Advancing Open Science

Within the scope of this article is the presentation of a modelling and measurement approach for the effects of roof greenings and the application of the approach to evaluate the influence of roof greenings upon the thermal conditions inside a typical residential building. It is shown that overheating in summer can be reduced, and thermal comfort for inhabitants can be increased. The cooling is caused by the transpiration of plants and by the evaporation of water from the substrate. Other relevant physical effects are the shading of plants and the increase in the heat capacity of the building. In state-of-the-art buildings, a layer with a high insulating effect is incorporated into the envelope. This leads to the effect that a huge fraction of the cooling power is taken from the outside of the building and only a smaller part is taken from the inside. In order to mitigate this decoupling, a hydraulic connection between the greening and the interior of the building is introduced. To evaluate the effect of the inside cooling, the difference in the number of yearly hours with overheating in residential buildings is estimated. In addition, the reduction in energy demand for the climatisation of a typical residential building is calculated. The used methods are as follows: (1) Performance of laboratory and free field measurements. (2) Simulation of a typical residential building, using a validated approach. In summary, it can be said that green roofs, in particular with hydraulic connections, can significantly increase the interior thermal comfort and potentially reduce the energy required for air conditioning.

The early stage of architectural design plays a decisive role in determining building energy performance, yet conventional evaluation is typically deferred to later phases, restricting timely and data-informed feedback. This paper proposes EnergAI, a generative design framework that incorporates energy optimization objectives directly into the scheme generation process through large language models (e.g., GPT-4o, DeepSeek-V3.1-Think, Qwen-Max, and Gemini-2.5 pro). A dedicated dataset, LowEnergy-FormNet, comprising 2160 cases with site parameters, massing descriptors, and simulation outputs, was constructed to model site, form, and energy relationships. The framework encodes building massing into a parametric vector representation and employs hierarchical prompt strategies to establish a closed-loop compatibility with ClimateStudio. Experimental evaluations demonstrate that geometry-oriented and fuzzy-goal prompts achieve average annual reductions of approximately 16–17% in energy use intensity and 3–4% in energy cost compared with human designs, while performance-oriented structured prompts deliver the most reliable improvements, eliminating high-energy outliers and yielding an average EUI-saving rate above 50%. In cross-model comparisons under an identical toolchain, GPT-4o delivered the strongest and most stable optimization, achieving 63.3% mean EUI savings, nearly 13% higher than DeepSeek-V3.1-Think, Qwen-Max, and Gemini-2.5 baselines. These results demonstrate the feasibility and indicate the potential robustness of embedding performance constraints at the generation stage, providing a feasible approach to support proactive, data-informed early design.

Background/objectives: The nutrition care process (NCP) is an evidence-based practice framework used in Medical Nutrition Therapy for the prevention, treatment, and management of non-communicable chronic health conditions. This review aimed to explore available artificial intelligence (AI)-integrated technologies across the NCP in dietetic primary care, their uses, and their impacts on the NCP and patient outcomes. Method: Six databases were searched: MEDLINE, Embase, PsycINFO, Scopus, IEEE, and ACM digital library. Eligible studies were published between January 2007 and August 2024 and included human adult studies, AI-integrated technologies in the dietetic primary care setting, and patient-related outcomes. Extracted details focused on participant characteristics, dietitian involvement, and the type of AI system and its application in the NCP. Results: Ninety-seven studies were included. Three different AI systems (image or audio recognition, chatbots, and recommendation systems) were found. These were implemented in web-based or smartphone applications, wearable sensor systems, smart utensils, and software. Most AI-integrated technologies could be incorporated into one or more NCP stages. Seventy-nine studies reported user- or patient-related outcomes, with mixed findings, but all highlighted efficiencies of using AI. Higher patient engagement was observed with Chatbots. Seventeen studies raised concerns encompassing ethics and patient safety. Conclusions: AI systems show promise as a clinical support tool across most stages of the NCP. Whilst they have varying degrees of accuracy, AI demonstrates potential in improving efficiency, supporting personalised nutrition, and enhancing chronic disease management outcomes. Integrating AI education into dietetic training and professional development will be essential to ensure safe and effective use in practice.

