Search Results (163,821)

The surging integration of volatile renewable energy severely exacerbates power grid frequency fluctuations, yet conventional frequency regulation (FR) market clearing mechanisms fail to efficiently coordinate heterogeneous energy storage systems (ESSs) due to the complete decoupling of multi-dimensional physical performance from economic dispatch. To resolve this critical industry bottleneck, this paper proposes a novel Stackelberg game-based clearing mechanism tailored for diverse ESS participation. A bi-level optimization framework is constructed to internalize physical FR characteristics into market economics; the upper level minimizes the system operator’s total procurement costs by transforming multi-dimensional physical metrics—including dynamic response rates, time delays, and control accuracy—into endogenous performance penalty factors. Concurrently, the lower level maximizes the individual revenues of heterogeneous ESS aggregators under a Gini coefficient-based fairness constraint to mitigate profit monopolization and promote a more sustainable market ecology. To address the computational challenges of high-dimensional non-convexity, an enhanced hybrid Genetic Algorithm and Quadratic Programming (GA-QP) solver is developed to secure robust convergence to the Stackelberg equilibrium. Comprehensive simulation results confirm that the proposed Stackelberg game-based clearing mechanism enables a highly rational, quality-driven allocation of frequency regulation capacity. By dynamically linking physical performance metrics with economic benefit factors, it successfully achieves an optimal balance of interests between heterogeneous energy storage aggregators and the overarching market. Crucially, compared to conventional purely economic models, this mechanism structurally prevents absolute technology monopoly—drastically reducing the market Gini coefficient from a hazardous 0.85 to a healthy 0.32—while sustaining multi-party equity at a negligible system cost increase of only 1.64%. Ultimately, this framework offers a highly feasible and resilient solution for the efficient clearing of multi-type energy storage in modern power systems. Full article

This paper proposes a predefined-time stable neurodynamic approach for solving generalized absolute value equations. In contrast to conventional fixed-time stability methods, the proposed approach provides greater flexibility and broader applicability through the inclusion of an adjustable time parameter. Under appropriate conditions, the method is rigorously proven to converge to the unique solution within a predefined time frame. Finally, numerical simulations are conducted to validate the convergence performance of the proposed neurodynamic method. Full article

The durability of dental restorations against erosive and staining challenges is crucial for long-term clinical success. Therefore, this in vitro study aimed to investigate the combined effects of sequential simulated gastric acid exposure, coffee immersion, and different cleaning procedures on the surface roughness and color stability of four contemporary flowable resin composites with different filler and matrix characteristics. Ninety-six disk-shaped specimens were fabricated from four resin composites: Omnichroma Flow, G-ænial Universal Injectable, Universal Flo, and EverX Flow. Baseline surface roughness and color coordinates were recorded. Specimens were immersed in 0.06 M hydrochloric acid for 48 h to simulate gastric acid exposure and subsequently immersed in coffee solution for 24 h to simulate staining. Color measurements were performed at baseline, after sequential gastric acid–coffee exposure, and after cleaning procedures. ΔE₀ was calculated between baseline and post-exposure values, whereas ΔE₁ was calculated between post-exposure and post-cleaning values. Specimens were then allocated to three cleaning subgroups: toothbrushing (BR), water flosser (WF) and mouthrinse (MR). Final surface roughness and color changes were evaluated. Data were analyzed using mixed and factorial ANOVA tests. Sequential gastric acid–coffee exposure significantly increased surface roughness in all materials. Omnichroma Flow showed the lowest roughness values, whereas G-ænial Universal Injectable demonstrated the highest post-treatment roughness, particularly after toothbrushing. Toothbrushing generally caused greater roughness increases than other cleaning procedures. Material type significantly affected color stability, with Universal Flo showing the greatest discoloration and Omnichroma Flow the lowest color change. Surface roughness and color stability were significantly influenced by sequential gastric acid–coffee exposure and cleaning procedures in a material-dependent manner. Full article

