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Why and When Merging Surface Nanobubbles Jump
No full textGas bubble accumulation on substrates reduces the efficiency of many physicochemical processes, such as water electrolysis. For microbubbles, where buoyancy is negligible, coalescence-induced jumping driven by the release of surface energy provides an efficient pathway for their early detachment. At the nanoscale, however, gas compressibility breaks volume conservation during coalescence, suppressing surface energy release and seemingly disabling this detachment route. Using molecular dynamics simulations, continuum numerical simulations, and theoretical analysis, we show that surface nanobubbles with sufficiently large contact angles can nevertheless detach after coalescence. In this regime, detachment is powered by the release of pressure energy associated with nanobubble volume expansion. This finding thus establishes a unified driving mechanism for coalescence-induced bubble detachment across all length scales.</p
Effects of operational conditions on hollow fiber nanofiltration for micropollutant removal from wastewater effluent
Get PDFTo remove organic micropollutants from wastewater effluent, hollow fiber nanofiltration (HFNF) is a promising solution based on lab scale work with mostly synthetic solutions. In this study, we investigate the application of commercially available HFNF membranes on a pilot scale (1 m3 h−1) for real wastewater effluent treatment. The effects of scale, recovery, flux, crossflow velocity and staging are presented, with a focus on their impact on the retention of ions and organic micropollutants (OMPs), while considering the energy consumption. It is shown that the retention measured for OMPs is substantially higher on the benchtop scale compared to the pilot scale. The most effective strategy to improve retention on a pilot scale is to decrease the recovery or increase the flux. The energy consumption shows that crossflow velocity has a major impact on energy consumption, whereas flux and recovery play a smaller role. Furthermore, Christmas Tree (CT) configurations have an energy consumption that is significantly lower than Feed & Bleed (FB) modes, ranging from 0.11-0.14 kW h m−3 for CT mode and around 0.29 kW h m−3 for FB mode at a crossflow velocity of 0.4 m s−1. Overall, the success of the HFNF membrane for OMP removal from wastewater depends on the targeted OMPs, combined with the way the OMPs in the concentrate are removed.</p
Sparsifying Dimensionality Reduction of PDE Solution Data with Bregman Learning
No full textClassical model reduction techniques project the governing equations onto a linear subspace of the original state space. More recent data-driven techniques use neural networks to enable nonlinear projections. While those often enable stronger compression, they may have redundant parameters and lead to suboptimal latent dimensionality. To overcome these issues, we propose a multistep algorithm that induces sparsity in the encoder-decoder networks for effective reduction in the number of parameters and additional compression of the latent space. This algorithm starts with sparsely initializing a network and training it using linearized Bregman iterations. These iterations have been very successful in computer vision and compressed sensing tasks, but have not yet been used for reduced-order modeling. After the training, we further compress the latent space dimensionality by using a form of proper orthogonal decomposition. Last, we use a bias propagation technique to change the induced sparsity into an effective reduction of parameters. We apply this algorithm to three representative PDE models: 1D diffusion, 1D advection, and 2D reaction-diffusion. Compared to conventional training methods like Adam, the proposed method achieves similar accuracy with 30\% fewer parameters and a significantly smaller latent space.</p
Universal Scale for Child Development Predicts Limited Intellectual Functioning at an Early Stage
No full textAim: Early identification of limited intellectual functioning is important for providing support. This study investigated whether a universal child development score (D-score) at 12, 24 and 36 months can predict limited intellectual functioning at 5–10 years of age, in addition to neonatal and parental characteristics.Methods: A case–control study using developmental milestones and health records from three Dutch child healthcare organisations. D-scores transformed into z-scores (DAZ) were calculated from communication and all milestones.Results: Data were available for 148 children with an intelligence quotient (IQ) of 50–69, 152 children with an IQ of 70–85 (special education) and 300 controls (mainstream education) at 5–10 years of age. The area under the curve for predicting an IQ of 50–69 was 0.75 using neonatal and parental characteristics. This increased to 0.89 with the addition of DAZ communication scores and to 0.94 with the inclusion of DAZ scores using all milestones instead of only communication milestones. For predicting an IQ of 70–85, these values were 0.67, 0.75 and 0.79, respectively.Conclusion: In addition to neonatal and parental characteristics, early child development significantly predicted limited intellectual functioning. The D-score, currently derivable from 14 different instruments, serves as a powerful predictive tool in clinical practice.</p
Peri-urban lake ecosystem governance:A systematic inquiry of actor-resource relationships in the peri-urban region of Ahmedabad, India
No full textPeri-urban lake ecosystems undergo socio-ecological transformations driven by rapid urbanization, economic growth, and land use changes, resulting in habitat loss, degraded water quality, and altered livelihood patterns. These impacts are exacerbated by conflicting activities and practices of actors with diverse roles and competing interests in lake resource use and function. While societal and economic uses of lakes are broadly understood, knowledge about actor-resource interactions in the peri-urban remains scarce. Addressing this gap is crucial for anticipating effective resource governance, management, and conservation. This study applies an actor-resource interaction framework that examines actor values, perceptions, and practices and their interactions with peri-urban lake ecosystems in the peri-urban region of Ahmedabad, Gujarat. India. We combine geospatial data, socio-ecological surveys, and actor interviews to characterise the spatial-ecological setting and governance interactions. The analysis reveals a landscape is dominated of small, hydrologically connected lakes vulnerable to degradation. Actors prioritize cultural and provisioning ecosystem services, highlighting conflicting interests and power asymmetries, which together lead to governance challenges. These insights on the peri-urban lake governance advance the understanding of how diverse actor – resource interactions mediate ecological outcomes. The analysis closes with suggestions for inclusive and evidence-based planning and governance of peri-urban lake ecosystems
