emphasizes administrative ownership through specific strategies such as developing integrity-based leadership programs and embedding governance practices that prioritize transparency and accountability. It proposes concrete institutional reforms, including performance-linked conditionalities that tie funding to measurable outcomes, ethical leadership academies to train future leaders, integrity audits to ensure accountability, and administrative benchmarking to set clear standards, as tools to foster autonomous, value-driven public institutions capable of adapting to evolving governance challenges while maintaining core democratic values. Full article

Battery energy storage systems (BESSs) play a vital role in contemporary smart grids, but their increasing digitalisation exposes them to sophisticated cyberattacks. Existing anomaly detection approaches typically treat sensor measurements as flat feature vectors, overlooking the intrinsic relational structure of cyber–physical systems. This work introduces an enhanced Graph Neural Network (GNN) autoencoder for unsupervised BESS anomaly detection that integrates multiscale graph construction, multi-head graph attention, manifold regularisation via latent compactness and graph smoothness, contrastive embedding shaping, and an ensemble anomaly scoring mechanism. A comprehensive evaluation across seven BESS and firmware cyberattack datasets demonstrates that the proposed method achieves near-perfect Receiver Operating Characteristic (ROC) and Precision–Recall Area Under the Curve (PR AUC) (up to 1.00 on several datasets), outperforming classical one-class models such as Isolation Forest, One-Class Support Vector Machine (One-Class SVM), and Local Outlier Factor on the most challenging scenarios. These results illustrate the strong potential of graph-informed representation learning for cybersecurity monitoring in distributed energy resource infrastructures. Full article

The goal of this scientific study is to clarify whether and how digital–intelligent integration contributes to urban carbon efficiency and to identify the conditions under which this contribution becomes nonlinear and policy-relevant. Focusing on 39 prefecture-level cities in the middle reaches of the Yellow River Basin during 2011–2022, we adopt an integrated measurement–modelling approach that combines efficiency evaluation, machine-learning interpretation, and dynamic–spatial validation. Specifically, we construct two super-efficiency DEA indicators: an undesirable-output SBM incorporating CO₂ emissions and a conventional super-efficiency CCR index. We then estimate nonlinear city-level relationships using XGBoost and interpret the marginal effects with SHAP, while panel vector autoregression (PVAR) and spatial diagnostics are employed to validate the dynamic responses and spatial dependence. The results show that digital–intelligent integration is positively associated with both carbon-related and conventional efficiency, but its marginal contribution is strongly conditioned by human capital, urbanisation, and environmental regulation, exhibiting threshold-type behaviour and diminishing returns at higher digitalisation levels. Green efficiency reacts more strongly to short-run shocks, whereas conventional efficiency follows a steadier improvement trajectory. Heterogeneity across urban agglomerations and evidence of spatial clustering further suggest that uniform policy packages are unlikely to perform well. These findings highlight the importance of sequencing and policy complementarity: investments in digital infrastructure should be coordinated with institutional and structural measures such as green finance, environmental standards, and industrial upgrading and place-based pilots can help scale effective digital applications toward China’s dual-carbon objectives. The proposed framework is transferable to other regions where the digital–climate nexus is central to smart and sustainable urban development. Full article

Lentigo maligna (LM) is a melanoma in situ with high cumulative sun damage. Histological evaluation of resection margins is difficult and time-consuming. Melanocyte density (MD) is a suitable, quantifiable, and reproducible diagnostic criterion. In this retrospective single-centre study, we investigated whether an artificial intelligence (AI) tool can support the assessment of LM. Training and evaluation were based on MD in Sox-10-stained digitalised slides. In total, 86 whole slide images (WSIs) from LM patients were annotated and used as a training set. The test set consisted of 177 slides. The tool was trained to detect the epidermis, measure its length, and determine the MD. A cut-off of ≥30 melanocytes per 0.5 mm of epidermis length was defined as positive. Our AI model automatically recognises the epidermis and measures the MD. The model was trained on nuclear immunohistochemical signals and can also be applied to other nuclear stains, such as PRAME or MITF. The WSI is automatically visualised by a three-colour heat map with a subdivision into low, borderline, and high melanocyte density. The cut-offs can be adjusted individually. Compared to manually counted ground truth MD, the AI model achieved high sensitivity (87.84%), specificity (72.82%), and accuracy (79.10%), and an area under the curve (AUC) of 0.818 in the test set. This automated tool can assist (dermato) pathologists by providing a quick overview of the WSI at first glance and making the time-consuming assessment of resection margins more efficient and more reproducible. The AI model can provide significant benefits in the daily routine workflow. Full article

