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Found 6273 publications
Development and experimental evaluation of single-port substrate integrated waveguide resonator with dual-parameter sensitivity for non-invasive blood glucose monitoring
Publication Name: Measurement Journal of the International Measurement Confederation
Publication Date: 2026-06-16
Volume: 278
Issue: Unknown
Page Range: Unknown
Description:
Current Blood Glucose (BG) monitoring techniques are invasive or semi-invasive and can impose financial and practical burden on patients. In this study a compact, non-invasive and single port Substrate Integrated Waveguide (SIW) loaded with X-shape has been present. The sensor resonant at 1.7 GHz, a dual parameter employed to evaluate the sensor by tracking the shift of resonance frequency in (MHz) and the reflection coefficient (dB) to detect glucose-induced changes in tissue permittivity. The device is designed using full-wave electromagnetic simulations with multilayer tissue models and validated experimentally on five human volunteers under controlled fasting and post-glucose conditions. Across the physiological range of 20–200 mg/dL, the sensor exhibits sensitivities up to 0.310 MHz per mg/dL and 0.333 dB per mg/dL, demonstrating consistent responsiveness to glucose variations. The results indicate that the proposed resonator can track glucose-related dielectric changes using a simple contact-based configuration. However, the measurements are influenced by subject-specific variability and sensor placement conditions, which currently limit generalization and repeatability. Further work is required to improve robustness, calibration, and validation on larger cohorts before practical deployment.
Open Access: Yes
Collaborative precise modeling of fuel cells based on adaptive Huber loss function and wild horse optimizer with critical statistical analysis
Publication Name: International Journal of Hydrogen Energy
Publication Date: 2026-06-15
Volume: 242
Issue: Unknown
Page Range: Unknown
Description:
Precise estimation of fuel cell parameters is critical for optimizing performance and developing energy systems. However, experimental data are often affected by outliers stemming from inaccurate measurements, transient operating conditions, or environmental variations. In this line, this study proposes a robust approach for estimating proton exchange membrane fuel cell (PEMFC) parameters. This study focuses on the steady-state current–voltage (I–V) characteristics and performs parameter extraction for a semi-empirical model. The proposed estimation framework employs the collaboration of the Huber loss function (HLF) in conjunction with adaptive hyperparameter and the metaheuristic Wild Horse Optimizer (WHO) to compute seven unknown PEMFC parameters. The impact of different hyperparameter (δ) values is examined on the performance of the HLF while estimating key fuel cell parameters. The sensitivity of the estimating process to the δ-value is explored using measured and estimated datasets, including accuracy, convergence rate, and resilience. The WHO-based approach is adopted to address and mitigate issues such as premature convergence and entrapment in local optima, which are common challenges in existing optimization strategies. The proposed model has been tested and verified through three test samples of standard commercial PEMFC units as benchmarks. The simulation results demonstrate that the WHO exhibits robust performance across the three benchmark PEMFC systems. Furthermore, the proposed model's generalization capability is validated under a range of operating conditions using polarization curves generated at different temperatures and cathode stoichiometries. A single globally specified parameter set reliably predicts fuel cell performance across these diverse conditions, as evidenced by its consistent ability to deliver high-quality solutions with an extraordinary level of precision under predefined experimental conditions. The proposed estimation framework outperforms three commercial PEMFC units (NedStack-PS6, Horizon-500 W, and BCS-500W), achieving Huber loss values of 1.03277845, 0.00562094, and 0.00584889, respectively. The adaptive HLF with hyperparameter (δ) ranging from 0.5 to 2.0 efficiently tackles outliers and improves convergence speed. While the hyperparameter (δ) in previous studies was kept constant, δ = 1. The proposed estimation framework closely matches the experimental data and offers significantly higher accuracy compared to existing competing methods in the literature. The results reveal that the suggested HLF enhances the robustness and immunity of the WHO optimizer, and it outperforms traditional approaches such as steady-state error.
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
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
Transferring and scaling innovation in urban green-blue Infrastructure: beyond one-size-fits-all solutions and preconceptions
Publication Name: Sustainable Futures
Publication Date: 2026-06-01
Volume: 11
Issue: Unknown
Page Range: Unknown
Description:
Green and Blue Infrastructure (GBI) innovations are increasingly promoted in Europe, yet their transfer to new socio-political contexts remains poorly understood. This study applies the Strategic Niche Management (SNM) framework to analyse the conditions under which GBI innovations can be replicated and scaled beyond their original settings. We examine six GBI projects from five EU Member States and assess their perceived transferability through three expert workshops in Belarus, Russia and Ukraine. Using a comparative qualitative design, workshop transcripts were analysed to identify how actors interpret innovation, allocate responsibilities, and negotiate risks within their socio-technical regimes. Across cases, successful transfer depended on leadership by municipal actors, a supportive knowledge base, flexible regulatory arrangements, and targeted communication that strengthens public acceptance. Major constraints included entrenched “business-as-usual’’ routines in administrative and epistemic communities, misconceptions about the costs and maintenance of GBI, weak participatory traditions, and corruption risks. The findings demonstrate that GBI diffusion is highly context-dependent: local actors may be unexpectedly supportive of nature-based solutions, while bottom-up initiatives can serve as viable entry points even within hierarchical governance systems. The study contributes empirical insights from an under-researched region and illustrates how SNM can be operationalised to guide GBI innovation transfer and regime change.
