Seismic performance of group pile foundation with ground improvement during liquefaction

A pile foundation with ground improvement under the footing is a composite foundation with the objectives of enhancing the seismic performance and rationalizing the substructure by combining the pile foundation with ground improvement. Although the effectiveness of this method has been confirmed in previous studies for application to soft grounds, the applicability of this method to liquefiable grounds has yet to be fully investigated. In this study, therefore, centrifuge model tests and finite element analyses were conducted to clarify the effectiveness of this method and to ascertain the improvement in strength (stiffness) when the method is applied to a liquefiable ground. Firstly, in order to investigate the effect of an improved ground on the behavior of the pile foundation during liquefaction, dynamic centrifuge model tests were conducted for three cases with different strengths of the improved ground. Then, three-dimensional soil–water coupled finite element analyses of the centrifuge model experiments were performed to validate the applicability of the analytical method. After that, parametric studies, in which the strength of the improved ground and the input ground motion were changed, were conducted using the same analytical model. The results confirmed that the horizontal displacement of the pile heads was reduced by the improved ground even in the liquefiable ground, and that the effect of this reduction was more remarkable in cases of high stiffness of the improved ground. Furthermore, it was possible to reduce the bending moments at the pile heads by applying the ground improvement. However, since the bending moment at the boundary between the improved ground and the natural ground became the local maximum, there was an optimum stiffness of the ground improvement at which the maximum bending moment of the piles was reduced. This is because improving the ground around the pile heads has the same effect as extending the footing. It was thus concluded that the behavior of the pile foundation is similar to that of a composite foundation comprised of a caisson and group piles

Molecular Evolutionary Analyses of the RNA-Dependent RNA Polymerase Region in Norovirus Genogroup II

Noroviruses are the leading cause of viral gastroenteritis in humans across the world. RNA-dependent RNA polymerase (RdRp) plays a critical role in the replication of the viral genome. Although there have been some reports on a limited number of genotypes with respect to the norovirus evolution of the RdRp region, no comprehensive molecular evolution examination of the norovirus GII genotype has yet been undertaken. Therefore, we conducted an evolutionary analysis of the 25 genotypes of the norovirus GII RdRp region (full-length), collected globally using different bioinformatics technologies. The time-scaled phylogenetic tree, generated using the Bayesian Markov Chain Monte Carlo (MCMC) method, indicated that the common ancestor of GII diverged from GIV around 1443 CE [95% highest posterior density (HPD), 1336–1542]. The GII RdRp region emerged around 1731 CE (95% HPD, 1703–1757), forming three lineages. The evolutionary rate of the RdRp region of the norovirus GII strains was estimated at over 10−3 substitutions/site/year. The evolutionary rates were significantly distinct in each genotype. The composition of the phylogenetic distances differed among the strains for each genotype. Furthermore, we mapped the negative selection sites on the RdRp protein and many of these were predicted in the GII.P4 RdRp proteins. The phylodynamics of GII.P4, GII.P12, GII.P16, and GII.Pe showed that their effective population sizes increased during the period from 2003 to 2014. Our results cumulatively suggest that the RdRp region of the norovirus GII rapidly and uniquely evolved with a high divergence similar to that of the norovirus VP1 gene

JAXA-ONERA-DLR cooperation: results from rotor optimization in hover

A cooperation between JAXA, ONERA and DLR puts the focus on the aerodynamic optimization of helicopter rotors. This paper represents the conclusions from the first phase: optimization of a hovering rotor. The HART-II blade is first investigated with low-fidelity tools and compared against state-of-the art CFD simulations. Afterwards, the chord distribution and twist of the HART-II blade are optimized using the low-fidelity tools as well as CFD. Since the partners observed differences in the outcome of the CFD simulations for the low-fidelity optimized blades, a deeper investigation of the effects of the turbulence modelling approach, elasticity and grid topology is conducted. The findings show that the chosen flight condition is close to the thrust of the maximum Figure of Merit and that the vortex-triggered separation on the outboard sections of the blade has to be modelled correctly. In this study, the blade grids had the most noticeable effect on the results, followed by the turbulence model and elasticity. With respect to the optimization, low-fidelity methods require special care, whereas CFD optimized blades were found to lead to more robust designs, even though they have only been optimized for a single point. This is explained by the more accurate modelling of the stall phenomenon with respect to geometrical changes

