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Found 6273 publications
Decentralized finance and sustainability analysis of global research patterns and emerging themes
Publication Name: Discover Sustainability
Publication Date: 2026-12-01
Volume: 7
Issue: 1
Page Range: Unknown
Description:
Decentralized finance (DeFi) is rapidly transforming financial systems, yet its environmental, social, and economic sustainability implications remain underexplored. To address this gap, we conducted a structured review of peer-reviewed literature published between 2022 and 2025, drawing on 239 records retrieved from Scopus and Web of Science and screened through the PRISMA 2020 protocol in Covidence. The review combined bibliometric analysis, thematic mapping, and a systematic review to synthesize patterns, clusters, and critical insights. Bibliometric results show a sharp post-2023 rise in outputs, with China leading in publication volume and Switzerland achieving the highest citation impact, although collaboration networks remain fragmented and weakly connected. Thematic analysis reveals three dominant clusters: blockchain-driven financial innovation, AI and fintech applications for sustainability, and green economy transitions, highlighting DeFi’s dual role as a driver of transparency and inclusion but also a source of energy inefficiency and systemic risk. The systematic review further identifies regulatory gaps, particularly around Maximal Extractable Value (MEV), and emphasizes the need for energy-efficient consensus mechanisms, standardized ESG metrics for tokenized assets, and inclusive platform designs to bridge digital divides. By aligning DeFi’s disruptive potential with sustainability objectives, the study proposes hybrid governance models and interdisciplinary collaboration to foster a resilient, equitable, and low-carbon financial ecosystem, underscoring the urgency of balancing technological innovation with planetary boundaries to realize DeFi’s promise as a catalyst for sustainable development.
Open Access: Yes
Effect of Spirulina platensis on the content values of wheat bread
Publication Name: Scientific Reports
Publication Date: 2026-12-01
Volume: 16
Issue: 1
Page Range: Unknown
Description:
Due to their nutritional composition, algae are promising ingredients in the development of new foods. The aim of our work was to prepare bread containing Spirulina platensis (new name Arthrospira platensis) in different percentages (0.5, 1.0, 2.5%) within the framework of the MSZ 6369/8-1988 standard, and to determine its content (dry matter, ash, fat, protein/nitrogen, fiber content, carbohydrate content, and polyoxide, color), as well as its texture and color. Furthermore, we assessed consumer opinions through a sensory evaluation. We found that increasing the width and shape fraction while decreasing the height. The results showed that the antioxidant and polyphenols properties of Spirulina-enriched breads increased. The protein, nitrogen, fibre content increased and carbohydrate, energy value properties of Spirulina-enriched breads decreased with increasing concentration of algae. Spirulina powder increased the greenness of the bread and decreased the lightness of the crumb. The hardness, cohesiveness and springiness increased with the addition of Spirulina to bread, while the gumminess and chewiness values became lower compared to the control. Consumer acceptability results showed that the addition of Spirulina at a concentration of 2.5% significantly reduced overall acceptance. Our results indicated that Spirulina cyanobacteria, can be a suitable raw material for making bread, also from the point of view of healthier and sustainable nutrition.
Open Access: Yes
Discrete element modeling of the effect of real-shape ballast angularity on sleeper lateral resistance
Publication Name: Scientific Reports
Publication Date: 2026-12-01
Volume: 16
Issue: 1
Page Range: Unknown
Description:
The mechanical behavior of railroad trackbeds, especially their lateral resistance under dynamic train loads, is significantly influenced by ballast angularity. Using simulations using the Discrete Element Method and realistic particle geometries acquired through 3D scanning, this study examines the function of ballast particle angularity. An Artec Space Spider was used to scan and import five ballast samples into PFC3D, each of which had a unique size distribution and angularity index. To simulate a Single Tie Push Test, a B70 concrete sleeper, which is frequently found in European tracks, was modelled and put through lateral loading. Results for the standard No. 24 ballast gradation were compared with experimental data to validate the simulation framework, and the results indicated a high degree of agreement in the lateral force–displacement behavior. By examining changes in the particle size distribution, ballast degradation was measured, and the resulting Ballast Breakage Index and Breakage Ratio were calculated. Using accepted techniques, lateral resistance was calculated as the area under the displacement curve at 3.5 mm. According to the results, samples with more angular particles had lower degradation and higher lateral resistance. The importance of angularity in stabilising ballast layers under lateral loads was validated by regression analysis. These results offer guidance for better ballast selection and maintenance practices in the field of railway engineering.
