Neural network models for seabed stability: a deep learning approach to wave-induced pore pressure prediction

Wave cyclic loading in submarine sediments can lead to pore pressure accumulation, causing geohazards and compromising seabed stability. Accurate prediction of long-term wave-induced pore pressure is essential for disaster prevention. Although numerical simulations have contributed to understanding wave-induced pore pressure response, traditional methods lack the ability to simulate long-term and real oceanic conditions. This study proposes the use of recurrent neural network (RNN) models to predict wave-induced pore pressure based on in-situ monitoring data. Three RNN models (RNN, LSTM, and GRU) are compared, considering different seabed depths, and input parameters. The results demonstrate that all three RNN models can accurately predict wave-induced pore pressure data, with the GRU model exhibiting the highest accuracy (absolute error less than 2 kPa). Pore pressure at the previous time step and water depth are highly correlated with prediction, while wave height, wind speed, and wind direction show a secondary correlation. This study contributes to the development of wave-induced liquefaction early warning systems and offers insights for utilizing RNNs in geological time series analysis

From Transistors to Phototransistors by Tailoring the Polymer Stacking

It is universally acknowledged that highly photosensitive transistors are strongly dependent on the high carrier mobility of polymer-based semiconductors. However, the polymer π–π stacking and aggregation, required to increase the charge mobility, conversely inhibit the dissociation of photogenerated charge carriers, in turn accelerating the geminate recombination of electron-hole pairs. To explore the effects of charge mobility and polymer stacking on the photoresponsivity of the phototransistors, here, two alternating copolymers are synthesized, namely P-PPAB-IDT and P-PPAB-BDT, by palladium-catalyzed Stille coupling of PPAB with indaceodithiophene (IDT) or benzo[1,2-b:4,5-b′]dithiophene-2,6-diyl) (BDT) monomers. The polymer P-PPAB-IDT demonstrates a nearly 20 times enhancement in the hole mobility compared to P-PPAB-BDT. Yet, P-PPAB-IDT surprisingly shows no response to white light illumination, whereas P-PPAB-BDT exhibits a significant photoresponse to the same light source with a high light-current/dark-current (Ilight/Idark) ratio of 21.6 in the p-type area and a low current ratio of just 5.2 in the n-type area. It is believed that this work will provide an effective strategy to develop highly photosensitive polymer semiconductors by reducing polymer stacking and aggregation rather than improving the charge carrier mobility.acceptedVersionPeer reviewe

Current induced anisotropic magnetoresistance in topological insulator films

Topological insulators are insulating in the bulk but possess spin-momentum locked metallic surface states protected by time-reversal symmetry. The existence of these surface states has been confirmed by angle-resolved photoemission spectroscopy (ARPES) and scanning tunneling microscopy (STM). Detecting these surface states by transport measurement, which might at first appear to be the most direct avenue, was shown to be much more challenging than expected. Here, we report a detailed electronic transport study in high quality Bi2Se3 topological insulator thin films. Measurements under in-plane magnetic field, along and perpendicular to the bias current show opposite magnetoresistance. We argue that this contrasting behavior is related to the locking of the spin and current direction providing evidence for helical spin structure of the topological surface states

Stability and Hydrocarbon/Fluorocarbon Sorption of a Metal-Organic Framework with Fluorinated Channels

The stabilities and hydrocarbon/fluorocarbon sorption properties of a zeolite-like metal-organic framework (MOF) Zn(hfipbb) with fluorinated channels has been studied. By the combination of thermogravimetric analysis (TGA) and powder X-ray diffraction (PXRD) measurements, we confirm that Zn(hfipbb) has exceptionally high hydrothermal and thermal stabilities. The adsorption behaviors of water and methanol by Zn(hfipbb) indicate that it is highly hydrophobic but with high adsorption of alcohols. Hexane and perfluorohexane adsorption measurements show that the fluorinated channels in Zn(hfipbb) have high affinity with hydrocarbon and fluorocarbon. The high fluorophilic nature of the channels and the high stability of the compound suggest its potential utility in practical separation applications

Using a Machine Learning Method to Predict the Penetration Depth of a Gravity Corer

The study of penetration depth of gravity piston samplers has an essential impact on sampling efficiency and instrument safety. This study focuses on predicting penetration depth based on the characteristic parameters of the sampled seafloor sediments and the sampler parameters. Although numerous studies of gravity corer penetration depth have been carried out, most have been based on the energy conservation equation, which considers a varying number of influencing factors. Furthermore, most research has focused on the same research idea of finding analytical solutions. The present study proposes a new approach to predicting gravity corer penetration depth based on a machine learning method that uses real sampling data from the sea and experimental data from a gravity sampling physical model for training and testing. Experimental results indicate that the machine learning model can accurately predict gravity corer penetration depth. Moreover, predictions were made for the same penetration conditions using the machine learning model and three other analytical solution models. Results show that the prediction accuracy of machine learning outperforms that of the analytical prediction model under various statistical rubrics. This study demonstrates the capacity of the proposed machine learning model and provides civil engineers with an effective tool to predict the penetration depth of gravity corers

Using a Machine Learning Method to Predict the Penetration Depth of a Gravity Corer

The study of penetration depth of gravity piston samplers has an essential impact on sampling efficiency and instrument safety. This study focuses on predicting penetration depth based on the characteristic parameters of the sampled seafloor sediments and the sampler parameters. Although numerous studies of gravity corer penetration depth have been carried out, most have been based on the energy conservation equation, which considers a varying number of influencing factors. Furthermore, most research has focused on the same research idea of finding analytical solutions. The present study proposes a new approach to predicting gravity corer penetration depth based on a machine learning method that uses real sampling data from the sea and experimental data from a gravity sampling physical model for training and testing. Experimental results indicate that the machine learning model can accurately predict gravity corer penetration depth. Moreover, predictions were made for the same penetration conditions using the machine learning model and three other analytical solution models. Results show that the prediction accuracy of machine learning outperforms that of the analytical prediction model under various statistical rubrics. This study demonstrates the capacity of the proposed machine learning model and provides civil engineers with an effective tool to predict the penetration depth of gravity corers