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Long-term pH trends across depth in coastal areas of the southeastern Bay of Biscay
No full textPublisher Copyright: © 2025 Elsevier LtdIncreased CO2 concentrations in the atmosphere have triggered ocean acidification over the past decades in the global ocean. However, regional efforts of pH monitoring across the southern Bay of Biscay's Basque coast remain elusive, with only a few short-term studies limited to the ocean's surface. Here we examine pH trends over time across the Basque coast using 21733 observations of long-term data collected during 2002–2022 with quarterly CTD casts from surface down to 100 m at three coastal sites. Results revealed significant pH decreases over time in all depth layers (0.5–100 m) at the three coastal sites (0.022–0.041 units decade−1), presumably driven by the global increase of atmospheric CO2. Across depth, observed pH trends also showed significantly higher ocean acidification rates with depth. Seasonally, observed pH changes ranged from wintertime highs of 8.18 ± 0.07 to summertime lows of 8.14 ± 0.05, with a mean seasonal amplitude of about ∼0.04 pH units. The observed pH seasonality and vertical patterns appeared to be tied to the combined effect of environmental factors alongside the development of the thermocline as well as to differences in the biological activity across the water column. Taken together, these findings highlight the importance of pH monitoring in coastal areas and warn on the effect of ocean acidification on marine ecosystems and the services they provide to society.Peer reviewe
Progress on waste plastics gasification process: a review of operating conditions, reactors and catalysts for clean syngas production and tar abatement: A review of operating conditions, reactors and catalysts for clean syngas production and tar abatement
Get PDFPublisher Copyright: © 2025 The AuthorsWith the continuous increase in global plastic production and the associated challenges in managing plastic and waste plastic, this review provides an overview of the plastic waste issue and explores gasification as a potential solution. The main objective of gasification is to convert selectively waste plastics into synthesis gas (syngas). It is important to highlight that the composition and potential applications of the resulting syngas are largely dependent on the gasifying agent employed. Specifically, steam or oxygen gasification typically yields hydrogen-rich syngas suitable for synthesis processes, whereas air gasification predominantly produces syngas for energy generation. Nonetheless, the applicability of the produced syngas is often constrained by the presence of various contaminants. Among these, the high tar content represents one of the principal limitations of the gasification process. This review examines the impact of key operational parameters and the utilization of various catalysts in the reduction of tar during the gasification of plastics. The results from over 100 experimental trials are summarised, providing a thorough synthesis of recent advancements in the field. Furthermore, the principal gasification strategies and technologies relevant to plastics and plastic waste are explored in detail.Peer reviewe
Development of personalized dexamethasone orodispersible solid oral dosage forms by semisolid extrusion 3D printing
Get PDFPublisher Copyright: © 2025 The Author(s)This work aimed to develop a semisolid extrusion 3D printing formulation that incorporates dexamethasone, a potent corticosteroid widely used to treat multiple ailments, that could be employed to manufacture personalized orodispersible dosage forms. Inks were optimized to allow proper extrusion and formulated to be composed of exclusively generally regarded as safe excipients which were also selected to account for a wide array of conditions and dietary restrictions. The influence of the design's physical characteristics (S/V and target weight) on disintegration time was studied and a S/V > 1.94 and a maximum weight of 165.39 mg were set as the limits to ensure the printlets can qualify as orodispersible. Printlets with doses ranging from 0.25 mg to 5 mg were manufactured and their disintegration time, dissolution and mass and content uniformity were evaluated. These dexamethasone dosage forms were able to disintegrate under the 3-minute mark and met the pharmacopoeia standards for mass and content uniformity for all 9 different designs printed. A 3D printing approach such as ours would allow to manufacture drugs specifically designed to meet the therapeutical needs of each patient without further dosage form modification while upholding the quality standards set for drugs manufacture. This, in turn, could improve the quality of care for paediatric or geriatric patients, groups that have more restricted access to suitable treatment options.Peer reviewe
Dynamic Movement Primitives in Constrained Environments: Teaching Control Policies Through Repeated Demonstrations
