Search in Publications
Found 6273 publications
A machine learning analysis of sustainable development: the case of the Harmonic Development Index
Publication Name: Sustainable Futures
Publication Date: 2026-06-01
Volume: 11
Issue: Unknown
Page Range: Unknown
Description:
Sustainable development requires multidimensional assessment beyond GDP, as nations similar in economic performance often diverge in environmental resilience, social equity, financial robustness, and demographic conditions. This study utilizes advanced machine learning methods on the Harmonic Development Index (H2DI), an integrative composite indicator covering economic, financial, environmental, social, demographic, and knowledge-based dimensions. Employing a Self-Organizing Map (SOM), we identify topology-preserving clusters, visualizing nuanced country proximities and sustainability trade-offs beyond traditional linear models. Complementarily, a Bayesian network uncovers conditional dependencies among sustainability pillars, highlighting critical pathways influencing national development trajectories. Our approach addresses common limitations of PCA and k-means methods by capturing nonlinearities and providing probabilistic insights into sustainability dynamics. Results reveal consistent patterns, robust economic and financial sustainability correlate positively with social resilience and knowledge capacity but inversely with demographic vitality. Temporal robustness checks from 2005 to 2023 affirm stability of these relationships despite global shocks, validating the framework’s applicability for sustainable policy guidance.
Open Access: Yes
Soluble factors from Aspergillus fumigatus promote NF-κB/AKT/ERK activation and pro-tumor phenotypes in lung cancer cells in vitro
Publication Name: Archives of Microbiology
Publication Date: 2026-06-01
Volume: 208
Issue: 6
Page Range: Unknown
Description:
Role of environmental fungi and Aspergillus fumigatus in respiratory diseases remains evident; however, its contribution to directly influencing lung cancer progression remains obscure. In this study, we investigated the effect of Aspergillus fumigatus extract (AFE) on the development of tumor-promoting characteristics in human lung cancer cell lines. The organism was distinguished based on Lactophenol Cotton Blue staining and further distinguished with protein expression patterns via SDS-PAGE and BCA analysis. A549 and H1299 lung adenocarcinoma human cell lines were challenged with AFE, and various cellular responses were monitored simultaneously for cell viability, proliferative activity, inflammatory gene expression, DNA damage expression, and migratory responses. AFE caused increased cell viability and exhibited cellular characteristics of highly proliferating cells with significant expression of Cyclin D1 and c-MYC. Highly inflammatory gene expression responses and protein expressions of AKT, ERK1/2, and NF-κB signaling pathways were noticed at both the gene and protein expression levels with NF-κB nuclear translocation verified with confocal microscopy studies. DNA damage expression markers like γ-H2AX, p-ATM, and p53 significantly contributed with observable genomic DNA cleavage. Additionally, AFE-exposed cells exhibited faster wound closure and expression of Epithelial-mesenchymal transition-associated factors contributing to cell migration and therapeutic efficacy of this combined approach needs further investigation and development into a targeting therapeutic agent against lung cancer.
Open Access: Yes
Mapping the scholarly literature on the infodemic using topic modelling
Publication Name: Social Sciences and Humanities Open
Publication Date: 2026-06-01
Volume: 13
Issue: Unknown
Page Range: Unknown
Description:
The present study aims to map the scholarly evolution of the infodemic as a research subject through a scientometric analysis of 852 peer-reviewed articles indexed in Web of Science between 2020 and 2024 building on scientometric methods and Structural Topic Modeling (STM). Findings reveal a sharp rise in publications during the pandemic years, peaking in 2022, followed by a remarkable decline in both output and citation impact. The STM uncovered 20 distinct topics, with dominant themes centred on health communication, misinformation, social media, and institutional trust. While several themes peaked early in the pandemic, others, such as institutional or public trust, gained prominence later. Topic correlations showed dense interlinkages but low modularity suggested conceptual fragmentation and weak field consolidation. The results highlight that infodemic scholarship remains an emergent, interdisciplinary domain, however, there is a need for stable theoretical foundations.