Efficient locomotion in autonomous driving and robotics requires clearer visualization and more precise map. This paper presents a high accuracy online mapping including weight matching LiDAR-IMU-GNSS odometry and an object-level highly dynamic point cloud filtering method based on a pseudo-occupancy grid. The odometry integrates IMU pre-integration, ground point segmentation through progressive morphological filtering (PMF), motion compensation, and weight feature point matching. Weight feature point matching enhances alignment accuracy by combining geometric and reflectance intensity similarities. By computing the pseudo-occupancy ratio between the current frame and prior local submaps, the grid probability values are updated to identify the distribution of dynamic grids. Object-level point cloud cluster segmentation is obtained using the curved voxel clustering method, eventually leading to filtering out the object-level highly dynamic point clouds during the online mapping process. Compared to the LIO-SAM and FAST-LIO2 frameworks, the proposed odometry demonstrates superior accuracy in the KITTI, UrbanLoco, and Newer College (NCD) datasets. Meantime, the proposed highly dynamic point cloud filtering algorithm exhibits better detection precision than the performance of Removert and ERASOR. Furthermore, the high-accuracy online mapping is built from a real-time dataset with the comprehensive filtering of driving vehicles, cyclists, and pedestrians. This research contributes to the field of high-accuracy online mapping, especially in filtering highly dynamic objects in an advanced way.

Developing efficient photocatalysts is essential for sustainable wastewater treatment and tackling global water pollution. Graphitic carbon nitride (g-C₃N₄) is a promising material because it is active under visible light and chemically stable. However, its practical application is limited by fast recombination of charge carriers and a low surface area. In this study, we report a simple hydrothermal method to synthesize exfoliated porous g-C₃N₄ (E-PGCN) combined with Ti₃C₂ MXene to form a heterojunction composite that addresses these issues. Various characterization techniques (FTIR, XRD, XPS, SEM, BET) confirmed that adding MXene improves light absorption, increases surface area (53.7 m²/g for the composite versus 21.4 m²/g for bulk g-C₃N₄ (BGCN)), and enhances charge separation at the interface. Under UV-visible light irradiation with Rhodamine B (RhB) as the model pollutant, the E-PGCN/Ti₃C₂ MXene composite containing 3 wt% MXene demonstrated an impressive degradation efficiency of 93.2%. This performance is superior to BGCN (66.6%), E-PGCN (82.5%), and E-PGCN/Ti₃C₂ MXene-5 wt% composites (81%). This is due to the excess Mxene which caused agglomeration and reduced activity. Scavenger studies identified electron radicals as the dominant reactive species, with optimal activity at pH ~4.5. This enhanced performance, 1.4 times greater than BGCN and 1.13 times higher than E-PGCN, is ascribed to the synergistic interplay between the excellent electrical conductivity of MXene and the porous structural features of E-PGCN. This work highlights the importance of morphological engineering and heterojunction design for advancing metal-free photocatalysts, offering a scalable strategy for sustainable water purification.

Gas bearings are attractive for sustainable, high-speed, and cryogenic applications, where gases replace liquid lubricants. This study numerically analyzed hybrid gas journal bearings lubricated with hydrogen, nitrogen, air, and helium, and quantifies the impact of circumferential micro-grooves. The compressible Reynolds equation was solved by the finite element method with constant-flow valve restrictors, while Gauss–Seidel iterations were used for convergence. The model was verified against published theoretical and experimental data with maximum deviations below 6%, and mesh independence is confirmed. The parametric results show that the gas type and texturing jointly controlled static and dynamic performance. Helium (highest viscosity) yielded the largest minimum film thickness, whereas hydrogen (lowest viscosity) attained higher peak pressures at a lower film thickness for a given load. Grooves redistributed pressure and reduced both the maximum pressure and the minimum film thickness, but they also lowered the frictional torque. Quantitatively, the hydrogen-lubricated grooved bearing reduced the frictional torque by up to 50% compared with the non-grooved air-lubricated bearing at the same load. Relative to air, hydrogen increased stiffness and damping by up to 10% and 50%, respectively, and raised the stability threshold speed by 110%. Conversely, grooves decreased the stiffness, damping, and stability threshold speed compared with non-grooved surfaces, revealing a trade-off between friction reduction and dynamic stability. These findings provide design guidance for selecting gas media and surface texturing to tailor hybrid gas journal bearings to application-specific requirements.