The shadow economy weakens fiscal sustainability, hampers the financing of public goods, and impedes the achievement of sustainable development goals. The informal sector remains a persistent challenge for policymakers, as it distorts competition, reduces transparency, and undermines the effectiveness of economic and fiscal policies. The aim of this article is to identify the key factors determining the size of the shadow economy in European Union countries and to provide policy-relevant insights. The analysis covers data on the share of the informal economy in GDP and macroeconomic variables such as GDP per capita, consumer price index, average wages, household consumption, government expenditure, and unemployment, as well as indicators of digital development in society and the economy (DESI, IDT), the share of cashless transactions in GDP, and information on the implementation of digital tax administration tools and restrictions on cash payments. Five hypotheses (H1–H5) are formulated concerning the effects of income growth, labour market conditions, digitalisation, cashless payments, and tax administration tools on the shadow economy. The research question addresses which factors—macroeconomic conditions, economic and social digitalisation, payment structures, and fiscal innovations in tax administration—play the most significant role in determining the size of the shadow economy in EU countries and whether these mechanisms have broader implications for fiscal sustainability and sustainable development. The empirical strategy is based on multilevel models with countries as clusters, complemented by correlation and comparative analyses. The results indicate that the most significant factor in limiting the size of the shadow economy is the level of GDP per capita and its growth, whereas the impact of card payments appears to be superficial, reflecting overall increases in wealth. Higher wages, household consumption, and digital development as measured by the DESI also play an important role. The implementation of digital solutions in tax administration, such as SAF-T or e-PIT/pre-filled forms, along with restrictions on cash transactions, can serve as complementary measures. The findings suggest that sustainable strategies to reduce the shadow economy should combine long-term economic growth with digitalisation and improved tax administration, which may additionally foster the harmonisation of economic systems and support sustainable development. Full article

This study presents a methodology for the digitalisation process for analysing transient flow phenomena in a U-tube. It comprises several layers, including the characterisation of liquid oscillation dynamics, image segmentation for experimentally determining variations in the meniscus position, and the integration of machine learning techniques with analytical solutions. The position, velocity, and acceleration of the meniscus are obtained using image-processing methods and subsequently compared with the corresponding analytical predictions. The proposed methodology accurately represents the existing hydraulic conditions, incorporating both Newtonian and Ogawa friction models. To assess model performance, the index of agreement was employed to compare analytical and experimental results. The findings indicate a systematic error of 2.2 mm ± 3 pixels when using the Ogawa friction model, which corresponds to the best model for predicting this hydraulic behaviour. Finally, the implementation of machine learning techniques demonstrates considerable potential for predictive analysis, with statistical measures showing coefficients of determination above 0.997 and consistently low Root Mean Square Error values. Full article

In the present research paper, we argue that digital transformation and students’ behavioural trends are intertwined through the increasing integration of digital technology tools into both academic and personal communication contexts. We construct our argument through a rigorous methodological approach employing quantitative and qualitative analyses, including ANOVA, Kruskal–Wallis, chi-squared, and multiple regression models, to assess the main predictors of digital communication satisfaction and engagement. Hence, we emphasise distinctly the diverse roles of digital communication platforms as relating to the expression of students’ engagement and adaptability, beyond mere technological adoption. While investigating the role of behavioural economics in modelling students’ engagement with digital technologies, we examine the impact of digitalisation on students’ communication patterns, both in terms of personal and academic purposes. To measure student engagement, we employ a mixed-methods approach by carrying out a pilot study (N = 167). The findings underline the role of digital transformation in enhancing students’ access to learning, communication, and collaboration tools, while they also align with Sustainable Development Goal 4 (Quality Education). Our intention is to develop a more comprehensive model that integrates behavioural insights with technology acceptance theories, while another further direction could be exploring longitudinal data to assess the long-term impacts of digital tools on student engagement and learning outcomes. Full article