Get PDFPublisher Copyright: © 2013 IEEE.Enabling robots to learn from few demonstrations is a crucial step toward task automation. This paper addresses a key challenge in robotic learning from demonstration: developing adaptable, constraint-aware systems that learn from a limited number of demonstrations, infer task-specific geometrical constraints autonomously, and ensure trajectory adherence to these constraints while remaining accessible to non-expert users. The proposed methodology integrates automatic task-constraint extraction from teleoperated demonstrations with a novel sigmoidal coupling term (SIG-CDMP) to enforce spatial constraints within Dynamic Movement Primitives (DMPs). By analyzing variability in demonstrations, the framework defines a tolerance zone for robot motion and ensures that generated trajectories remain within these bounds, even when adapting to new initial or goal positions. The efficacy of this approach is validated in two real-world industrial applications—sterility testing in pharmaceutical industry and cable wiring in electric vehicle batteries—demonstrating negligible increases in computational cost and smooth, constraint-compliant trajectories. By integrating task-constraint extraction and enforcement, this approach advances the development of constraint-aware robotic systems that learn from repeated demonstrations to teach control policies respecting inferred geometrical constraints, paving the way for safe and reliable task automation in complex environments.Peer reviewe
Novel Co-Polyamides Containing Pendant Phenyl/Pyridinyl Groups with Potential Application in Water Desalination Processes
No full textPublisher Copyright: © 2025 by the authors.This study explores the development and evaluation of a novel series of aromatic co-polyamides featuring diverse pendant groups, including phenyl and pyridinyl derivatives, designed for water desalination membrane applications. These co-polyamides, synthesized with a combination of hexafluoroisopropyl, oxyether, phenyl, and amide groups, exhibited excellent solubility in polar aprotic solvents, thermal stability exceeding 350 °C, and the ability to form robust, flexible films. Membranes prepared via phase inversion demonstrated variable water permeability and NaCl rejection rates, significantly influenced by the pendant group chemistry. Notably, pyridinyl-substituted membranes achieved water fluxes up to 17.7 L m−2 h−1 and a NaCl rejection of 37.3%, while phenyl-substituted variants provided insights into the interplay of hydrophobicity and porosity. These findings highlight the critical role of pendant group functionality in tailoring membrane performance, offering a foundation for further structural modifications to enhance efficiency in water treatment technologies.Peer reviewe
Sustainable design on manufacturing V2O5 nanoparticles and analysis of their material properties for CO gas sensors
No full textPublisher Copyright: © 2025 Vietnam Academy of Science & Technology. All rights, including for text and data mining, AI training, and similar technologies, are reserved.In this study, V2O5 nanoparticles were prepared using agate containers and balls in the ball milling technique. The structural, morphological, compositional, optical, and CO gas sensing properties of nanostructured V2O5 samples were thoroughly investigated. The primary objective of this research was to explore the correlation between the synthesis conditions and the properties of V2O5 materials prepared in an agate environment. The morphological study revealed that the ball milling technique altered the geometrical shapes and irregular grain sizes (ranging from 500 to 2000 nm) into nanograins (with sizes reduced to approximately 200 nm). Energy dispersive x-ray spectroscopy (EDS) spectra confirmed the presence of vanadium and oxygen elements in the samples, and the EDS mapping demonstrated their homogeneous distribution. The formation of the orthorhombic crystal structure of V2O5 was observed through structural analysis. It was found that the intensity of the peaks and the crystallinity of the V2O5 samples decreased with increasing milling time. Optical properties showed an improvement in the bandgap of the V2O5 semiconductors at higher milling times, which can be attributed to lattice deformation effects. BET analysis indicated an increase in surface area and a reduction in pore size after the milling process. The CO gas sensing properties were associated with changes in the surface electrical resistance upon exposure to CO gas at various concentrations, with the milled samples showing a relatively high response. Therefore, nanostructured V2O5 has the potential for use in gas sensing applications. However, further investigations are required to optimize the CO gas sensing properties.Peer reviewe
LLM in the Loop: A Framework for Contextualizing Counterfactual Segment Perturbations in Point Clouds