The building envelope is a key lever for reducing energy demand and carbon emissions in the built environment. However, conventional envelope systems remain largely static and are unable to respond effectively to changing climatic conditions. Biomimetics has emerged as a promising approach for the development of climate-adaptive envelope solutions. Nevertheless, research in this field remains fragmented across disciplines, and its evolution and technological maturity have not yet been systematically assessed. This study proposes an integrated analytical framework combining a bibliometric analysis of 2.007 Scopus-indexed documents, based on a VOSviewer keyword co-occurrence network, with a cluster-guided state of the art review, and a Technology Readiness Level (TRL) assessment of selected biomimetic envelope solutions. The TRL assessment is conducted using explicit operational criteria. The analysis identifies three main research clusters: (C1) environmental-performative, focusing on energy efficiency and envelope optimisation; (C2) material-experimental, addressing biomimetic composites and innovative materials; and (C3) technological fabrication, centred on digital fabrication, smart materials, and 4D printing. Temporal trends reveal a shift after 2018 from materials science-oriented studies towards computational design and adaptive manufacturing, providing quantitative evidence of a transition previously described mainly in qualitative terms. The review highlights a strong focus on solar-shading applications, while energy harvesting and passive thermoregulation remain comparatively underexplored. The TRL assessment shows that more than 80% of the analysed solutions are concentrated at TRL 3, indicating an early stage of technological development. The main barriers include limited material durability, non-standardised production costs, and regulatory constraints. The findings suggest that future progress will depend less on the identification of new biological inspirations and more on advancing the technological maturity and industrial scalability of existing concepts. This will require integrated developments in materials, parametric design, life-cycle assessment, and regulatory frameworks. Full article

Building- and residential-scale energy systems are becoming increasingly complex due to the growing use of renewable energy sources, variable generation profiles, and uncertainties in user demand. This study comprehensively examines the role of electrical, thermal, and hydrogen-based energy storage technologies in building-scale hybrid energy systems and evaluates these systems alongside energy management strategies. In this context, lithium-ion batteries, supercapacitors, flywheel systems, thermal energy storage solutions, and hydrogen-/fuel cell-based architectures are discussed in terms of their technical characteristics, intended uses, limitations, and complementary aspects. The reviewed studies show that individual storage technologies remain limited in their ability to meet all operational requirements, whereas hybrid storage architectures offer significant advantages in terms of power quality, energy flexibility, energy storage system lifetime, renewable energy utilization, and long-duration energy supply security. Furthermore, energy management systems are shown to be critical not only for cost minimization but also for user comfort, grid interaction, forecasting accuracy, uncertainty management, and the coordination of storage units operating at different timescales. Consequently, achieving high efficiency, low-carbon operation, and energy autonomy in building- and residential-scale systems requires the integrated design of multilayered hybrid storage approaches that are supported by intelligent energy management. Full article

Water scarcity and increasing energy demand are critical challenges that often characterize Mediterranean regions, especially islands such as Crete. A sustainable solution for a combined water and energy supply lies in the domain of hybrid renewable energy systems. This research study evaluates a large-scale wind-driven desalination system with pumped-hydro energy storage for the Regional Unit of Rethymno, Crete, focusing on climate-driven demand and air temperature variation. The proposed system integrates wind energy production, seawater desalination, pumped-hydro storage, and water supply both for domestic and for irrigation purposes. Four scenarios, each with increasing air temperature, are examined in order to assess their effect on water demand and system performance. The analysis evaluates electricity allocation, the production of desalinated water, domestic and irrigation coverage, as well as the economic performance of the system. The results indicate that domestic water demand is almost fully covered in all four scenarios, reaching nearly 99.9%, while irrigation water coverage decreases from 82% under present conditions to 67% under higher-temperature scenarios. Wind-generated electricity is mainly used for water-related processes, with a constant share supplied to the grid. The economic assessment indicates that the system can operate under break-even conditions using realistic water and electricity prices. Full article

