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Abstract In the context of automated driving, the connected and automated vehicles (CAVs) technology unlock the energy saving potential. This paper develops an LSTM‐based deep learning framework for eco‐driving adaptive identification on Intelligent vehicle multivariate time series data. The framework can be adapted for Driver Assistance Systems (DAS) to reduce fuel consumption. Specifically, considering overtaking on rural road is a critical maneuver for operation and has potential to reduce consumption, a simulated driving experiment with 30 participants was conducted to collect the multivariate time series data of the overtaking operation behaviors in conditional automation driving. Driving behaviors were classified into eco‐driving operation behaviors and high fuel consumption operation behaviors based on fuel consumption calculated by using vehicle specific power (VSP). Significance analysis based on linear regression was adopted to identify operation behaviors, and an eco‐driving behavior identification model was established with the use of long short‐term memory (LSTM) for multivariate classification theory. Meanwhile, the other four classification algorithms were used to establish identification models for comparison. The results indicated that the gear position, lane position, the acceleration pedal depth, the clutch pedal depth, and the brake pedal depth had a significant influence on fuel consumption. The eco‐driving behavior identification model of overtaking demonstrated a high classification power and robustness with a classification accuracy of 89.16%. According to the simulation results, the developed adaptive identification model is with promising performance. The conclusions provide theoretical support for developing an adaptive strategy for connected eco‐driving.

This paper presents a stochastic analysis of the size effect on the nominal strength of quasibrittle structures with a large preexisting notch. This type of scaling behavior has been extensively studied within a deterministic framework. Little attention is paid to stochastic analysis, which is essential for reliability-based analysis and design of engineering structures. Stochastic finite element simulations are performed to study the failure of geometrically similar beams of different sizes. The numerical analysis uses a continuum damage model, in which the tensile strength and fracture energy are modeled by homogeneous random fields. Different correlation lengths are considered as a parametric study. The analysis yields the size effects on the mean and coefficient of variation (CoV) of the nominal structural strength. It is shown that the size effect on the mean strength agrees well with the Bazant size effect model. The simulation predicts a strong size effect on the CoV of the nominal strength. Small-, intermediate-, and large-size asymptotes are derived analytically for the scaling behavior of the strength CoV. Based on these asymptotes, an approximate scaling equation is proposed for the CoV of nominal strength. The effect of the correlation length on the simulated failure behavior is discussed. [ABSTRACT FROM AUTHOR]

Helicostoa sinensis E. Lamy, 1926 is a unique freshwater gastropod species with a sessile habit. This enigmatic species was first found cemented on river limestones from China about 120 years ago and described together with the genus. It was never collected again and has been considered monotypic. Here, we report the rediscovery of Helicostoa from several rivers in China, and describe a second species of this genus based on a comprehensive study. In addition to the unique sessile habit of both species, the new Helicostoa species presents one of the most remarkable cases of sexual dimorphism within molluscs. Only the adult female is sessile and the original aperture of the female is sealed by shell matter or rock, while an opening on the body whorl takes the function of the original aperture. The male is vagile, with a normal aperture. Our results confirm the recently suggested placement of Helicostoa within the family Bithyniidae. The sessility of Helicostoa species is considered as an adaption to the limestone habitat in large rivers. The extreme sexual dimorphism and secondary aperture of females are considered as adaptations to overcome the obstacles for mating and feeding that come with a sessile life style. [ABSTRACT FROM AUTHOR]

Promoting the hydrogen oxidation reaction (HOR) activity and poisoning tolerance of electrocatalysts is crucial for the large-scale application of hydrogen-oxygen fuel cell. However, it is severely hindered by the scaling relations among different intermediates. Herein, lattice-contracted Pt-Rh in ultrasmall ternary L12-(Pt0.9Rh0.1)3V intermetallic nanoparticles (~2.2 nm) were fabricated to promote the HOR performances through an oxides self-confined growth strategy. The prepared (Pt0.9Rh0.1)3V displayed 5.5/3.7 times promotion in HOR mass/specific activity than Pt/C in pure H2 and dramatically limited activity attenuation in 1000 ppm CO/H2 mixture. In-situ Raman spectra tracked the superior anti-CO* capability as a result of compressive strained Pt, and the adsorption of oxygen-containing species was promoted due to the dual-functional effect. Further assisted by density functional theory calculations, both the adsorption of H* and CO* on (Pt0.9Rh0.1)3V were reduced compared with that of Pt due to lattice contraction, while the adsorption of OH* was enhanced by introducing oxyphilic Rh sites. This work provides an effective tactic to stimulate the electrocatalytic performances by optimizing the adsorption of different intermediates severally., (© 2024 Wiley‐VCH GmbH.)

Localized deformation and randomly shaped imperfections are salient features of buckling-type instabilities in thin-walled load-bearing structures. However, it is generally agreed that their complex interactions in response to mechanical loading are not yet sufficiently understood, as evidenced by buckling-induced catastrophic failures which continue to today. This study investigates how the intimate coupling between localization mechanisms and geometric imperfections combine to determine the statistics of the pressure required to buckle (the illustrative example of) a hemispherical shell. The geometric imperfections, in the form of a surface, are defined by a random field generated over the nominally hemispherical shell geometry, and the probability distribution of the buckling pressure is computed via stochastic finite element analysis. Monte-Carlo simulations are performed for a wide range of the shell's radius to thickness ratio, as well as the correlation length of the spatial distribution of the imperfection. The results show that over this range, the buckling pressure is captured by the Weibull distribution. In addition, the analyses of the deformation patterns observed during the simulations provide insights into the effects of certain characteristic lengths on the local buckling that triggers global instability. In light of the simulation results, a probabilistic model is developed for the statistics of the buckling load that reveals how the dimensionless radius plays a dual role which remained hidden in previous deterministic analyses. The implications of the present model for reliability-based design of shell structures are discussed., Competing Interests: Competing interests statement:The authors declare no competing interest.