Open Access: Yes
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
Casson hybrid nanofluid flow between two rotating disks under magnetic field and convective boundary conditions
Publication Name: Results in Engineering
Publication Date: 2026-06-01
Volume: 30
Issue: Unknown
Page Range: Unknown
Description:
Nanotechnology plays a vital role in heat transport due to its wide range of applications, significantly contributing to fields such as bioengineering, space exploration, biosensor research, semiconductor technology, and advanced electronics. The primary objective of this analysis is to examine the Casson fluid model for heat and mass transport between stretchy rotating disks, incorporating copper and titanium oxide nanoparticles into a sodium alginate base fluid. This analysis encompasses the effects of mixed convection, chemical reactions, convective conditions, activation energy, and thermal radiation. The bvp4c method is utilized to solve the resultant equations. Tables and Figures offer a clear depiction of the results. Understanding the thermal characteristics of hybrid fluids is crucial to energy systems, biological fluid dynamics, and engineering applications, where fluid flow and heat transfer are critical to system performance. At lower disk, the skin friction improved by 10.24% and 12.36% relative to the higher values of the magnetic and Cason parameters. The Schmidt number reduces mass-transfer gradients by 18.1%, whereas the activation energy decreases by 13.7%. The volume fractions of the selected nanoparticles vary from 0.02 to 0.04, and the heat transfer rates for the hybrid nanofluid increases 12% for the hybrid nanofluid as compared to the nanofluid. The hybrid nanofluid significantly affects flow distributions.
Open Access: Yes
Enabling industry symbiosis between energy-intensive industries via optimal integration of thermal energy storage
Publication Name: Thermal Science and Engineering Progress
Publication Date: 2026-06-01
Volume: 74
Issue: Unknown
Page Range: Unknown
Description:
Energy-based industrial symbiosis is a potential decarbonisation strategy for energy-intensive industries, which contribute significantly to carbon emissions. Thermal energy storage (TES) can be integrated to enhance energy efficiency and operational flexibility, while addressing issues related to supply–demand fluctuations. Nonetheless, the economic feasibility of TES-supported interplant heat recovery depends on the costs and properties of the storage media incorporated. Therefore, this work presents a systematic framework for optimising TES selection across a spectrum of storage options for interplant indirect heat integration. The objective is to minimise the total annualised cost (TAC), comprising energy and storage capital costs. The optimal TES option can then be identified based on its respective TAC ranking. A case study that compares the effectiveness of the indirect method against the intraplant and direct methods is conducted. The results show that among the 33 TES options evaluated, silica fire brick offers the lowest TAC and energy-related carbon emissions, leading to a reduction of 21.60% and 13.16%, respectively, as compared to the intraplant method. Subsequently, a sensitivity analysis is performed to explore the impacts of varying stream flowrates and storage capacity redundancy allocation on the TES selection. This provides insights into the performance of various TES options under intraplant, direct, and indirect heat integration methods. Finally, the threshold (i.e., stream flowrate required to provide economic gain under a given redundant allocation scenario) aligned with the strategic planning can be determined. This work demonstrates that TES integration can improve the economic feasibility and sustainability of industrial symbiosis in energy-intensive industries.
Open Access: Yes
Speaking up with passion: Voice, leadership, and helping behavior in public organizations
Publication Name: Acta Psychologica
Publication Date: 2026-06-01
Volume: 266
Issue: Unknown
Page Range: Unknown
Description:
Healthcare delivery depends not only on technical expertise but also on the willingness of clinicians to speak up and to support one another in demanding environments. Drawing on Self-Determination Theory, this study explores how prosocial voice fosters helping behavior through the energizing role of work passion, and how ethical leadership shapes this process. Using a three-wave, multi-source design, we obtained 306 matched employee–leader dyads from public hospitals. Structural equation modeling demonstrated that prosocial voice enhanced work passion, which in turn predicted helping behavior. Mediation analysis confirmed the indirect effect, while moderation results revealed that ethical leadership amplified the passion–helping relationship, producing a significant conditional indirect effect. These findings extend theoretical work by positioning passion as a motivational mechanism that explains how voice translates into prosocial outcomes, and by showing that ethical leadership provides the contextual support necessary for this process. These results provide practical guidance for strengthening supportive climates and leadership practices that enable clinicians' discretionary contributions.
Open Access: Yes