Aeolian vibrations of overhead transmission conductors under light wind excitation can cause fatigue damage at line clamps, threatening grid safety. Accurately evaluating wind energy input is key for assessing vibration intensity and designing anti-vibration devices. This paper employs CFD overlapping mesh technology to establish a two-degrees-of-freedom fluid–structure interaction model of the conductor. Through numerical simulations at different Reynolds numbers, a cubic polynomial relationship between wind energy input power and amplitude ratio is derived, and a wind turbulence intensity correction factor is introduced. Using a long-span conductor as a case study, the fitted wind energy input is substituted into the energy balance equation to calculate dynamic bending strain under four conditions: no control, only vibration dampers, only damping wires, and a combined scheme. The results show that using either vibration dampers or damping wires alone cannot reduce dynamic bending strain below 200 με, while the combined scheme reduces the maximum strain within the critical frequency range to 113.09 με. This verifies the effectiveness of the fitted wind energy input formula in engineering anti-vibration design. Full article

Induction motor starting often causes voltage fluctuations due to high inrush current, which can impact system stability and power quality. To address this, smart inverters with power control functions and superconducting fault current limiters (SFCLs) have emerged as promising solutions for reducing voltage instability. The power control of smart inverters ensures that the voltage at the point of common connection is maintained at a constant level, adapting to the system’s conditions. In this paper, system voltage characteristics during induction motor starting were analyzed by applying smart inverter power control and SFCL, which aimed to suppress voltage fluctuation under high inrush current conditions. Experiments were conducted with smart inverters dynamically adjusting reactive power while SFCLs limit high inrush currents. The results show that smart inverters effectively suppress voltage drops during motor starting. Although SFCLs reduce inrush current, they can further decrease system voltage at the point of common coupling. This issue can be reduced through smart inverter control, improving voltage stability. Full article

Preterm infants are at high risk of neurodevelopmental disorders (NDDs), yet current brain assessments using 2D cranial ultrasound (cUS) remain subjective and limited. 3D cUS offers richer anatomical details but is underutilized due to its complexity and lack of automated tools. We developed and evaluated Deep Learning (DL) methods for total brain (TB) segmentation from 3D cUS of preterm neonates, emphasizing accuracy, reproducibility, and clinical deployability. We compared 2D UNet models, a contextual UNet, and self-configuring 2D/3D nnUNet variants, evaluating accuracy, efficiency, and GPU/CPU feasibility. Clinical relevance was examined through longitudinal total brain volume (TBV) trajectories and their association with 2-year neurodevelopmental outcomes. The nnUNet ensemble achieved the best performance (Dice: 0.97, Volumetric Difference Error: 2.32%), while contextual UNet offered a favorable accuracy-efficiency trade-off on low-resource hardware. Longitudinal analysis showed significantly slower TBV growth in infants with adverse outcomes (p = 0.007), supporting the prognostic value of automated volumetric measurements. We developed a clinician-facing web application to illustrate clinical integration. This work demonstrates the feasibility of DL-based TB segmentation from 3D cUS and provides a scalable reproducible framework supporting early quantitative assessment of brain development, laying the groundwork for future lightweight, privacy-aware AI solutions for Neonatal Intensive Care Unit integration. Full article

Drought research on quinoa has focused almost exclusively on the shoots, leaving the roots, the organ that first senses soil drying, largely unexamined, and its recovery dynamics are still poorly characterized. Here, we show that in the Chilean coastal quinoa ecotype AZ1, recovery from drought is governed belowground, and the root regains hydraulic and metabolomic competence well before the stomata reopen. After 72 h of soil drying, stomatal conductance (g_s) decreased by 98%, whole-plant transpiration declined biphasically (~92% of the loss within the first two hours), and water potential decreased steeply at the soil–root interface (with soil and root water potential declining approximately 10- and 20-fold relative to well-watered plants), while the stem remained near-stable, pinpointing the root as the dominant hydraulic bottleneck. Twenty-four hours after rewatering, root system and whole-plant water potential, osmotic root hydraulic conductance (Lp_rOS), root anatomy, and the polar metabolome were largely restored, yet g_s remained statistically indistinguishable from droughted plants. Strikingly, hydraulic recovery proceeded without rebuilding the osmotic sugar pool; instead, normalization of TCA-cycle intermediates points to an energy-powered and possible aquaporin-mediated transport route that bypasses still-suberized apoplastic barriers. Root system metabolomics, led by GABA and L-alanine, which overshot the control, tracked root rehydration but correlated negatively with g_s, suggesting that nitrogen-rich solutes may act as candidate belowground cues restraining stomatal reopening. These findings suggest that the quinoa root system acts as a pacemaker for drought recovery. Full article

