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Found 6273 publications
State-dependent predictability of precious metals: The economic role of critical minerals and climate risk
Publication Name: Gondwana Research
Publication Date: 2026-06-01
Volume: 154
Issue: Unknown
Page Range: 274-289
Description:
The current study investigates the predictive ability of critical minerals of price returns of precious metals (gold, silver, platinum, and palladium) in presence of different degrees of climate policy uncertainty (CPU). Using a novel Multivariate Quantile-on-Quantile Causality (MQQC) model, we test the predictive dynamics, unconditional and CPU-conditional, in the entire joint return distribution continuum. Predictability, unconditionally, is localized in the tails, i.e. under extreme market conditions, mineral shocks have strong impact but under normal regimes, they have little impact. The tail dependence is indicative of co-production and industrial-demand relationships of silver, platinum, and palladium, but gold mostly maintains its safe-haven property. After the addition of CPU, the predictive effects are stretched out further to the middle of the distribution, indicating wider and more enduring spillovers. In the case of gold, CPU enhances the safety haven demand by augmenting the crucial mineral precious metal co-movements between regimes. In the case of silver, platinum and palladium, CPU increases industrial sensitivities relating to clean-energy use. These findings highlight the twofold contribution of the geological factors in conjunction with policy uncertainty towards price fluctuations, and significance of the findings on resource planning, governance and risk management.
Open Access: Yes
Galerkin finite element analysis of trihybrid nanofluid flow in porous corrugated cavities with thermal radiation and ANN validation
Publication Name: Results in Engineering
Publication Date: 2026-06-01
Volume: 30
Issue: Unknown
Page Range: Unknown
Description:
This work tackles the issue of enhancing heat transmission and minimizing entropy formation in tiny enclosures pertinent to thermal energy storage. It looks at how magnetohydrodynamic (MHD), non-Darcian porous media, a ternary hybrid nanofluid composition (Fe3 O4 –hBN–CuO/water), and triangle corrugation work together in a corrugated rectangular cavity. The goal is to figure out how these things affect convection, entropy formation, and the overall efficiency of the thermodynamic system. Utilizing the Galerkin finite element technique (GFEM), we found numerical solutions to the mathematical models for momentum, energy, and entropy generation. The effects of the porosity parameter, ternary nanoparticle concentration, Hartmann number, Darcy number, and Rayleigh number were carefully studied for the cavities' flat and triangular corrugated walls. Artificial Neural Network (ANN) model was developed and trained to predict the average Nusselt number and total entropy generation with high precision, using fewer computational resources compared to conventional CFD approaches. It is observed that the ANN model is used mostly as an ancillary prediction instrument derived from FEM-generated data, rather than as the principal computational framework. The results show that corrugated shapes improve local heat transfer by increasing the surface area and causing flow disruptions. However, too many corrugations lower the average Nusselt numbers because they cause recirculation. Higher Rayleigh numbers make buoyancy-driven convection stronger, whereas larger magnetic fields make circulation weaker, which makes conduction-dominated transport more likely and lowers entropy generation. The porosity and Darcy number have a big effect on convective intensity and entropy formation. On the other hand, the right number of nanoparticles may boost thermal conductivity without making irreversibility too high. The ANN model showed great prediction ability (MSE≈1.12 × 10⁻⁷), which proved that it works well for quickly testing Multiphysics systems. These results show that integrating ternary nanofluids, controlling porous media, and changing the magnetic field may improve thermal performance in advanced applications, including solar collectors, cooling electronics, and thermal energy storage devices. Combining ANN prediction gives us a solid base for designing and improving next-generation heat management solutions in a way that works well.
Open Access: Yes
Leveraging Forest Resources, Green Energy, and Digitalization: Contextual Evidence Apropos Sustainable Growth in the Lens of Climate Resilience Policies
Publication Name: Land Degradation and Development
Publication Date: 2026-05-30
Volume: 37
Issue: 9
Page Range: 4411-4425
Description:
Sustainable forest landscape, digitalization, and green energy are the core pillars of the European Union's policies to achieve sustainable growth; however, their impacts are divergent due to variant regional forest management policies, economic structures, and digital transformation. This study contributes to the literature by uniquely evaluating the asymmetric impacts of forest access, green energy, and digitalization on sustainable growth across the EU countries from 1991 to 2022. It introduces the moderating role of digitalization on forest access, a dimension that has been unexplored previously. This analysis employs the method of moments quantile regression (MMQR) to address the slope heterogeneity and cross-sectional problems. The outcomes exhibit that green energy and digitalization are the drivers of sustainable growth, while their effects are pronounced at higher and lower growth quantiles, respectively. In contrast, forest access inhibits economic sustainability, with larger impacts realized in high-growth economies. The interaction term indicates that the applications of digital technologies in the forest landscape significantly support sustainable growth. The robustness analysis confirms the consistency of regression outcomes. These insights offer novel implications for EU climate and digital policy integration under the Green Deal and REPower EU agenda.