Get PDFPublisher Copyright: © 2013 IEEE.Point Cloud Data analysis has seen a major leap forward with the introduction of PointNet algorithms, revolutionizing how we process 3D environments. Yet, despite these advancements, key challenges remain, particularly in optimizing segment perturbations to influence model outcomes in a controlled and meaningful way. Traditional methods struggle to generate realistic and contextually appropriate perturbations, limiting their effectiveness in critical applications like autonomous systems and urban planning. This paper takes a bold step by integrating Large Language Models into the counterfactual reasoning process, unlocking a new level of automation and intelligence in segment perturbation. Our approach begins with semantic segmentation, after which LLMs intelligently select optimal replacement segments based on features such as class label, color, area, and height. By leveraging the reasoning capabilities of LLMs, we generate perturbations that are not only computationally efficient but also semantically meaningful. The proposed framework undergoes rigorous evaluation, combining human inspection of LLM-generated suggestions with quantitative analysis of semantic classification model performance across different LLM variants. By bridging the gap between geometric transformations and high-level semantic reasoning, this research redefines how we approach perturbation generation in Point Cloud Data analysis. The results pave the way for more interpretable, adaptable, and intelligent AI-driven solutions, bringing us closer to real-world applications where explainability and robustness are paramount.Peer reviewe
A collaborative content moderation framework for toxicity detection based on multitask neural networks and conformal estimates of annotation disagreement
No full textPublisher Copyright: © 2025 Elsevier B.V.Content moderation typically combines the efforts of human moderators and machine learning models. However, these systems often rely on data where significant disagreement occurs during moderation, reflecting the subjective nature of toxicity perception. Rather than dismissing this disagreement as noise, we interpret it as a valuable signal that highlights the inherent ambiguity of the content—an insight missed when only the majority label is considered. In this work, we introduce a novel content moderation framework that emphasizes the importance of capturing annotation disagreement. In this work, we propose a novel content moderation framework that prioritizes capturing annotation disagreement. Our approach leverages multitask neural networks with transformer architectures as their backbone, where toxicity classification serves as the primary task and annotation disagreement is modelled as an auxiliary task. By framing disagreement as a predictive problem within the multitask learning architecture, our method effectively captures the nuanced ambiguity of content toxicity. Additionally, we leverage uncertainty estimation techniques, specifically Conformal Prediction, to account for the model's inherent uncertainty in predicting toxicity and annotation disagreement. The framework also allows moderators to adjust thresholds for annotation disagreement, offering flexibility in determining when ambiguity should trigger a review. We demonstrate that our joint approach enhances model performance, calibration, and uncertainty estimation, while offering greater parameter efficiency and improving the review process in comparison to single-task methods.Peer reviewe
Design and qualification of a ceramic insulating break for ITER In-Vessel Coils
No full textPublisher Copyright: © 2025 Elsevier B.V.The ITER In-Vessel Coils (IVC) are water-cooled coils, composed by a mineral-insulated copper conductor, enclosed in a stainless-steel jacket. The system is installed inside the ITER Vacuum Vessel, and it is fed by high-voltage feedthroughs that provide electrical power and cooling water to the copper conductors. Each coil is cooled individually and has dedicated inlet/outlet stainless-steel pipes in the ex-vessel area. To electrically separate the IVC from the ITER cooling water system, an electrical insulating break is needed. The IVC Insulating Break (IB) is operated in a radiation environment and at temperatures up to 240 °C. It shall provide electrical insulation up to 2.4 kV and shall withstand cooling water pressure up to 44 bar as well as mechanical and thermal loads related to the ITER IVC operations.Peer reviewe
Photovoltaic module with encapsulant system based on recyclable composite material
Get PDFPublisher Copyright: © 2025An initial approach for a photovoltaic module with enhanced chemical recyclability and its recycling process is presented. The module encapsulant system consisted of a glass fiber reinforced composite material with cleavable epoxy matrix and a polymeric frontsheet as an additional protection for the composite. Using vacuum assisted resin infusion process, lab-size modules with monocrystalline back-contact cells were manufactured with the mentioned encapsulation. The performance stability under thermal cycling and ultraviolet exposure was acceptable, whereas in damp-heat conditions the electrical performance loss was slightly more pronounced. This was attributed to the effect of humidity in the cleavable groups of the resin, leading to an optically non-homogeneous composite material. Regarding the recyclability in mild acid conditions, the effect of process time, temperature and acetic acid concentration was analyzed. A suitable solvolysis window was defined leading to wafer, reinforcement and frontsheet separation and recovery. The study concluded that damp-heat stability should be optimized considering the features of the epoxy matrix in terms of a balance between durability in humid conditions and recyclability. Further, advancing in the recycling process would focus on parameter optimization and their influence in the nature and quality of recovered materials and components.Peer reviewe