Embedded Modular Multilevel Converter-Based Multi-Terminal Direct Current (MMC-MTDC) systems have become an important solution for enhancing transmission capacity and operational flexibility in urban hybrid AC/DC power grids. However, the coexistence of Grid-Following (GFL) and Grid-Forming (GFM) MMC stations introduces complex dynamic interactions through both AC and DC networks. Existing small-signal stability studies often neglect MMC internal dynamics, such as submodule capacitor voltage fluctuations and circulating current-related states, or simplify the AC network as an ideal voltage source, which may lead to inaccurate stability assessment and limited control parameter optimization performance. To address these issues, this paper proposes a coordinated small-signal stability enhancement strategy for an embedded heterogeneous MMC-MTDC system considering AC/DC bilateral coupling. First, a system-level full-order small-signal state-space model is established by incorporating the internal dynamics of both GFL-MMC and GFM-MMC stations, non-ideal AC networks, and DC transmission links. Then, eigenvalue analysis and participation factor-based sensitivity evaluation are performed to identify weakly damped oscillation modes and screen the key variables and control parameters associated with dominant oscillations. Furthermore, a quadratic performance index is constructed by weighting the sensitivities of key control parameters, and particle swarm optimization is employed to obtain coordinated optimized parameters for heterogeneous MMC stations. Comparative case studies and PSCAD/EMTDC time-domain simulations verify the effectiveness of the proposed strategy under different scenarios. The quantitative active-power indices show that, compared with the unoptimized parameters, Strategy 2 reduces the settling time by 49.1% in the power step response, suppresses the power step overshoot from 6.4% to 0%, shortens the settling time by 53.8% under grid-strength variation, and reduces the active-power peak and settling time by 28.0% and 74.8%, respectively, under the fault ride-through scenario. Full article

Aqueous two-phase systems (ATPSs) provide a versatile, fully aqueous platform for probing solute–water interactions and protein structure. This review first surveys the diversity and phase behavior of biphasic aqueous systems formed by polymers and salts. We describe how phase diagrams characterize ATPS formation and composition and how both polymer chemistry and salt identity, rather than molecular size alone, govern phase separation by modulating the solvent properties of water. Building on a modified binodal model, we show that phase separation and solute partitioning can be understood in terms of changes in aqueous solvent dipolarity/polarizability, hydrogen-bond donor/acceptor properties, hydrophobicity, and electrostatics, quantified via solvatochromic probes and homologous solute series. These measurements underpin solvent interaction analysis (SIA), in which the partition coefficients of small molecules and proteins across panels of ATPSs are used to generate “structural signatures” that sensitively report on amino acid substitutions, conformational changes, aggregation, ligand binding, osmolyte effects, and post-translational modifications, independent of protein size. We discuss how SIA can be implemented in vial-, plate-, and microfluidic formats and combined with diverse analytical readouts (HPLC, MS, colorimetric assays, and immunoassays), and we contrast this structure-focused approach with conventional concentration-only proteomic and biomarker strategies. Particular emphasis is placed on structure-based biomarker discovery, where disease-relevant shifts in proteoform distributions—especially glycosylation changes—are often more informative than bulk protein levels and where SIA can complement or simplify complex glycomics and top-down proteomics workflows. As a case study, we describe the recently FDA-approved IsoPSA assay, which applies SIA principles to prostate-specific antigen by measuring cancer-associated structural alterations in circulating PSA via its partition behavior in a proprietary ATPS. IsoPSA generates a single index that discriminates between high-grade prostate cancer and benign and low-grade conditions. Prospective, longitudinal, and MRI-integrated clinical studies demonstrate that IsoPSA improves pre-biopsy risk stratification, reduces unnecessary biopsies, and provides robust negative and positive predictive values within the PSA “gray zone.” Collectively, the data support aqueous solvent interaction analysis as a broadly applicable, mechanistically grounded technology for protein characterization, drug–protein interaction studies, and structure-centric biomarker development, exemplified by the clinical translation of IsoPSA. Full article