With advancement in Unmanned Aerial Vehicle (UAV) photogrammetry, productivity in construction management can now be achieved with accuracy and is less labour-intensive. In the basement construction of buildings, prudent earthwork activities are often necessary, setting the basis of the building footprint. As such, monitoring earthwork volume estimation becomes important to avoid over- or under-cutting the earth. Conventional methods by means of land surveying are time-consuming, labour-intensive, and susceptible to varying degrees of accuracy. Moreover, earthwork sites often have multiple activities ongoing that increase the complexity of volume estimation through land surveying. This study explores the use of UAV photogrammetry to estimate earthwork excavation volume in a complex urban earthwork site in Singapore over time and discusses the feasibility, challenges and productivity enhancements of integrating the technology into the construction process. In this study, the earthwork site and controlled trials show that the models reconstructed with UAV photogrammetry data can produce volume measurements that fulfil the stakeholder’s accuracy tolerance of 5% between the estimated and actual volume. The filtering of unwanted objects in the model, such as columns, cranes and trucks, was successful but was insufficient for objects that occluded large areas of the soil surface. The integration of UAV photogrammetry with a highly automated acquisition and processing workflow for earthwork monitoring brings about productivity enhancements in time and labour efforts and improves the efficiency and consistency of models. Furthermore, the digitalisation of earthwork sites into point clouds and three-dimensional (3D) models increases data visualisation and accessibility, facilitates project team collaboration, and enables cross-platform compatibility into Building Information Modelling (BIM), which can significantly aid in reporting and decision-making processes. Full article

The COVID-19 pandemic offers an opportunity for the revitalisation of empty Spain and the development of new sustainable business models in a healthier environment, taking the competitive advantages of digitalisation and the benefits of contact with nature. This study presents a positive analysis of the situation after three months of confinement with the research objective of evaluating the potential for development a sustainable social economy in empty Spain based on the hypotheses presented. In order to demonstrate the six hypotheses put forward in the research, a review of the existing literature was conducted, socio-economic and environmental indicators from official sources were consulted, and descriptive statistics methods have been applied. Digitalisation, the social economy, the bio-economy, and the revitalisation of heritage seem to be the drivers for achieving the challenges proposed. By perceiving reality through a lens that values nature and creative intelligence, a new avenue of opportunities may be opened up, leading to an improvement in quality of life and well-being, and potentially retaining the rural population. Following this study, which assesses the opportunities, risks, and challenges and establishes a plan of measures, players, and resources for future implementation in vacant Spain, new lines of work will become available. Full article

As businesses increasingly adopt digital processes and solutions to enhance efficiency and productivity, they face heightened cybersecurity threats. Through a systematic literature review and concept development, this article examines the intersection of digitalisation and cybersecurity. It identifies the methodologies and tools used for cybersecurity assessments, factors influencing the adoption of cybersecurity measures, and the critical success factors for implementing these measures. The article also puts forward the concept of cybersecurity governance process categories, which are used to classify the factors uncovered in the research. Findings suggest that current information security standards tend to be too broad and not adequately tailored to the specific needs of small and medium-sized enterprises (SMEs) when implementing emerging technologies, like Internet of Things (IoT), blockchain, and artificial intelligence (AI). Additionally, these standards often employ a top-down approach, which makes it challenging for SMEs to effectively implement them, as they require more scalable solutions tailored to their specific risks and limited resources. The study thus proposes a new framework based on the Plan-Do-Check model, built around the cybersecurity governance process categories and the three core pillars of governance, culture and standards. This is essentially a bottom-up approach that complements current top-down methods, and will be of value to both information technology (IT) professionals as an operational guide, and to researchers as a basis for future research in this field. Full article

The scientific community has shown considerable interest in Industry 4.0 due to its capacity to revolutionise the manufacturing sector through digitalisation and data-driven decision-making. However, the actual implementation of Industry 4.0 within complex industrial settings presents obstacles that are typically beyond the scope of mainstream research articles. In this paper, a comprehensive case-study detailing our collaborative partnership with a leading medical device manufacturer is presented. The study traces its evolution from a state of limited digitalisation to the development of a digital intelligence platform that leverages data and machine learning models to enhance operations across a wide range of critical machines and assets. The main business objective was to enhance the energy efficiency of the manufacturing process, thereby improving its sustainability measures while also saving costs. The project encompasses energy modelling and analytics, Fault Detection and Diagnostics (FDD), renewable energy integration and advanced visualisation tools. Together, these components enable informed decision making in the context of energy efficiency. Full article