Ionizing Radiation Resistance of Butadiene and Silicone Rubbers for Mars Applications
No full textThis paper aims to investigate the ionizing radiation resistance of Butadiene (BR) and Vinyl-Silicone (VMQ) rubbers, which are the most promising candidates for Mars applications due to their low-temperature elasticity. The influence of various fillers on BR and aromatic silicone oligomer on VMQ radiation resistance was investigated by β or γ irradiation. The irradiation was carried out at two doses of 5 kGy or 10 kGy. In general, VMQ exhibits good radiation resistance even without the addition of the aromatic silicone oligomer. In contrast, the radiation resistance of BR was improved after the incorporation of the fillers, especially of mineral origin – silica, TiO2, or ZnO, which is probably a result of radiation interaction with the fillers’ particles instead of rubber macromolecules. Both rubbers have proved to be promising elastomer bases for designing future Mars rubber compounds
A Dual-Mode Anemometer Fabricated in 65-nm CMOS BEOL Without Postprocessing
No full textA thermal anemometer was industrially fabricated without any pre-, co- or post-processing for the first time. The total sensor footprint is only 0.245 mm 2, using TSMC's 65–nm CMOS process. The dual-mode anemometer measures wind speeds up to 1.2 ms -1 with a precision of 3.1 % (or 0.037 ms- 1) calorimetrically. Alternatively, using the second hot-element mode the sensor measures in the range of 0 ms -1to 7.5 ms -1 with a 0.1 ms -1 accuracy and an average 1.34 % precision. This is the first industrially fabricated thermopile-based CMOS anemometer, paving the way for a compact foundry SoC with inbuilt underlying computing for low-cost air speed monitoring.</p
DVLO4D:Deep Visual-Lidar Odometry with Sparse Spatial-Temporal Fusion
No full textVisual-LiDAR odometry is a critical component for autonomous system localization, yet achieving high accuracy and strong robustness remains a challenge. Traditional approaches commonly struggle with sensor misalignment, fail to fully leverage temporal information, and require extensive manual tuning to handle diverse sensor configurations. To address these problems, we introduce DVLO4D, a novel visual-LiDAR odometry framework that leverages sparse spatial-temporal fusion to enhance accuracy and robustness. Our approach proposes three key innovations: (1) Sparse Query Fusion, which utilizes sparse LiDAR queries for effective multi-modal data fusion; (2) a Temporal Interaction and Update module that integrates temporally-predicted positions with current frame data, providing better initialization values for pose estimation and enhancing model's robustness against accumulative errors; and (3) a Temporal Clip Training strategy combined with a Collective Average Loss mechanism that aggregates losses across multiple frames, enabling global optimization and reducing the scale drift over long sequences. Extensive experiments on the KITTI and Argoverse Odometry dataset demonstrate the superiority of our proposed DVLO4D, which achieves state-of-the-art performance in terms of both pose accuracy and robustness. Additionally, our method has high efficiency, with an inference time of 82 ms, possessing the potential for the real-time deployment.</p
Scaling dynamics of the electricity utility sector:Assessing the role of agglomeration externalities and sensitivity to population cutoffs in spatial dynamics across European regions
No full textUrban scaling studies have gained popularity in the last two decades, summarising urban attributes’ variation with population. Recent research, however, highlights scaling exponents’ sensitivity to industry-specific dynamics, population cut-offs, and data distribution. Despite this, few studies systematically examine industry scaling using plant-level data while accounting for sector-specific externalities. This study addresses that gap by analysing longitudinal data on green electricity firms across 968 NUTS (Nomenclature of Territorial Units for Statistics)-3 regions in 14 European countries (1985–2023). We assess how scaling exponents for firm entry and concentration vary across population cutoffs, both with and without controls for agglomeration externalities. Our findings reveal predominantly sublinear scaling, suggesting that population size alone does not drive green energy growth. Concentration consistently scales more strongly than entry, indicating that large cities are more conducive to firm survival than to the creation of new firms. When agglomeration externalities are not controlled for, scaling exponents are systematically underestimated. While variability is observed in regions at population extremes, results remain robust across cutoffs, especially when using inverse thresholds. Comparative analysis with high-tech service and manufacturing sectors confirms sublinear scaling in entry across all sectors, with green electricity showing the lowest exponent, reinforcing its maturity and low innovation intensity. These findings align with the Smart Specialization framework, emphasizing the importance of targeted institutional support, supplier networks, and sector-specific strategies. They also highlight the potential for smaller or lagging regions to take a more active role in the green transition, particularly within cohesion policy efforts.</p
Why not have the best of both worlds? How to use direct instruction principles in inquiry-based instructional design
No full textThis paper explores how principles drawn from direct instruction can inform the design of inquiry-based instruction, moving beyond traditional debates that pit one method against the other. Inquiry-based instruction encourages students to infer and construct knowledge through activities such as hypothesis generation, experimentation, data analysis, and drawing conclusions, while direct instruction involves explicit guidance, modeling, and structured practice, so as to minimize errors. Both methods have unique strengths: inquiry-based instruction fosters conceptual understanding and higher-order thinking, while direct instruction ensures mastery of foundational skills such as problem solving. Recent work has tried combinations of these approaches, using designs where inquiry cycles are supported by just-in-time direct instruction or alternating methods to try to optimize learning; this paper presents another approach and attempts to apply direct instruction principles within guided inquiry learning. Examples from disciplines such as mathematics, biology, chemistry, and physics as presented within the Go-Lab ecosystem illustrate how blending these methods can support students' active engagement while ensuring robust knowledge development.</p