Nature-Based Solutions (NBS) have emerged as a prominent and rapidly expanding strategic approach for addressing climate change, biodiversity loss, and socio-ecological resilience in cities, especially within European sustainability agendas where NBS are embedded in EU policy frameworks. Nevertheless, their practical implementation remains uneven across the globe, with relatively strong mainstreaming in Europe but more fragmented uptake in other regions. These disparities stem from a gap between policy-oriented NBS discourse and its translation into design and spatial practice. This study investigates how governance-related NBS research can be more effectively translated into landscape architecture and design practice. It integrates insights from narrative literature reviews, project-based experiences, and international conference sessions. We argue that effective NBS implementation requires alignment with landscape architecture (systems-multi-scale planning, design, site-specific ecological materialization and aesthetic mediation) and governance integration. Through comparative analysis of Global North and South contexts, we identify differences in institutional capacity, socio-cultural perception of nature, and knowledge systems that shape NBS implementation. We propose a multi-scale, co-creative framework connecting environmental knowledge, governance interface, and design practice. The findings demonstrate that NBS cannot succeed solely as a science and policy approach; instead, they must be spatially translated through culturally responsive and ecologically informed landscape design processes and maintenance. Full article

To overcome the challenges in conventional frame structure design, namely the difficulty in balancing lightweight design and performance enhancement, the low efficiency of finite element (FE) simulation, and the tedious process of multivariable preliminary screening, an efficient optimization framework for frame structures that integrates multi-objective size optimization, machine learning-based performance prediction, and feature correlation analysis is proposed. First, for the steel–aluminum hybrid frame (with the main load-bearing components made of 6005A aluminum alloy and the critical load-bearing supports and joints made of Q345 low-alloy high-strength steel), a trade-off solution is obtained through multi-objective size optimization. Verified by FE simulation, this solution reduces the frame mass by 6.37% and increases the torsional stiffness by 10.47% compared with the initial design. At the same time, the modal performance, structural strength, and deformation control capability are all significantly improved, achieving a precise balance between lightweighting and stiffness enhancement. Second, a linear regression prediction model is constructed to achieve high-accuracy predictions. The average prediction error for torsional stiffness is only 2%, and the maximum prediction error for the seventh-order modal frequency is less than 1%. The prediction time for a single sample is less than one second, which is more than 1000 times faster than conventional FE simulation, thus efficiently replacing time-consuming simulation analyses. Finally, feature correlation analysis is adopted as an alternative to traditional sensitivity analysis. The core variables identified by this method are highly consistent with those obtained from Hypermesh sensitivity analysis, enabling rapid multivariable screening without additional simulations and greatly improving the efficiency of the preliminary analysis phase. The proposed optimization framework achieves a favorable combination of optimization effectiveness, prediction accuracy, and design efficiency. It not only provides a feasible engineering solution for the lightweight design of frame structures but also serves as a technical reference for the efficient optimization of similar complex structures, demonstrating significant engineering application value. Full article

Copenhagen is often regarded as a pioneer city in climate change adaptation and green–blue infrastructure deployment. However, when addressing extreme environmental problems such as heavy rainfall flooding and urban heat island effects, many interventions remain site-specific and insufficiently connected across spatial scales. This article investigates how Nature-Based Solutions (NBS) can be structured as an integrated spatial infrastructure system to enhance climate resilience and urban livability in Copenhagen. The research combines theoretical analysis, comparative project studies, GIS-based spatial analysis, and a multi-criteria evaluation matrix. Building on this method, a multi-scalar framework for NBS interventions is developed, which consists of site prioritization at the Meso scale, development strategies at the Micro scale, and NBS spatial design at the Nano scale. With the assessment criteria adapted from the UNalab project Nature-Based Solutions Technical Handbook Factsheets, the results demonstrate that Nano-scale design strategy can enhance green connectivity, create new inclusive urban public spaces, transform existing industrial areas, and improve new streets and parks for climate resilience. The Meso-scale distributed measures deliver localized benefits such as infiltration and flood buffering, while the most effective strategies integrate cross-scalar NBS interventions within a connected network. The case of Nordhavn is analyzed through multi-criteria selection and multifunctionality assessment as a qualitative process for a better demonstration of the practical application of NBS prioritization and spatial design analysis. Full article