Open Access: Yes
Hybrid generative–ensemble approach for predicting recycled aggregate concrete strength properties
Publication Name: Scientific Reports
Publication Date: 2026-12-01
Volume: 16
Issue: 1
Page Range: Unknown
Description:
This study proposes a hybrid generative–ensemble framework to predict key mechanical properties of recycled aggregate concrete from mix proportions. An established database of 112 mixes was used to model compressive strength, split tensile strength, flexural strength, and elastic modulus. To mitigate data scarcity, a conditional variational autoencoder was trained on the training data only and used to generate additional physically plausible input samples, after which seven supervised learning algorithms were trained and compared using cross-validation. Gradient boosting and support vector regression achieved the most accurate and stable predictions across all targets, outperforming baseline linear models and commonly used empirical correlations. Feature-attribution analysis was used to identify the dominant drivers of each property, showing that binder-related variables primarily govern strength, while aggregate-related variables dominate stiffness. The results support practical, data-driven screening of recycled aggregate concrete mixes and provide interpretable guidance for sustainable mix design.
Open Access: Yes
Enhancing heat and mass transfer in hybrid nanofluid with gyrotactic microbes and local thermal non equlibrium effects using artificial neural network
Publication Name: Discover Nano
Publication Date: 2026-12-01
Volume: 21
Issue: 1
Page Range: Unknown
Description:
This study analyzes the impact of local thermal non-equilibrium on the bioconvection flow of hybrid nanofluid across a slender extending sheet containing gyrotactic bacteria using artificial neural networks trained using a Bayesian regularization backpropagation approach (ANN-BRS). The effects of magnetic fields, thermal radiation, and Hall current are all things related to fluid flow. The suggested model has particular applicability in microscale drug delivery systems, where gyrotactic microorganisms and hybrid nanofluid can be employed to control nutrition and medication dispersion under non-equilibrium temperature circumstances. It can be used in lab-on-chip and organ-on-chip technologies to improve bio-mixing and accurate heat control. The model also applies to micro-solar collectors and porous micro-heat exchangers, which use hybrid nanoparticles to boost thermal efficiency. It can also be used in bioreactors and biomedical cooling systems, where local thermal non-equilibrium effects and ANN-based prediction allow for precise control of heat, mass, and microbe transfer, resulting in optimal performance. Similarity transformations are used to convert the original nonlinear PDEs into non-dimensional ODEs and the bvp4c program is applied to numerically resolve the resulting boundary-value problem. The training, testing, and validation processes yield the expected outcomes for every scenario based on the chosen data points. Regression analysis, histograms of error, and mean square error (MSE) metrics are employed to assess the ANN-BRS model's outcome. The liquid phase heat thermal profile increases as the interphase heat transfer parameter values rise, while the solid phase thermal profile decreases.
Open Access: Yes
Categorisation of SDG targets into ESG pillars based on ESRS taxonomy
Publication Name: Discover Sustainability
Publication Date: 2026-12-01
Volume: 7
Issue: 1
Page Range: Unknown
Description:
This study examines the alignment between the Sustainable Development Goals (SDGs) and the Environmental, Social, and Governance (ESG) pillars through the lens of the European Sustainability Reporting Standards (ESRS) taxonomy, complemented by the Global Reporting Initiative (GRI). The research introduces a policy-relevant framework that categorizes SDG targets within ESG pillars, offering structured guidance for policymakers and regulatory bodies to harmonize global sustainability goals with corporate reporting practices. By mapping 199 GRI and 201 ESRS accounting entries to the 17 SDGs, the study identifies significant opportunities to address thematic and procedural gaps in existing reporting systems. The findings demonstrate that SDG 8 (“Decent Work and Economic Growth”) exhibits the highest linkage rate to ESRS accounting items, reinforcing its relevance for policy-driven frameworks that integrate economic resilience with social equity. This harmonized approach underscores the role of policy in fostering alignment between corporate ESG strategies and broader sustainability objectives, mitigating greenwashing risks, and advancing standardization across regions and sectors. The study advocates for policy interventions that leverage this framework to enhance transparency, accountability, and long-term decision-making for sustainable development.