Open Access: Yes
DOI: 10.1002/ldr.70413
Optimizing landing mechanics to modulate patellar tendon loading: An individualized moment arm analysis
Publication Name: Iscience
Publication Date: 2026-05-15
Volume: 29
Issue: 5
Page Range: Unknown
Description:
Single-leg landing (SL) imposes substantial mechanical demand on the patellar tendon, with peak patellar tendon force (PPTF) serving as a key metric for characterizing the internal mechanical environment of the tendon. This study integrates 3D modeling with high-resolution in vivo kinematics to quantify the patellar tendon moment arm (PTMA) and the PPTF, examining their biomechanical correlations and neuromuscular features. Minimal sex-related PTMA differences suggest comparable anatomical leverage during knee flexion across both sexes. In both sexes, PPTF was significantly positively correlated with the knee flexion angle at initial contact (IC) and significantly negatively correlated with the knee range of motion (ROM). Muscle network analysis showed lower clustering coefficients in high-frequency versus low-frequency bands. Reduced IC knee flexion and increased ROM attenuate patellar tendon mechanical demand. By incorporating individualized moment-arm analysis, this study provides a biomechanical basis for understanding patellar tendon loading during landing.
Open Access: Yes
Human Perceptions of Reliability of Autonomous Drone Systems Under Dynamic Disturbances
Publication Name: Applied Sciences Switzerland
Publication Date: 2026-05-01
Volume: 16
Issue: 9
Page Range: Unknown
Description:
This study analyzes how dynamic disturbances influence the decisions made during the human supervision of autonomous unmanned aerial vehicles. While previous research has primarily focused on control algorithms and system stability, the effect of disturbances originating from system dynamics on operator intervention behavior has been less extensively investigated. To examine this problem, a hardware-in-the-loop (HIL) experimental framework was developed, which is based on a previously validated unmanned aerial vehicles (UAVs) test platform and was adapted in this study to enable the investigation of human supervisory decision-making. Participants observed the behavior of an autonomously operating system under controlled disturbances and were provided with the possibility to intervene by activating an emergency landing mechanism. The results indicate that the disturbance intensity had a significant effect on intervention decisions, while the reaction times did not show notable differences. This finding suggests that supervisory behavior is primarily determined by the evaluation of the system state rather than by timing characteristics. It also identifies that subjective risk perception plays a decisive role in the formation of intervention decisions, indicating the presence of an implicit decision threshold for participant behavior. The research findings offer a novel approach to the interpretation of human–UAV interaction by emphasizing the role of system dynamics in shaping user decisions. The presented method may provide a foundation for the development of predictive and adaptive supervisory systems that take into account the characteristics of human decision-making, thereby contributing to the design of safer and more efficient autonomous systems.
Open Access: Yes
DOI: 10.3390/app16094353
Evaluating the role of blue-green infrastructures in mitigating climate change: a case study of the Hungarian “Green City” program
Publication Name: Clean Technologies and Environmental Policy
Publication Date: 2026-05-01
Volume: 28
Issue: 5
Page Range: Unknown
Description:
Urban environments are increasingly vulnerable to climate change, with extreme weather events expected to become more frequent and severe. This paper addresses sustainable urban development and the importance of stormwater retention, integrating adaptation and mitigation strategies. It evaluates the publicly funded Hungarian “Green City” program’s water management, focusing on blue-green infrastructures. The 198 implemented projects in the program were assessed for green credentials, vegetation concepts, and rainwater retention using public databases of real municipal data and Google Earth spatial analyses rather than hypothetical scenarios. A lifetime climate change impact assessment with sensitivity analysis was conducted using two case studies from the “Green City” program, highlighting the benefits of prioritizing rainwater over tap water for irrigation. The study proposes a three-pillar—environmental as operational carbon footprint, economic as extended net present value (NPV), and social as accessibility and recreational benefit—evaluation method for urban blue-green developments. It found that many projects rely on tap water irrigation, thus resulting in higher lifetime carbon emissions. The financial assessment of carbon footprint within the extended NPV method emphasizes the need for improved green area irrigation strategies. By modernizing irrigation practices and implementing effective rainwater retention measures, blue-green infrastructures can significantly reduce carbon dioxide emissions while improving long-term economic performance and social benefits through improved usability. The research offers valuable insights into the role of blue-green infrastructures in urban development to combat climate change. The combined three-pillar framework integrating LCA to assess green projects is a transferable decision-support tool that can be adapted to locally available data, advocating the use of rainwater over tap water to achieve environmental, social, and economic benefits. Unlike earlier studies that used hypothetical scenarios, this research relies on the implemented projects of the “Green City” development program with their observed designs and available real data, thus providing a framework for urban blue-green implementations to integrate sustainable practices and effectively address the challenges posed by climate change.