Open Access: Yes
Who gets to use ChatGPT? A global study on digital access and inequality in higher education
Publication Name: Social Sciences and Humanities Open
Publication Date: 2026-06-01
Volume: 13
Issue: Unknown
Page Range: Unknown
Description:
This study examines how national-level digital development – measured by the ICT Development Index (IDI) – affects university students’ use of ChatGPT. Special emphasis is placed on mediating factors that may influence this relationship, including technical access, institutional and linguistic support, and individual background characteristics, particularly in relation to educational equity and sustainability (SDG 4). The analysis is based on survey data from 20,242 students across 58 countries and applies multivariate statistical methods, including logistic regression, PLS-SEM modeling, and cluster analysis. The results indicate that students in countries with higher IDI scores are more likely to use ChatGPT, primarily because of more advanced digital competencies and greater technological access. The country of study proved to be a stronger predictor than citizenship, underscoring the key role of the local educational environment. Functional access emerged as the most decisive mediating factor, while institutional and linguistic support had a more indirect effect on usage. Cluster analysis identified three distinct student profiles and highlighted that a high level of digital infrastructure alone does not ensure the widespread adoption of generative AI tools. The study proposes a multi-level interpretive framework: at the macro level, national digital infrastructure; at the meso level, institutional and linguistic support; and at the micro level, individual characteristics – connected by functional access as a mediating dimension. This context-sensitive approach contributes to a more comprehensive and practice-oriented understanding of digital inequalities and the integration of generative AI in higher education, offering guidance for promoting inclusive and sustainable technology use.
Open Access: Yes
Linking digital platform design to circular supply chains: Evidence from knowledge sharing and collaboration mechanisms
Publication Name: Technology in Society
Publication Date: 2026-06-01
Volume: 86
Issue: Unknown
Page Range: Unknown
Description:
Digital platforms are increasingly central to circular economy transitions, yet it remains unclear how platform design translates into measurable circular outcomes. This study addresses this gap by developing a socio-technical framework that explains how digital platform information transparency and interoperability enable circular value creation. Drawing on socio-technical systems theory, platform governance, and the dynamic capabilities perspective, the study conceptualizes platform design features as layered affordances that operate through governance and knowledge-based mechanisms. Using survey data from platform-enabled supply chain actors and analysing the model via PLS-SEM, the findings reveal that transparency and interoperability significantly enhance sustainability governance mechanisms, while transparency strengthens knowledge sharing intensity and interoperability supports collaborative decision-making. Sustainability governance mechanisms, in turn, drive both circular business model implementation and circular supply chain performance, whereas knowledge sharing intensity primarily supports business model transformation. Notably, collaborative decision-making does not directly improve circular supply chain performance, suggesting that collaboration without governance alignment remains insufficient. By unpacking the indirect pathways through which platform design shapes circular outcomes, the study advances platform governance and circular supply chain literature. It offers actionable insights for managers and policymakers seeking to design digital ecosystems that move beyond symbolic digitalization toward measurable circular performance and systemic value creation.
Open Access: Yes
Varieties of capitalism and sustainability reporting: Text analysis-based evidence on European companies
Publication Name: Sustainable Futures
Publication Date: 2026-06-01
Volume: 11
Issue: Unknown
Page Range: Unknown
Description:
Sustainability reporting has become a crucial tool for global corporations to disclose environmental, social, and governance (ESG) aspects. This paper examines how corporate sustainability reporting depth varies with firm financial characteristics and with the institutional diversity of European economic systems. Companies are classified using the Varieties of Capitalism (VoC) framework, accounting for the economic differences represented by distinct capitalism models. The quantitative design applies dictionary-based text mining to 2022 and 2023 sustainability reports, using ESRS-aligned scoring to measure disclosure breadth and topic coverage. The sample includes the 20 highest-revenue firms from each VoC group. WordStat-based text analytics generate ESRS topic scores and link disclosure outcomes to firm size, profitability, and leverage. Results indicate systematic cross-VoC differences in ESRS coverage, with higher median disclosure breadth in Social-Democrat Economies (SDE) and Mediterranean Capitalism (MED) settings, thinner coverage more common in Continental European Capitalism (CEC) and Eastern and Central European Capitalism (EAST) settings, and Liberal Market Economies (LME) typically occupying an intermediate position. Regression evidence identifies firm size as the most consistent positive determinant of disclosure breadth, while profitability shows weaker or inconsistent associations and leverage is more often linked to thinner environmental coverage. Findings imply that achieving ESRS comparability across Europe require not only harmonized standards but also institution-sensitive implementation and enforcement capable of reducing persistent cross-country differences in disclosure depth.
