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The immersed tunnel is considered an effective solution for traffic problems across rivers and seas. The sand filling layer, as an important part of immersed tunnel foundation treatments, directly affects segment attitude stability. Due to difficulties in quality control of concealed construction and the complex hydrodynamic environment, the sand filling layer is prone to compaction defects, further leading to changes in segment attitude. However, limited by structural concealment and state complexity, most studies consider the sand filling layer part of the foundation to study its impact on settlement while neglecting its influence on segment attitude. This research proposes an evaluation method for the sand filling layer state based on elastic wave testing and the elastic wave characteristic parameters selected come from analysis of the time domain, frequency domain and time-frequency domain. By classifying the elastic wave characteristic parameters through the K-means clustering method, the relationship between the state of the sand filling layer and the elastic wave characteristic parameters is established. The state of the sand filling layer is divided into dense, incompact, and void. A numerical model is established based on the Guangzhou BI-UT immersed tunnel with incompact and void sand filling layer states to simulate deformation and torsion. The results indicate that the settlement of the tunnel segment is low in the eastern region and high in the western region due to the presence of a less dense sand filling layer, with a maximum differential settlement of 0.04m. The evaluation method plays a crucial role in guiding the construction of immersed tube tunnels.

Icing of water (moisture) at subzero temperatures with different length scales is harmful to a variety of applications spanning from large-scale aircraft to small camera lens. Existing strategies relying on controlling the surface structure and material are encumbered with shortcomings of short frost delay time, poor durability, and difficulty in large-scale production. Inspired from the mucus secretion of mollusks, we introduce organohydrogel dynamic interfaces that can perform dynamic and reversible exchange of the cryoprotectant and water at the interface, resulting in exceptional antifrosting, antiadhesion, and deicing properties with long-term durability. The replenishable coating shows superlubrication to the surface ice with a sliding angle up to 1.9 ± 0.4oand a frost delay time up to 970 ± 31 min in the presence of water spray (99% relative humidity) at subzero temperatures. The strategy offers a reliable and scalable solution to prevent engineering surfaces, i.e., aircraft, pavement, bridge, and other public facilities, from icing/frosting and ice adhesion, even under extreme cold environments.

There is growing experimental and theoretical evidence that vibronic couplings, couplings between electronic and nuclear degrees of freedom, play a fundamental role in ultrafast excited-state dynamics in organic donor–acceptor hybrids. Whereas vibronic coupling has been shown to support charge separation at donor–acceptor interfaces, so far, little is known about its role in the real-space transport of charges in such systems. Here we theoretically study charge transport in thiophene:fullerene stacks using time-dependent density functional tight-binding theory combined with Ehrenfest molecular dynamics for open systems. Our results reveal coherent oscillations of the charge density between neighboring donor sites, persisting for ∼200 fs and promoting charge transport within the polymer stacks. At the donor–acceptor interface, vibronic wave packets are launched, propagating coherently over distances of more than 3 nm into the acceptor region. This supports previous experimental observations of long-range ballistic charge-carrier motion in organic photovoltaic systems and highlights the importance of vibronic coupling engineering as a concept for tailoring the functionality of hybrid organic devices.

Flood frequency analysis generally involves the use of simple parametric probability distributions to smooth and extrapolate the information provided by short flood records to estimate extreme flood flow quantiles. Parametric probability distributions can have difficulty simultaneously fitting both the largest and smallest floods. A danger is that the smallest observations in a record can distort the exceedance probabilities assigned to the large floods of interest. The identification and treatment of such Potentially Influential Low Floods(PILFs) frees a fitting algorithm to describe the distribution of the larger observations. This can allow parametric flood frequency analysis to be both efficient, and also robust to deviations from the proposed probability model's lower tail. Historically, PILF identification involved subjective judgement. We propose a new multiple Grubbs-Beck outlier test (MGBT) for objective PILF identification. MGBT PILF identification rates (akin to Type I errors) are reported for the lognormal (LN) distribution and the log-Pearson Type III (LP3) distribution with a variety of skew coefficients. MGBT PILF identification generally matched subjective identification from a recent California flood frequency study. Monte Carlo results show that censoring of PILFs identified by the MGBT algorithm improves the extreme quantile estimator efficiency of the expected moments algorithm (EMA) for negatively skewed LP3 distributions and has little effect for zero or positive skews; simultaneously it protects against deviations from the LP3 in the lower tail, as illustrated by distorted LN examples. Thus, MGBT generally makes flood frequency analysis based on the LP3 distribution with EMA both more accurate and more robust. Treatment of Potentially Influential Low Floods can make flood frequency methods more robustThe MGBT outlier test can be an effective tool for identifying PILFsUsing the MGBT to identify PILFs improves robustness of the EMA method, while not losing efficiency