Open Access: Yes
Financial Supervision for the Green Transition: Comparative Insights From the EU, Hungary, and Singapore
Publication Name: Thunderbird International Business Review
Publication Date: 2026-05-01
Volume: 68
Issue: 3
Page Range: 357-366
Description:
This paper examines how financial supervisory authorities integrate Environmental, Social, and Governance (ESG) objectives into their regulatory mandates amid the accelerating green transition. It aims to understand how institutional variation shapes supervisory strategies for sustainable finance. The study employs a qualitative, comparative case study design across three jurisdictions: the European Union, Hungary, and Singapore. Drawing on regulatory theory and document analysis, this study identifies the key institutional logics, instruments, and governance mechanisms through which ESG considerations are embedded in financial supervision. The analysis reveals three supervisory models: the EU's rule-based legal harmonization through taxonomy and disclosure mandates, Hungary's responsive approach led by the central bank using soft tools and innovation, and Singapore's principle-based framework emphasizing strategic guidance and market collaboration. These pluralistic pathways highlight that ESG integration is shaped by legal mandates, legitimacy concerns, and adaptive governance. This study provides insights to policymakers and supervisors seeking to align financial oversight with sustainability objectives. This emphasizes the importance of institutional flexibility, regulatory legitimacy, and hybrid governance in designing effective ESG supervision frameworks. This study contributes to the literature on sustainable finance and regulatory governance by offering a comparative perspective on how financial supervision evolves in response to ESG risks. It advances a novel typology of supervisory models that can inform future regulatory design and policy debates.
Open Access: Yes
DOI: 10.1002/tie.70039
Digital Transformation and Sustainable Visitor Engagement in Zoological Parks: A Systematic Review and Conceptual Framework
Publication Name: Sustainability Switzerland
Publication Date: 2026-05-01
Volume: 18
Issue: 9
Page Range: Unknown
Description:
Zoological parks increasingly operate as sustainability-oriented institutions that integrate biodiversity conservation, environmental education, animal welfare, and community engagement. In parallel with these evolving roles, digital and interactive technologies have emerged as key instruments for supporting sustainable visitor engagement and conservation communication. This study provides a systematic review and conceptual mapping of the literature by combining a PRISMA-based systematic literature review with bibliometric co-word analysis. The bibliometric results reveal four thematic clusters: (1) mobile and visitor-oriented digital technologies, (2) immersive augmented reality (AR) or virtual reality (VR) based solutions, (3) animal–computer interaction and welfare-focused technologies, and (4) traditional conservation and education research. While digital technologies demonstrate measurable short-term effects on engagement, empathy, and knowledge retention, their long-term sustainability impact and systemic integration remain limited. To address this gap, the study introduces three theoretical contributions: the concept of the zoo-based digital learning ecology, the identification of the digital fragmentation problem, and the Integrated Digital Zoo Ecosystem (IDZE) model. The proposed framework conceptualizes digital visitor experience, animal welfare technologies, and conservation communication as interdependent subsystems within a unified sustainability-oriented ecosystem. This study provides a conceptual foundation for future sustainability-driven digital innovation in zoological parks.
Open Access: Yes
DOI: 10.3390/su18094336
Addressing the Impact of Resolution Scaling on YOLO Performance for Brain Tumor Detection Through Optimized Network Depth/Width Adjustments
Publication Name: Applied Sciences Switzerland
Publication Date: 2026-05-01
Volume: 16
Issue: 9
Page Range: Unknown
Description:
Deep learning-based object detectors, particularly You Only Look Once (YOLO) architectures, have demonstrated strong performance in automated brain tumor detection. However, the impact of resolution scaling on tumor localization accuracy remains underexplored, especially under conditions where image resolution is reduced. This study aims to investigate how lowering the input resolution from 640 × 640 to 480 × 480 affects detection performance and whether optimized depth/width scaling and hyperparameter tuning can compensate for the expected loss of spatial detail. In this work, we propose an optimized YOLO-based framework for brain tumor detection and localization in MRI scans, building upon the method “Addressing the Impact of Resolution Scaling on YOLO Performance for Brain Tumor Detection through Optimized Network Depth/Width Adjustments.” Our model, an enhanced variant of the BGF-YOLO architecture, is specifically tailored for the challenges of medical imaging. The proposed network features both architectural and training-level optimizations. We used a publicly available dataset from Kaggle that consists of 500 training images, 201 validation images, and 100 test images. Experimental analysis demonstrates that while reducing input resolution alone degrades performance, integrating targeted modifications specifically increases network depth and width. In addition, advanced training strategies such as MixUp augmentation, dropout regularization, AdamW optimization, cosine learning rate scheduling, and finely tuned learning rate ranges lead to substantial performance gains. The optimized model achieves a precision of up to 0.858, a recall of 0.943, mAP50 of 0.946, and mAP50–95 of 0.672. These results not only outperform the reduced-resolution baseline but also approach, and in some cases surpass, the original high-resolution BGF-YOLO setup.
Open Access: Yes
DOI: 10.3390/app16094320
