Molerus and Wirth's heat transfer model for bubbling fluidized beds: Proposal for an extended model including immersed tube banks and particle cross-flow

While there are many semi-empirical correlations for estimating the wall-to-bed heat transfer coefficient in a fluidized bed, most are only usable in a limited range of operating conditions. The correlation developed by Molerus and Wirth (1997) provides the most expansive and successful model; however, it does not consider the influence of the properties of an immersed tube bank or a horizontal movement of particles (cross-flow). This study expands Molerus and Wirth’s correlation to include these additional influencing factors by identifying and introducing new dimensionless factors using dimensional analysis. Collected secondary data and measurements from a test rig were used to evaluate the extended model. The model’s estimates largely align with the collected secondary data and previously published models describing the influence of tube diameter and tube packing density on the wall-to-bed heat transfer coefficient. The model also provides new insight into the conditions under which a particle cross-flow contributes significantly to the wall-to-bed heat transfer coefficient. Future research should use these findings to conduct targeted measurements and further improve the model’s predictions

A spline-based stress function approach for the principle of minimum complementary energy

In computational engineering, ensuring the integrity and safety of structures in fields such as aerospace and civil engineering relies on accurate stress prediction. However, analytical methods are limited to simple test cases, and displacement-based finite element methods (FEMs), while commonly used, require a large number of unknowns to achieve high accuracy; stress-based numerical methods have so far failed to provide a simple and effective alternative. This work aims to develop a novel numerical approach that overcomes these limitations by enabling accurate stress prediction with improved flexibility for complex geometries and boundary conditions and fewer degree of freedoms (DOFs). The proposed method is based on a spline-based stress function formulation for the principle of minimum complementary energy, which we apply to plane, linear elastostatics. The method is first validated against analytical solutions and then tested on two test cases challenging for current state-of-the-art numerical schemes—a bi-layer cantilever with anisotropic material behavior and a cantilever with a non-prismatic, parabolic-shaped beam geometry. Results demonstrate that our approach, unlike analytical methods, can be easily applied to general geometries and boundary conditions, and achieves stress accuracy comparable to that reported in the literature for displacement-based FEMs, while requiring significantly fewer DOFs. This novel spline-based stress function approach thus provides an efficient and flexible tool for accurate stress prediction, with promising applications in structural analysis and numerical design

Sustainable phosphate-catalyzed synthesis of non-symmetric pyrazines in water – mechanistic insights, biocatalytic applications and industrial potential

Pyrazines are pivotal flavor compounds with widespread applications in the food, pharmaceutical, and chemical industries. Their natural abundance is low, and traditional synthetic methods often involve hazardous conditions unsuitable for the food sector. In this study, we present a novel biocatalytic methodology for synthesising non-symmetric trisubstituted pyrazines using aminoacetone dimerisation followed by electrophile incorporation under environmentally benign conditions, catalyzed by phosphate anion. The approach includes the employment of l-threonine dehydrogenase from Cupriavidus necator to generate aminoacetone in situ from natural l-threonine, integrating biocatalysis with green chemistry principles. Detailed mechanistic investigations, supported by control experiments and DFT calculations, revealed the critical role of phosphate buffering, an E1cB elimination, and a tautomerisation-driven pathway for product formation. The methodology demonstrates broad substrate scope and scalability, yielding pyrazines with diverse structural modifications up to 96% yields. This work establishes a starting point for the industrial production of non-symmetric pyrazines, addressing current regulatory and environmental demands in the flavor and fragrance sector

The asymptotic of the Mullins-Sekerka and the area-preserving curvature flow in the planar flat torus

We study the asymptotic behavior of flat flow solutions to the periodic and planar two-phase Mullins-Sekerka flow and area-preserving curvature flow. We show that flat flows converge to either a finite union of equally sized disjoint disks or to a finite union of disjoint strips or to the complement of these configurations exponentially fast. A key ingredient in our approach is the derivation of a sharp quantitative Alexandrov inequality for periodic smooth sets

A distributed-parameter observer for estimating the distribution of concentrations in a tubular protein refolding reactor

Inclusion bodies formed during recombinant protein expression in Escherichia coli serve as a high-yield source of target proteins but require refolding to regain their native structure. Continuous refolding processes, such as tubular refolding, provide advantages over batch and fed-batch methods by enabling precise control of key process variables like concentration, temperature and refolding time. In this contribution, a real-time monitoring framework based on an extended Kalman filter configuration is presented. This filter configuration enables accurate, dynamic concentration distribution estimation by fusing continuous inline measurements of refolded protein with periodic at-line measurements of aggregated species. The combination of both measurements enhances observer precision and ensures consistent product quality. To further improve the estimation, the observer scheme is augmented to simultaneously estimate reaction rates during operation. For determining the optimal sensor locations, the impact of the measurement positions along the tube on the accuracy of the state estimates is studied. The model-based extended Kalman filter utilizes a partial differential equation model that captures the protein refolding kinetics within a tubular reactor. Coupled with a reduced-order model, real-time capable and feasible state correction is possible. This hybrid monitoring strategy improves refolding efficiency, yield, and scalability for large-scale protein production applications. Using a simulated reality, the observer methodology is validated and compared to an open-loop simulation of the refolding process

Construction and spectrum of the AndersonHamiltonian with white noise potential on ℝ2 and ℝ3