Output Prediction Attacks on Block Ciphers using Deep Learning

Cryptanalysis of symmetric-key ciphers, e.g., linear/differential cryptanalysis, requires an adversary to know the internal structures of the target ciphers. On the other hand, deep learning-based cryptanalysis has attracted significant attention because the adversary is not assumed to have knowledge about the target ciphers with the exception of the algorithm interfaces. Such cryptanalysis in a blackbox setting is extremely strong; thus, we must design symmetric-key ciphers that are secure against deep learning-based cryptanalysis. However, almost previous attacks do not clarify what features or internal structures affect success probabilities. Although Benamira et al. (Eurocrypt 2021) and Chen et al. (ePrint 2021) analyzed Gohr’s results (CRYPTO 2019), they did not find any deep learning specific characteristic where it affects the success probabilities of deep learning-based attacks but does not affect those of linear/differential cryptanalysis. Therefore, it is difficult to employ the results of such cryptanalysis to design deep learning-resistant symmetric-key ciphers. In this paper, we propose deep learning-based output prediction attacks in a blackbox setting. As preliminary experiments, we first focus on two toy SPN block ciphers (small PRESENT-[4] and small AES-[4]) and one toy Feistel block cipher (small TWINE-[4]). Due to its small internal structures with a block size of 16 bits, we can construct deep learning models by employing the maximum number of plaintext/ciphertext pairs, and we can precisely calculate the rounds in which full diffusion occurs. Next, based on the preliminary experiments, we explore whether the evaluation results obtained by our attacks against three toy block ciphers can be applied to block ciphers with large block sizes, e.g., 32 and 64 bits. As a result, we demonstrate the following results, specifically for the SPN block ciphers: First, our attacks work against a similar number of rounds that the linear/differential attacks can be successful. Next, our attacks realize output predictions (precisely ciphertext prediction and plaintext recovery) that are much stronger than distinguishing attacks. Then, swapping or replacing the internal components of the target block ciphers affects the average success probabilities of the proposed attacks. It is particularly worth noting that this is a deep learning specific characteristic because swapping/replacing does not affect the average success probabilities of the linear/differential attacks. Finally, by analyzing the influence of the differences in the characteristics of three S-boxes (i.e., the original PRESENT S-box and two known weak S-boxes) on deep learning specific characteristics, we clarify that the resistance of the target ciphers to differential/linear attacks can affect the success probability of deep learning-based attacks. We also confirm whether the proposed attacks work on the Feistel block cipher. We expect that our results will be an important stepping stone in the design of deep learning-resistant symmetric-key ciphers

Distinctive detection of insulinoma using [¹⁸F]FB(ePEG12)12-exendin-4 PET/CT

Specifying the exact localization of insulinoma remains challenging due to the lack of insulinoma-specific imaging methods. Recently, glucagon-like peptide-1 receptor (GLP-1R)-targeted imaging, especially positron emission tomography (PET), has emerged. Although various radiolabeled GLP-1R agonist exendin-4-based probes with chemical modifications for PET imaging have been investigated, an optimal candidate probe and its scanning protocol remain a necessity. Thus, we investigated the utility of a novel exendin-4-based probe conjugated with polyethylene glycol (PEG) for [¹⁸F]FB(ePEG12)12-exendin-4 PET imaging for insulinoma detection. We utilized [¹⁸F]FB(ePEG12)12-exendin-4 PET/CT to visualize mouse tumor models, which were generated using rat insulinoma cell xenografts. The probe demonstrated high uptake value on the tumor as 37.1 ± 0.4%ID/g, with rapid kidney clearance. Additionally, we used Pdx1-Cre;Trp53R172H;Rbf/f mice, which developed endogenous insulinoma and glucagonoma, since they enabled differential imaging evaluation of our probe in functional pancreatic neuroendocrine neoplasms. In this model, our [¹⁸F]FB(ePEG12)12-exendin-4 PET/CT yielded favorable sensitivity and specificity for insulinoma detection. Sensitivity: 30-min post-injection 66.7%, 60-min post-injection 83.3%, combined 100% and specificity: 30-min post-injection 100%, 60-min post-injection 100%, combined 100%, which was corroborated by the results of in vitro time-based analysis of internalized probe accumulation. Accordingly, [¹⁸F]FB(ePEG12)12-exendin-4 is a promising PET imaging probe for visualizing insulinoma

Generation of time-multiplexed chiroptical information from multilayer-type luminescence-based circular polarization conversion films

時間変化する円偏光スペクトルの生成と読み出しに成功 --光記録や偽造防止技術への円偏光利用に期待--. 京都大学プレスリリース. 2024-02-09.Circularly polarized (CP) light generated from photoluminescence (PL) has great potential for the transmission of diverse forms of optical information including light intensity (brightness), spectral profile (color), and polarization (left-handed (LH)/right-handed (RH)), as well as temporal information corresponding to the PL lifetime of the CP light source. However, a systematic approach to the design of CP light-generating materials for the conveyance of time-multiplexed chiroptical information has not yet been reported. Herein, we demonstrate a novel approach to time-multiplexing chiroptical information using multilayered luminescence-based CP convertors comprising two linearly polarized luminescence (LPL) films with different PL lifetimes and a quarter-wave retardation film. We prepared LPL films with short and long PL lifetimes by stretching films comprising poly[2-methoxy-5-(2-ethylhexyloxy)-1, 4-phenylenevinylene] (MEH-PPV) and CdSe/CdS core/shell quantum rod (QR) luminogens, respectively. We then fabricated four types of multilayered luminescence-based CP convertors by laminating the LPLMEH-PPV and LPLQR films with quarter-wave retardation films, so that the azimuthal angles between the polarization axes of the LPL films and the fast axes of the quarter-wave films differed in each case. The resulting CP light comprised short- and long-lifetime components. Subsequently, we used a time-resolved spectroscopic technique to extract time-multiplexed chiroptical information from changes in the time-course of the spectral profile of the LH- and RH-CP light. The time-varying of CP light profiles were thereby read-out as time-multiplexed chiroptical information. Our findings will pave the way for the design of CP light-generating materials for conveying time-multiplexed chiroptical information