Accurate registration of multi-stained whole-slide images (WSIs) enables the integration of complementary morphological and molecular information, improving both clinical diagnostic and prognostic analysis. It also enables efficient transfer of annotations between consecutive or re-stained slides, significantly reducing annotation time and cost, as well as three-dimensional tissue reconstruction. Despite its importance, WSI registration remains challenging due to variations in slide preparation across stains and complex tissue deformations. Existing methods are often tailored to specific staining modalities and fail to generalize across diverse settings, highlighting the need for a robust and generalizable cell-level multi-stain registration method. In this work, we introduce CORE, a novel coarse-to-fine framework for accurate cell-centric registration across six multimodal WSI datasets, encompassing 30 distinct staining types, including Hematoxylin & Eosin (H&E), periodic acid–Schiff (PAS), multiplex immunohistochemistry (mIHC), multiplex immunofluorescence (mIF) and Cyclic immunofluorescence (Cyc-IF). CORE first performs coarse global registration by extracting tissue masks through prompt-based segmentation to remove artifacts and non-tissue regions, followed by rigid alignment using tissue morphology and a pre-trained feature extractor. The resulting alignment is refined using a shape-aware point-set registration model applied to automatically detected nuclei centroids, enabling fine-grained rigid alignment. Finally, Coherent Point Drift (CPD) is used to estimate a non-linear displacement field for non-rigid cellular alignment. Using nuclei correspondences throughout the pipeline, CORE achieves accurate and robust cell-level alignment across modalities.Experimental results show that CORE consistently outperforms state-of-the-art methods in accuracy, robustness, and generalization across both bright-field and immunofluorescence WSIs.
Background Active site leaching of CaO-based catalysts severely limits their durability and reusability in biodiesel production. To address this issue, this study aims to enhance the stability of CaO catalysts through cerium (Ce) modification and rational design of Ce/Ca bimetallic metal-organic frameworks (MOFs). Methods A series of Ce/Ca MOF-derived catalysts were synthesized by varying the Ce/Ca molar ratio and calcination temperature. The effects of composition and structural evolution on catalytic performance were systematically investigated using TG, XRD, BET, FTIR, SEM, and XPS techniques. Significant Findings Results indicate that the incorporation of Ce would induce lattice distortions of Ca, thus increasing the oxygen vacancies. To maintain electrical neutrality, electrons tend to cluster around Ca; as a result, the electron density around Ca increased. This confirms the strong electronic interaction between Ca and Ce, which is the reason for the enhanced stability of CaO. The optimized catalyst, C3-800N (Ce:Ca = 3:10, calcined at 800 °C under N2) exhibited remarkable reusability and strong tolerance to water and free fatty acids, with only a 2.17% decline in activity after eight cycles under mild conditions (65 °C, 8 wt.% catalyst, methanol-to-oil molar ratio = 9:1, 1 h). This work demonstrates a feasible strategy for developing durable and efficient MOF-derived Ca-based solid base catalysts for sustainable biodiesel production.
This review critically evaluates Mg-mediated routes for producing non-ferrous metals and related materials, including magnesiothermic extraction and MgCl2-assisted electrochemical processing. The discussion first clarifies the thermodynamic basis and metallurgical significance of Mg before analyzing the reaction pathways, kinetics, and transport limitations in direct solid-state reduction and gas-phase Mg infiltration. Mg-mediated extraction of Ti, Zr, rare earth elements, Nb, Ta, and Si is subsequently examined with emphasis on the coupling of reduction behavior with separation, purification, morphology control, and by-product management. The review also assesses engineering barriers, including Mg vapor containment, reactor corrosion, MgO separation, and the energy penalty of Mg regeneration. Electrochemical processing in molten halides is discussed with particular attention to MgCl2 as a chlorinating agent, an electroactive melt component, and a functional additive that improves process feasibility and efficiency. Finally, future directions are outlined for closed-loop Mg metallurgy and hybrid thermochemical-electrochemical extraction, focusing on electrolysis hardware, Mg recovery, and heat integration. By linking magnesiothermic reduction with molten-halide electrochemistry, this review positions Mg as both an effective reductant and a recyclable redox carrier for low-carbon non-ferrous extractive metallurgy.
Reconfigurable manufacturing requires robotic workcells that can adapt to changing products, processes, and equipment without extensive redesign or manual reprogramming. This paper presents the R3M framework, a ROS 2 driven integration architecture that connects model based manufacturing knowledge with execution level robotic control. Product, process, and equipment information is formalised through UML domain models and serialised in AutomationML(AML), enabling automated correspondence between assembly requirements, available skills, and executable recipes. The framework integrates automated programme generation, reinforcement learning based recipe optimisation, and a modular CAD informed six degree of freedom perception layer to support both technical and semantic interoperability across simulated and physical workcells. The approach is evaluated through Cube Kitting and Cylinder Stacking use cases implemented on distinct robotic platforms, including ABB and Universal Robots systems. Experimental results show high reliability, with environment launch performance reaching up to 99.93%, standard skill sequences achieving 100% execution success, and full use case trials exceeding 97% success in simulation and reaching 100% on physical hardware. These findings demonstrate that R3M provides a scalable foundation for adaptive robotic manufacturing, reducing programming effort while supporting modular substitution, robust perception, and simulation to real deployment.
Polyolefins constitute the largest fraction of plastic waste and remain difficult to recycle using conventional mechanical routes. Supercritical water liquefaction (SCWL) enables the conversion of these chemically resistant polymers into refinery-compatible hydrocarbons, but progress towards scale-up is constrained by several factors, including the limited availability of kinetic models that describe product distribution as a function of operating conditions. This study develops a refinery-aligned lumped kinetic framework for the SCWL of polypropylene (PP) and low-density polyethylene (LDPE) using true boiling point (TBP)-derived pseudo-components obtained from thermogravimetric data. Four reaction network structures are evaluated, namely the Cascade Degradation Model, the Constrained Parallel Model, the Extended Parallel Model, and the Fully Connected Network. Model performance is assessed using statistical fit, information criteria, and Arrhenius behaviour. The results show that model performance is governed by pathway connectivity rather than model complexity alone. For PP, the Constrained Parallel Model provides the most appropriate representation, indicating a pathway-dominated degradation mechanism. For LDPE, the Extended Parallel Model is required to capture distributed and overlapping reaction pathways. Fully Connected Networks do not improve the model fidelity and introduce redundant parameters. These results establish that the optimal kinetic representation corresponds to the minimum pathway connectivity required to resolve concurrent reactions. The framework provides a refinery-aligned kinetic model of polyolefin liquefaction, with the fitted parameters and lump definitions directly applicable to reactor modelling, process simulation, and integration with downstream refining operations.