We propose a simple construction of the Anderson Hamiltonian with white noise potential on R² and R³ based on the solution theory of the parabolic Anderson model. It relies on a theorem of Klein and Landau (1981) that associates a unique self-adjoint generator to a symmetric semigroup satisfying some mild assumptions. Then, we show that almost surely the spectrum of this random Schrödinger operator is R. To prove this result, we extend the method of Kotani (1985) to our setting of singular random operators

The fully-automatic Sentinel-1 Global Flood Monitoring service: Scientific challenges and future directions

Sentinel-1 is a unique resource for global flood monitoring, providing systematic, weather-independent Synthetic Aperture Radar (SAR) imagery with unprecedented coverage. To overcome limitations of on-demand flood mapping services that depend on human operators to collect and interpret satellite images, a fundamentally new approach was adopted by the Global Flood Monitoring (GFM) service. This service, which was launched in 2021 as part of the Copernicus Emergency Management Service (CEMS), processes all Sentinel-1 land images acquired in VV polarisation fully automatically in near-real time. This article presents the first comprehensive analysis of GFM’s scientific achievements and challenges during its initial years of operation. To map floods reliably under diverse environmental conditions, GFM combines three complementary flood-mapping algorithms with reference water datasets to differentiate flooded areas from permanent and seasonal water bodies. The service also offers a novel flood-likelihood layer and contextual information to highlight areas where flood mapping is unreliable or not feasible. These data layers were derived from a global 20 m backscatter datacube containing approximately 379 billion land surface pixels. This datacube also made it possible to generate the first global Sentinel-1 flood archive (2015 to present). Our performance analysis shows that GFM typically delivers flood maps within five hours of image acquisition. However, a significant percentage of floods may go undetected due to coverage gaps. Initial evaluation results show that good accuracies are achieved for larger-scale floods and regions in the temperate and tropical zones, while accuracies are lower for smaller-scale floods and arid environments. The GFM service will continue to improve service quality by enhancing flood detection capabilities using improved algorithms and additional data, such as the VH channel from Sentinel-1 or L-band data from the upcoming ROSE-L mission

Intercomparison of Earth Observation products for hyper-resolution hydrological modelling over Europe

The increasing frequency and severity of hydrological extremes demand the development of early warning systems and effective adaptation and mitigation strategies. Such systems and strategies require spatially detailed hydrological predictions, mostly provided by hydrological models. However, current state-of-the-art hydrological predictions remain limited in their spatial resolution. A proposed solution is the integration of high-resolution (0.5 with the benchmark reference over most areas and are recommended for hyper-resolution hydrological modelling over Europe. The MODIS (resolution of 250 m) and Sentinel-2/Landsat-8 (resolution of 20/30 m) snow cover products showed the highest classification accuracy and were selected as the best choice for the use of snow cover area products in hyper-resolution hydrological modelling. For surface soil moisture, the NSIDC SMAP product at 1 km resolution yielded correlation coefficients >0.6 at most stations and is recommended for hyper-resolution hydrological modelling. Finally, evapotranspiration products showed similar performances at the selected flux sites (correlations coefficients > 0.8). While the MODIS-Terra/Aqua evapotranspiration products (MOD16A2/MYD16A2) offer higher spatial resolution (500 m), making them potentially advantageous for hyper-resolution hydrological modelling, their temporal resolution is coarser (8-day intervals). In contrast, products like ETMonitor (1 km), ALEXI, and HOLAPS (5 km) provide daily estimates, albeit at lower spatial resolution. The assimilation of the proposed high-resolution products in models individually or in combination could lead us to hyper-resolution hydrological modelling. Still, developing integration workflows is required to overcome difficulties related to scale mismatches and data-gaps

Quality in the circular economy: What does it mean, and how is it measured?

Quality is increasingly recognized as a critical factor for achieving functional value retention in the circular economy. However, the concept remains poorly defined and inconsistently measured, limiting its integration into circularity assessments. This article develops a typology that distinguishes six types of quality metrics based on two dimensions: the targeted resource state (material, component, or product) and the nature of the quality characteristics assessed (intrinsic or extrinsic). It also identifies three assessment levels (generic, application-specific, and indirect) reflecting how quality is evaluated in practice. To illustrate the typology, existing approaches that quantify changes in resource quality were collected and positioned within the typology. This analysis highlights the diversity of methodological strategies and provides guidance on when to use which type of metric. While no single metric can fully capture the multifaceted nature of resource quality, the typology provides a practical foundation for more consistent quality assessments in circular economy research

Sparse outlier-robust PCA for multi-source data

Sparse and outlier-robust principal component analysis (PCA) has been a very active field of research recently. Yet, most existing methods apply PCA to a single data set whereas multi-source data—i.e. multiple related data sets requiring joint analysis—arise across many scientific areas. We introduce a novel PCAmethodologythatsimultaneously(i)selects important features, (ii) allows for the detection of global sparse patterns across multiple data sources as well as local source specific patterns, and (iii) is resistant to outliers. To this end, we develop a regularization problem with a penalty that accommodates global-local structured sparsity patterns and where an outlier-robust covariance estimator, namely the ssMRCD, is used as plug-in to permit joint, robust analysis across multiple data sources. We provide an efficient implementation of our proposal via the alternating direction method of multipliers and illustrate its practical advantages in simulations and in applications