Open Access: Yes
Reliability-first, emissions reduction in grid-connected PV-coal systems: Optimal PV integration and coal dispatch under emission caps
Publication Name: Results in Engineering
Publication Date: 2026-06-01
Volume: 30
Issue: Unknown
Page Range: Unknown
Description:
Coal-dependent power systems must reduce cost volatility and emissions while maintaining reliable supply under rising demand. This study assesses whether a practical transition architecture, high-penetration photovoltaic (PV) generation combined with a dispatchable coal unit and grid support, can improve techno-economic and environmental performance without sacrificing feasibility. A grid-connected PV-coal-grid hybrid system was modelled and optimized in HOMER Pro, and a sensitivity campaign was conducted by varying coal fuel price, global horizontal irradiance (GHI), and load demand to test robustness and dispatch shifts. The least-cost feasible solution within the explored design space comprises 145 MW PV and a 75 MW coal power plant with grid interaction. Under baseline conditions, the optimized system achieves a net present cost (NPC) of $632 million and a levelized cost of electricity (COE) of $0.049/kWh. Sensitivity results show that increased GHI consistently reduces NPC and COE, while coal price increases drive greater PV utilization in dispatch without undermining feasibility. Load growth increases total system cost due to higher capital and operating requirements, yet COE changes remain modest, indicating improved utilization of installed assets at higher demand levels. The optimized configuration’s emissions inventory quantifies the residual environmental footprint of the least-cost reliable solution, including 429.2 million kg/yr CO₂, 3.30 million kg/yr SO₂, 0.44 million kg/yr NOₓ, 2.30 million kg/yr CO, 19.7 thousand kg/yr particulate matter, and 122 thousand kg/yr unburned hydrocarbons, reflecting reduced coal combustion through PV displacement during high-resource periods. These findings demonstrate that an optimized PV-coal-grid hybrid can deliver cost-competitive electricity, operational robustness to fuel/resource/demand uncertainty, and measurable multi-pollutant emissions mitigation, offering a realistic transition pathway for coal-reliant systems.
Open Access: Yes
Mathematical Analysis of Real-Time Data Processing Methods for IoT Applications Based on Hesitant Bipolar Fuzzy Dombi Power Operators
Publication Name: Systems and Soft Computing
Publication Date: 2026-06-01
Volume: 8
Issue: Unknown
Page Range: Unknown
Description:
The rapid growth of Internet of Things (IoT) technologies has made real-time data processing a critical component for efficient monitoring, analysis, and intelligent decision-making in dynamic and large-scale environments. IoT systems continuously generate massive volumes of heterogeneous data that must be processed with minimal latency to ensure timely responses and reliable system performance. Effective real-time data processing enables IoT applications to adapt to changing conditions, enhance operational efficiency, improve safety and reliability, and support time-sensitive services in domains such as smart cities, healthcare monitoring, industrial automation, and intelligent transportation systems. This study presents a comprehensive mathematical framework for the analysis of real-time data processing methods for IoT applications based on hesitant bipolar fuzzy (HBF) Dombi power operators. The proposed model is designed to effectively capture uncertainty, hesitation, and bipolar information that naturally arise in real-world IoT environments due to incomplete, imprecise, and conflicting data sources. By incorporating a multi-criteria decision-making (MCDM) approach, multiple real-time data processing techniques are systematically evaluated and prioritized with respect to several performance-related attributes. The proposed HBF Dombi power-based framework offers a reliable and transparent mechanism for comparing competing real-time data processing strategies and selecting the most suitable method for specific IoT scenarios. The results indicate that the proposed approach improves decision accuracy and supports better alignment between data processing methods and the complex operational requirements of modern IoT systems. This work contributes both theoretical insights and practical guidance for the design and evaluation of efficient, adaptive, and intelligent real-time IoT data processing architectures.
Open Access: Yes
Optimal parameter extraction of fuel cells based on interval branch-and-bound optimization algorithm
Publication Name: Energy Reports
Publication Date: 2026-06-01
Volume: 15
Issue: Unknown
Page Range: Unknown
Description:
Fuel cells play an important role in reducing environmental impacts to produce cleaner electricity. Numerical models are used to simulate their performance and build efficient observers in real use. The accuracy of these models is a major concern, as they can be parameterized by several values. Most of the previous works study the estimation of these parameters using various metaheuristics. While these methods are stochastic and do not provide any proof of optimality, the current paper introduces a global optimization method to accurately bound the optimal root mean square error between the parameterized model and some experimental data. The proposed algorithm is based on a deterministic Interval Branch-and-Bound optimization (IBBO) framework. Interval arithmetic ensures set-based computations to safely bound the objective function value. Four types of fuel cells, with their experimental data, are used to demonstrate the efficiency of the proposed methods. IBBO results are compared with some competing optimization methods used in the literature. They show a better accuracy for the computed feasible solutions (upper bounds) and a guaranteed value of the best possible solutions (lower bounds). This last information is not possible to obtain with metaheuristic algorithms. Compared to other Branch-and-Bound algorithms, IBBO proposes a new mix of mechanisms (e.g. advanced constraint propagation, specific search heuristic and feasible point finding method). Due to the deterministic nature of IBBO, results can be repeated. Its convergence analysis is detailed on four fuel cells from which a real test system based on Scribner technology is used to demonstrate the accuracy and robustness of IBBO on several usage scenarios.
Open Access: Yes