Paper spray ionization mass spectrometry (PSI-MS) enables rapid analysis with minimal sample preparation, yet negative-ion mode performance has been limited by poor sensitivity and unstable signals, similar to conventional electrospray ionization. In this study, we optimized negative PSI tandem MS (MS/MS) for twelve endocrine-disrupting chemicals (EDCs) and related biomarkers—including bisphenols, phthalates, parabens, and substituted phenols—used as model analytes. A systematic solvent and additive screen identified 1 mM ammonium fluoride in methanol and 0.1% ammonium hydroxide in 9:1 MeOH/carbon tetrachloride as optimal conditions, providing enhanced deprotonated-ion intensities and improved stability. Calibration curves generated under these conditions showed excellent linearity, with limits of quantitation (LOQs) in the low-ppb range. Application to cosmetic formulations demonstrated reliable paraben quantitation. In fortified hand cream, LOQs below 1 mg/kg were achieved, with recoveries of 93–110% and intra- and inter-day precision below 10% RSD. Notably, PSI-MS/MS performance was comparable to LC–MS/MS, without a separation step. These results demonstrate the feasibility of optimized negative PSI-MS as a sensitive and robust tool for paraben determination in cosmetics and highlight its potential as a versatile platform for broader EDC quantification.

Dehydroandrographolide (DA), a bioactive diterpenoid from Andrographis paniculata with diverse biological activity, was investigated for its antioxidant and anti-inflammatory effects in lipopolysaccharide (LPS)-stimulated RAW264.7 macrophages and dextran sulfate sodium (DSS)-induced murine colitis. In vitro, DA inhibited the inflammatory response by modulating extracellular Signal-Regulated Kinase (Erk), c-Jun N-terminal Kinase (Jnk), p38 Mitogen-Activated Protein Kinase (P38), nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) p65 activation, and downregulated interleukin-6 (il-6) and interleukin-1β (il-1β) mRNA. It also had antioxidant effects by upregulating Nuclear Factor Erythroid 2-Related Factor 2 (Nrf2), NAD(P)H quinone dehydrogenase 1 (Nqo-1) and heme oxygenase-1 (Ho-1), promoting protein kinase B (Akt) and 5′-adenosine monophosphate-activated protein kinase-α1 (Ampk-α1) phosphorylation. DA decreased cyclooxygenase-2 (Cox-2) and inducible nitric oxide synthase (iNos) levels and alleviated intracellular reactive oxygen species (ROS) accumulation. In vivo, DA alleviated DSS-induced colitis in wild type (WT) mice by improving weight loss, disease activity index, colonic inflammation, and oxidative stress. The beneficial effects were linked to inhibiting Erk, Jnk, and P38 activation and enhancing Nrf2 signaling pathway. DA inhibited NOD-like receptor family pyrin domain-containing 3 (Nlrp3) inflammasome-mediated pryoptosis. However, DA’s protective effects were abolished in DSS-induced nrf2^−/− mice, suggesting its efficacy depends on Nrf2 signaling. Overall, DA alleviates oxidative stress, inflammatory responses, and pyroptosis in experimental colitis mice mainly by activating Nrf2 signaling pathway, highlighting its potential as a promising therapeutic option for inflammatory bowel disease.