AbstractA mechanical model and numerical method for the simultaneous analysis of Reissner–Mindlin shells with geometries implied by a continuous set of level sets (isosurfaces) over some three‐dimensional bulk domain is presented. A three‐dimensional mesh in the bulk domain is used in a tailored FEM formulation where the elements are by no means conforming to the level sets representing the shape of the individual shells. However, the shell geometries are bounded by the intersection curves of the level sets with the boundary of the bulk domain so that the boundaries are meshed conformingly. This results in a method which was coined Bulk Trace FEM before. The simultaneously considered, continuously embedded shells may be useful in the structural design process or for the continuous reinforcement of bulk domains. Numerical results confirm higher‐order convergence rates.

Reliable experimental data and models are required to better understand and design chemical looping processes with oxygen carrier materials like ilmenite. A dubious variability of suggested kinetics for similar oxygen carrier materials has been presented in the literature. Part I of this work focuses on thermogravimetric analysis (TGA) of gas–solid kinetics and addresses several of its challenges, which are possible reasons behind such deviations. The reduction of synthetic ilmenite (60 mass% Fe2O3＋40 mass% TiO2) powder with H2 in a TGA system was investigated for this purpose.Multiple steps were necessary to overcome mass transfer limitations during the measurements: (i) small sample masses down to 1.6mg, (ii) high gas flow rates, (iii) a suitable sample carrier and (iv) proper sample dispersion on the sample carrier. Three types of sample carriers (crucible, basket and plate) were tested; the plate showed the best performance overall. It was alarming that an exemplary increase in sample mass from 1.6 to 3mg, which was still significantly lower than all other studies reviewed, already introduced a noticeable influence of diffusion. Isothermal (650–950°C, 17–50vol% H2) and nonisothermal parameter studies were conducted and yielded vastly different isoconversional activation energies. A computational fluid dynamics (CFD) study of the TGA system suggested considerable axial dispersion of H2 influencing the initial conversion period.These findings help to assess the reliability of kinetic studies and guide towards diffusion-free, kinetic measurements. The results will be used for model development in part II.

Automata learning enables model-based analysis of black-box systems by automatically constructing models from system observations, which are often collected via testing. The required testing budget to learn adequate models heavily depends on the applied learning and testing techniques. Test cases executed for learning (1) collect behavioural information and (2) falsify learned hypothesis automata. Falsification test-cases are commonly selected through conformance testing. Active learning algorithms additionally implement test-case selection strategies to gain information, whereas passive algorithms derive models solely from given data. In an active setting, such algorithms require external test-case selection, like repeated conformance testing to extend the available data. There exist various approaches to learning and conformance testing, where interdependencies among them affect performance. We investigate the performance of combinations of six learning algorithms, including a passive algorithm, and seven testing algorithms by performing experiments using 153 benchmark models. We discuss insights regarding the performance of different configurations for various types of systems. Our findings may provide guidance for future users of automata learning. For example, counterexample processing during learning strongly impacts efficiency, which is further affected by testing approach and system type. Testing with the random Wp-method performs best overall, while mutation-based testing performs well on smaller models.

In order to ensure the proper function of the cementless hip implant, the connection between the femoral bone and the implant has to be as strong as possible. According to experimental studies, implants with a rough surface reduce micro-movements between femoral bone and implant, which helps form a stronger connection between them. The goal of this study was to analyze how half-cylinder surface topographies of different diameter values affect shear stress values and their distribution on the surface of the hip implant and trabecular femoral bone. Nine models with different half-cylinder diameter values (200 μm, 400 μm, and 500 μm) and distances between half-cylinders were created for the analysis using the finite element method. Each model consisted of three layers: implant, trabecular, and cortical femoral bone. For all three diameter values, the highest shear stress value, for the implant layer, was located after the first half-cylinder on the side where force was defined. For the trabecular bone, the first half-cylinder was under lower amounts of shear stress. If we only consider shear stress values, we can say that models with 400 μm and 500 μm diameter values are a better choice than models with 100 μm diameter values.

This paper presents a factor graph formulation and particle-based sum-product algorithm (SPA) for robust sequential localization in multipath-prone environments. The proposed algorithm jointly performs data association, sequential estimation of a mobile agent position, and adapts all relevant model parameters. We derive a novel non-uniform false alarm (FA) model that captures the delay and amplitude statistics of the multipath radio channel. This model enables the algorithm to indirectly exploit position-related information contained in the multipath components (MPCs) for the estimation of the agent position without using any prior information such as floorplan information or training data. Using simulated and real measurements in different channel conditions, we demonstrate that the algorithm can provide high-accuracy position estimates even in fully obstructed line-of-sight (OLOS) situations and show that the performance of our algorithm constantly attains the posterior Cramér-Rao lower bound (P-CRLB), facilitating the additional information contained in the presented FA model. The algorithm is shown to provide robust estimates in both, dense multipath channels as well as channels showing specular, resolved MPCs, significantly outperforming state-of-the-art radio-based localization methods.

In this brief, we realize different architectural techniques for improving the performance of post-quantum cryptography (PQC) algorithms when implemented as hardware accelerators on an application-specific integrated circuit (ASIC) platform. Having SABER as a case study, we designed a 256-bit wide architecture geared for high-speed cryptographic applications that incorporates smaller and distributed SRAM memory blocks. Moreover, we have adapted the building blocks of SABER to process 256-bit words. We have also used a buffering technique for efficient polynomial coefficient multiplications to reduce the clock cycle count. Finally, double-sponge functions are combined serially (one after another) in a high-speed KECCAK core to improve the hash operations of SHA/SHAKE. For key-generation, encapsulation, and decapsulation operations of SABER, our 256-bit wide accelerator with a single sponge function is 1.71x, 1.45x, and 1.78x faster than the raw clock cycle count of a serialized SABER design. Similarly, our 256-bit implementation with double-sponge functions takes 1.08x, 1.07x & 1.06x fewer clock cycles compared to its single-sponge counterpart. The studied optimization techniques are not specific to SABER – they can be utilized for improving the performance of other lattice-based PQC accelerators.