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In the study of sedimentary acoustics, measuring sound speed in the first layer sediment is a key issue. To solve this problem, we proposed a method for measuring the sound speed in the deep-sea first-layer sediment using a high-frequency submersible sub-bottom profiler. In this method, we obtained the reflection data of deep-sea sediments near the bottom using a wide-band narrow-beam single transducer and obtained the profile of deep-sea sediments using the reflection compression method. The first sediment layer was extracted according to the layer structure information, and the sound speed of the first sediment layer was directly measured. The transducer feasibility of the high-frequency submersible sub-bottom profiler operating at high pressure in deep sea was verified through high hydrostatic pressure tests in the laboratory. We designed a laboratory reflection compression measurement device to simulate the first layer of deep-sea sediments and verified the effectiveness of the measurement method. This method also was used to process deep-sea sediment data acquired by the Deep-Sea Warrior manned submersible. [ABSTRACT FROM AUTHOR]

Segmented proton exchange membrane (PEM) fuel cells are widely implemented in measuring their internal spatially distributed variables (e.g. current density and temperature). However, the difference between the segmented and ordinary fuel cells in spatial distribution characteristics has not been fully understood. In this work, we studied the spatial distribution characteristics in segmented fuel cells (cell area: 366.6 cm2) utilizing a three-dimensional (3D) large-scale fuel cell model that considers the full morphologies of metallic bipolar plates (BPs) and dot-matrix hydrogen, air and coolant flow distribution zones, including the current density, temperature, water content, etc., and compared with those in ordinary fuel cells. It is found that placing a segmented print circuit board (PCB) next to the bipolar plate (BP) component has little influence on fuel cell performance and internal distribution characteristics, whereas the measured current density distribution in the PCB still has inconsistency with that in the ordinary fuel cell, indicating that the influence of segmentation on electron conduction cannot be neglected. In the fuel cell with segmented BP and PCB, the fuel cell performance decreases significantly mainly due to increased electrical and ionic ohmic loss. In addition, the BP's segmentation significantly influences on the water and thermal state inside the whole fuel cell, leading to inconsistency in the measured temperature, relative humidity (RH) and current density distributions with ordinary fuel cells. [ABSTRACT FROM AUTHOR]

Fake news is frequently disseminated through social media, which significantly impacts public perception and individual decision-making. Accurate identification of fake news on social media is usually time-consuming, laborious, and difficult. Although the leveraging of machine learning technologies can facilitate automated authenticity checks, the time-sensitive and voluminous nature of the data brings considerable challenge for fake news detection. To address this issue, this paper proposes a quantum multimodal fusion-based model for fake news detection (QMFND). QMFND integrates the extracted images and textual features, and passes them through a proposed quantum convolutional neural network (QCNN) to obtain discriminative results. By testing QMFND on two social media datasets, Gossip and Politifact, it is proved that its detection performance is equal to or even surpasses that of classical models. The effects of various parameters are further investigated. The QCNN not only has good expressibility and entangling capability but also has good robustness against quantum noise. The code is available at • Quantum multimodal fusion-based fake news detection model is proposed for social media. • Amplitude encoding is adopted to encode multimodal features into a VQC. • QCNN is proposed for efficient training of encoded multimodal data. [ABSTRACT FROM AUTHOR]

Self-humidification is becoming a popular strategy for water management in proton exchange membrane fuel cells (PEMFCs), owing to the advantages of decreased volume, cost, and parasitic power of the hydrogen fuel cell engine. Membrane dehydration is one of the key issues that seriously affect the performance and lifespan of PEMFCs under low humidity conditions. In this work, an electrospinning approach was employed to fabricate gas diffusion layers (e-GDLs) composed of nanosized carbon fibers with a nanoscale pore structure. Further, a vapor deposition of Dow Corning Sylgard 184 was utilized to apply a hydrophobic coating to enhance the hydrophobicity of the e-GDL. The fabricated e-GDL can help alleviate the dehydration of the catalyst-coated membrane by increasing the breakthrough pressure. The breakthrough pressure of the e-GDL is five folds higher than that of the current commercial GDL, owing to the combined effects of the nanostructure and enhanced hydrophobicity. This superior characteristic is expected to effectively alleviate membrane dehydration under low humidity conditions. In addition, the e-GDL has excellent elastic deformability, which can effectively alleviate the irreversible damage caused by the pre-tightening force in the stack assembly process, thus enhancing the durability and lifetime of PEMFCs. [ABSTRACT FROM AUTHOR]