Danish Geotechnical Society

• Textile wastewater was treated by simultaneous forward osmosis and reverse osmosis. • FO draw solution was continuously concentrated by RO. • Chemical oxygen demand and total organic carbon were rejected above 99%. • Simultaneously, clean water was obtained as the reverse osmosis permeate. The concerning and abundant textile wastewater can be treated by forward osmosis (FO) in order to reduce its volume and simultaneously recover clean water. However, the productivity of FO depends on the concentration of the draw solution that is used. In this work, a simultaneous application of FO and reverse osmosis (RO) is proposed. The HFFO14® FO membrane (Aquaporin, Denmark) was employed to concentrate a real textile wastewater, whereas the SW30-2540 (DuPont, USA) RO membrane was employed to simultaneously regenerate the draw solution, which consisted in a 0.7 M NaCl solution, and to obtain a clean water stream. The concentration of the textile wastewater increased until 90% water recovery was achieved. The rejection values obtained for the chemical oxygen demand and total organic carbon were in the range 99 – 100%. Afterwards, the previously concentrated textile wastewater was again processed until a volume concentration factor of 16.5 was reached. Stable values of permeate flux (around 4 L/h·m 2 ) were obtained in the FO process, whereas the reverse osmosis step permitted the maintenance of a stable conductivity in the draw solution and provided clean water as permeate.

1. To develop a deep-learning segmentation model for automated measurement of maximal aortic diameter (Dmax) and volumes of aortic dissection components: true-lumen (TL), circulating false-lumen (CFL), and thrombus (Th) on CT angiography (CTA). 2. To assess the predictive value of these measures for adverse aortic remodeling in residual aortic dissection (RAD). This retrospective study included 322 patients from two centers. The segmentation model was trained on 120 patients (Center 1) and tested on an internal dataset (30 patients, Center 1) and an external dataset (10 patients, Center 2) in terms of Dice Similarity Coefficient (DSC). The model extracted Dmax, global false-lumen volume (FLGlo = CFL + Th), and local false-lumen volume (FLLoc, measured 3 cm around the largest diameter). Clinical validation was performed on 83 patients from Center1 (internal validation, 2-year follow-up) and 79 patients from Center2 (external validation, 4.5-year follow-up). The segmentation model achieved high accuracy (Center 1, DSC: 0.93 TL, 0.93 CFL, 0.87 Th; Center 2, DSC: 0.92 TL, 0.93 CFL, 0.84 Th) with strong agreement between automated and manual measurements. Aortic remodeling occurred in 39/83 patients (46.9%) from Center1 and 33/79 patients (41.7%) from Center2. Aortic remodeling occurred in 39/83 patients (47%) from Center1 and 33/80 (42%) from Center2. FLLoc outperformed Dmax and FLGlo (Center 1: AUC = 0.83, 0.73, and 0.76; Center 2: AUC = 0.77, 0.64, and 0.70). At optimal thresholds, FLLoc showed good predictive performance (Center 1: Sensitivity = 0.87, Specificity = 0.68). Deep-learning segmentation provides accurate aortic measurements. Local false-lumen volumes predict adverse aortic remodeling in RAD better than diameter and global false-lumen volumes. Question In residual aortic dissection (RAD) after type-A dissection, early identification of high-risk patients on initial CT angiography is crucial for endovascular treatment decisions. Findings False-lumen local volumes (3 cm around aortic dissection maximal diameters), obtained with an automatic deep-learning method, predict adverse remodeling better than diameter or global false-lumen volumes. Clinical relevance A deep-learning segmentation method of aortic dissection components on CTA, enabling automatic measurements of diameters and volumes is feasible. It provides local false-lumen volumes, a better predictive marker of adverse aortic remodeling than the currently used diameters and global volumes.