Open Access: Yes
Antimicrobial use and Escherichia coli resistance patterns in Hungarian pig farms: a data-driven farm-level analysis
Publication Name: Scientific Reports
Publication Date: 2026-12-01
Volume: 16
Issue: 1
Page Range: Unknown
Description:
Antimicrobial resistance (AMR) poses a critical challenge to both human and veterinary medicine, with pig production recognized as one of the major contributor due to intensive antimicrobial usage (AMU). This study aimed to explore the relationship between AMU and AMR patterns of Escherichia coli isolated from commercial pig farms, using data-driven analytical methods. Farm-level records were harmonized with microbiological data from 203 isolates collected in December 2023 across four Hungarian farms. AMU was summarized over 3-, 6-, 9-, and 12-month retrospective windows and expressed in modified population-corrected units, while AMR was quantified as mean minimum inhibitory concentration (MIC) and AMR rate under epidemiological and clinical breakpoints. The results revealed substantial variation in AMU among farms, with amoxicillin predominating across timeframes. Farm-specific comparisons indicated that higher AMU may not always coincide with elevated resistance levels, and data analysis did not consistently identify a direct association between use and resistance at the individual farm level, which warrants further investigation in larger datasets. Correlation analyses identified strong intra-class relationships among β-lactams and fluoroquinolones, as well as a cross-class linking, suggesting concurrent selection pressures. Overall, the integration of AMU and AMR data demonstrated the feasibility of farm-level surveillance for AMR modelling and provides a foundation for future predictive systems to support antimicrobial stewardship in livestock production.
Open Access: Yes
Trends and insights from bibliometric analysis for mapping artificial intelligence and machine learning in sustainable development
Publication Name: Discover Sustainability
Publication Date: 2026-12-01
Volume: 7
Issue: 1
Page Range: Unknown
Description:
Rapid population growth, environmental degradation and persistent urgency of climate change have intensified the global search for sustainable development solutions. Governments, researchers and institutions alike face the challenge of balancing economic progress with social equity and environmental protection. In response, recent scholarships have increasingly turned to digital technologies as potential enablers of sustainable transformation. This study addresses the need to understand how artificial intelligence (AI) and machine learning (ML) are being incorporated into sustainable development strategies, with a particular focus on mapping knowledge trends and research patterns. Using bibliometric analysis of SCOPUS data spanning 2015 to 2024, the study uncovers the evolution of research topics, highlights influential authors and institutions, and traces the diffusion of ideas across disciplines. The findings reveal that AI and ML are emerging as key drivers of sustainability, with strong applications in energy and emission management, environmental monitoring, climate change mitigation, precision agriculture and water resource management. Research in this area has grown rapidly over the past decade, shifting from theory to real applications. It also highlights that China's and the United States dual dominance in both publication volume and citation impact, while also recognizing the contributions of other countries like India, the United Kingdom and Australia in shaping global research landscapes. Three main implications arise from these results. For policymakers, the evidence underscores the urgency of designing inclusive policies, investing in digital infrastructure, and fostering global cooperation to ensure the equitable distribution of technological benefits. For the research community, the study points to opportunities for cross-disciplinary collaborations that link technological innovation with real-world sustainability challenges. From a broader societal perspective, the findings emphasize the importance of knowledge sharing and technology transfer, enabling both developed and developing countries to advance collectively toward achieving the Sustainable Development Goals.
Open Access: Yes
Issues in video-based data collection of traffic flows
Publication Name: European Transport Studies
Publication Date: 2026-12-01
Volume: 3
Issue: Unknown
Page Range: Unknown
Description:
Detectors, such as object detectors and motion detectors, are increasingly promising technologies with applications across various industries, including medicine, agriculture, autonomous vehicle driving, and traffic monitoring. They offer significant advantages over traditional image classifiers. One such advantage is their ability to provide detailed and accurate records of traffic activity, crucial for transportation planning and engineering decisions, thus enhancing the safety and efficiency of transportation systems. In this article, we introduce a method utilizing a simple camera to identify moving vehicles, collect data on following distance, and measure speed, offering a low-cost solution for traffic monitoring. This approach has the potential to significantly improve traffic management in both urban and rural areas, addressing the pressing need for efficient transportation systems.
Open Access: Yes