Chemical risk assessment is evolving from traditional deterministic approaches to embrace proba­bilistic methodologies, where risk of hazard manifestation is understood as a more or less probable event depending on exposure, individual factors, and stochastic processes. This is driven by advancements in human stem cells, complex tissue engineering, high-performance computing, and cheminformatics, and is more recently facilitated by large-scale artificial intelligence models. These innovations enable a more nuanced understanding of chemical hazards, capturing the complexity of biological responses and variability within populations. However, each technology comes with its own uncertainties impacting on the estimation of hazard probabilities. This shift addresses the limitations of point estimates and thresholds that oversimplify hazard assessment, allowing for the integration of kinetic variability and uncertainty metrics into risk models. By leveraging modern technologies and expansive toxicological data, probabilistic approaches offer a comprehensive evaluation of chemical safety. This paper summarizes a workshop held in 2023 and discusses the technological and data-driven enablers, and the challenges faced in their implementation, with particular focus on perturbation of biology as the basis of hazard estimates. The future of toxico­logical risk assessment lies in the successful integration of these probabilistic models, promising more accurate and holistic hazard evaluations.

Soft intertwined channel systems are frequently found in fluid flow networks in nature. The passage geometry of these systems can deform due to fluid flow, which can cause the relationship between flow rate and pressure drop to deviate from the Hagen–Poiseuille linear law. Although fluid–structure interactions in single deformable channels have been extensively studied, such as in Starling's resistor and its variations, the flow transport capacity of an intertwined channel with multiple self-intersections (a ‘hydraulic knot’), is still an open question. We present experiments and theory on soft hydraulic knots formed by interlinked microfluidic devices comprising two intersecting channels separated by a thin elastomeric membrane. Our experiments show flow–pressure relationships similar to flow limitation, where the limiting flow rate depends on the knot configuration. To explain our observations, we develop a mathematical model based on lubrication theory coupled with tension-dominated membrane deflections that compares favourably with our experimental data. Finally, we present two potential hydraulic knot applications for microfluidic flow rectification and attenuation.

Two-photon lithography enables powerful nanoscale 3D printing. Unfortunately, it uses toxic photopolymers. We report a layer-by-layer digital process using condensed organic vapor thin films as the starting material. The process uses a focused electron beam to chemically cross-link the low-toxicity organic ice ito a solid and shares software and CAD databases with industrial 3D printing. Guided by electron-matter-interaction simulations, we control the cross-linking thickness between 250 nm and 2 µm. The 3D prints contained up to 500 layers, and the voxel size is about 550-nm. In addition to more sustainable materials, our digital process complements two-photon lithography in three areas; (i) it is compatible with chemistry beyond photopolymers, (ii) we can print delicate suspended structures and tubes because our structures are not immersed in liquid resins that reside in cavities and destroy structures by interfacial forces, (iii) free-hanging structures are printed without sacrificial supports. Nanophotonics and microfluidics applications are demonstrated.

International audience

This paper presents a quantitative program verification infrastructure for discrete probabilistic programs. Our infrastructure can be viewed as the probabilistic analogue of Boogie: its central components are an intermediate verification language (IVL) together with a real-valued logic. Our IVL provides a programming-language-style for expressing verification conditions whose validity implies the correctness of a program under investigation. As our focus is on verifying quantitative properties such as bounds on expected outcomes, expected run-times, or termination probabilities, off-the-shelf IVLs based on Boolean first-order logic do not suffice. Instead, a paradigm shift from the standard Boolean to a real-valued domain is required. Our IVL features quantitative generalizations of standard verification constructs such as assume- and assert-statements. Verification conditions are generated by a weakest-precondition-style semantics, based on our real-valued logic. We show that our verification infrastructure supports natural encodings of numerous verification techniques from the literature. With our SMT-based implementation, we automatically verify a variety of benchmarks. To the best of our knowledge, this establishes the first deductive verification infrastructure for expectation-based reasoning about probabilistic programs.

The Dense Discrete Phase Model, DDPM, was used to investigate erosion, caused by the impacts of solid particles, in an industrial cyclone preheater by application of Oka's erosion model. The model predictions of flow pattern and temperature profile were validated by measurements in an industrial cyclone preheater. Then, the erosion model was tested against the results in a lab-scale cyclone reported in the literature. Furthermore, the erosion patterns in the industrial cyclone preheater were predicted by the model. The predicted results were qualitatively matching with the erosion observed in the cyclone. In addition, parametric studies on key operating conditions were performed and the impacts on erosion as well as pressure drop and heat transfer rate were evaluated. The results indicate that gas flow rate and inlet temperature have the greatest influence on erosion. The erosion rate is largely unaffected by solid flow rate and particle size in the studied range.