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With the advancement in living standards, there has been a significant surge in the quantity and diversity of household waste. To safeguard the environment and optimize resource utilization, there is an urgent demand for effective and cost-efficient intelligent waste classification methodologies. This study presents MRS-YOLO (Multi-Resolution Strategy-YOLO), a waste detection and classification model. The paper introduces the SlideLoss_IOU technique for detecting small objects, integrates RepViT of the Transformer mechanism, and devises a novel feature extraction strategy by amalgamating multi-dimensional and dynamic convolution mechanisms. These enhancements not only elevate the detection accuracy and speed but also bolster the robustness of the current YOLO model. Validation conducted on a dataset comprising 12,072 samples across 10 categories, including recyclable metal and paper, reveals a 3.6% enhancement in mAP50% accuracy compared to YOLOv8, coupled with a 15.09% reduction in volume. Furthermore, the model demonstrates improved accuracy in detecting small targets and exhibits comprehensive detection capabilities across diverse scenarios. For transparency and to facilitate further research, the source code and related datasets used in this study have been made publicly available at GitHub. [ABSTRACT FROM AUTHOR]

The spatter generated by the interaction between laser and powder during Powder Bed Fusion-Laser Melting (PBF-LM) can significantly affect the quality of printed parts. A high-speed camera is used to observe the dynamic process of spatter's behavior under different layer thickness and laser powers during the printing process, and to analyze the printed samples' surface roughness, microstructure, and mechanical properties. In terms of spatter image processing, employing an optical flow approach to track and quantify the number of spatters efficiently eliminates statistical redundancy and improves statistical correctness. It is found that under the same laser power, the number of spatters produced by the laser scan direction with the gas flow (LSD-W) is more than that by the laser scan direction against the gas flow (LSD-A), and the number of spatters produced increases with the increase of laser power. Analyzing the mechanical properties and surface roughness of the printed samples under different process parameters quantitatively reveals that differences in the spatter amount generated under different process parameters in the PBF-LM process is not the determining factor affecting the difference in tensile strength of printed parts. During LSD-W, the number of spatters generated at laser power of 170 W and layer thickness of 0.03 mm is 87, and the tensile strength of the printed sample is 618 MPa. During LSD-W, the number of spatters generated at laser power of 320 W and layer thickness of 0.05 mm is 211, and the tensile strength of the printed sample is 680 MPa. Instead, spatter generation has a more direct impact on the surface roughness of printed parts. The layer thickness is 0.03 mm, the laser power is 170 W, and (Ra = 2.372 μm) is the surface roughness of the sample. The layer thickness is 0.05 mm, the laser power is 320 W, and (Ra = 8.163 μm) is the surface roughness of the sample. [ABSTRACT FROM AUTHOR]

Submarine pipelines are widely adopted around the world for transporting oil and gas from offshore fields. They tend to be severely ruined by the extreme waves induced by the natural disaster, such as hurricanes and tsunamis. To maintain the safety and function integrity of the pipelines, porous media have been used to wrap them from the external loads by the submarine environment. The functions of the porous wrappers under the hydrodynamic impact remain to be uncovered before they are widely accepted by the industry. In this study, a numerical wave tank is established with the immersed boundary method as one of the computational fluid dynamics. The submarine pipelines and their porous wrappers are two-way-coupled in terms of displacement and pressure at their interfaces. The impact from the solitary waves, which approximately represent the extreme waves in the reality, on the pipelines with different configurations of the porous wrapper is investigated. The results present significant protective functions of the wrappers on the internal pipelines, transferring the impact forces from the pipelines to the wrappers. The protective effects tend to be enhanced by the porosity and thickness of the wrappers. The influence of the pipeline configurations and the marine environment are then analysed. As for the front pipeline, an increase in the gap leads to a slight increase in the horizontal forces on both the wrapper and the pipeline, but a significant increase in the vertical forces. As for the rear pipeline, because of the shield function of the front pipeline, the velocity within the gap space and the forces on the pipes are decreased with the decrease in the gap size. The complex flow fields around the pipelines with wrappers are also illuminated, implying that the protection function of the wrapper is enhanced by the wave height reduction. [ABSTRACT FROM AUTHOR]

Membrane electrode assemblies (MEAs) are critical components in influencing the electrochemical performance of high-temperature proton exchange membrane fuel cells (HT-PEMFCs). MEA manufacturing processes are mainly divided into the catalyst-coated membrane (CCM) and the catalyst-coated substrate (CCS) methods. For conventional HT-PEMFCs based on phosphoric acid-doped polybenzimidazole (PBI) membranes, the wetting surface and extreme swelling of the PA-doped PBI membranes make the CCM method difficult to apply to the fabrication of MEAs. In this study, by taking advantage of the dry surface and low swelling of a CsH5(PO4)2-doped PBI membrane, an MEA fabricated by the CCM method was compared with an MEA made by the CCS method. Under each temperature condition, the peak power density of the CCM-MEA was higher than that of the CCS-MEA. Furthermore, under humidified gas conditions, an enhancement in the peak power densities was observed for both MEAs, which was attributed to the increase in the conductivity of the electrolyte membrane. The CCM-MEA exhibited a peak power density of 647 mW cm−2 at 200 °C, which was ~16% higher than that of the CCS-MEA. Electrochemical impedance spectroscopy results showed that the CCM-MEA had lower ohmic resistance, which implied that it had better contact between the membrane and catalyst layer. [ABSTRACT FROM AUTHOR]

The evolution of a submarine landslide is a very complicated process due to slurry–water interactions. Most previous studies have focused on debris flows or turbidity currents independently. Little research has been conducted on the processes of transformation from debris flows into turbidity currents. Moreover, the underlying mechanical mechanisms of these transformation processes are not well understood. In this study, we aimed to better understand these mechanisms by simulating submarine landslide transportation processes using computational fluid dynamics. In the numerical models, the two-phase mixture module was adopted to mimic the interactions of the slurry with the ambient water, which we validated through a dam-break case. Here, the rheological behaviors of the slurries are described using the Herschel–Bulkley model. A formula for transformation rates is best fitted through a case series of debris flows. In particular, the activation stress is expressed by the dynamic pressure at the moment when the slurry starts to mobilize, which is fitted as a coefficient 6.55 × 10−5 times the shear strength. Then, two coefficients in the formula of the transformation rate are fitted as 1.61 and 0.26, respectively, based on the cases of debris flows, considering their different initial thicknesses, levels of slurry consistency and slope angles. Finally, in a real-scale debris flow case study, we demonstrate that the slurry is fully transformed before it is deposited. The expected outcome, the mechanical theory, the activation stress and the transformation rate would be applied to assess the influence area of the realistic turbidity currents and their harm to the subsea environment. [ABSTRACT FROM AUTHOR]

BiFeO3–BaTiO3 (BF–BT) ceramics exhibit great potential for diverse applications in high temperature piezoelectric transducers, temperature-stable dielectrics and pulsed-power capacitors. Further optimization of functional properties for different types of applications can be achieved by modification of processing parameters or chemical composition. In the present work, the influence of pentavalent niobium substitution for trivalent ferric ions on the structure, microstructure and dielectric properties of 0.7BF–0.3BT ceramics was investigated systematically. Doping with niobium led to incremental reductions in grain size (from 7.0 to 1.3 µm) and suppression of long-range ferroelectric ordering. It was found that core-shell type microstructural features became more prominent as the Nb concentration increased, which were correlated with the formation of distinct peaks in the dielectric permittivity–temperature relationship, at ~470 and 600 °C, which were attributed to the BT-rich shell and BF-rich core regions, respectively. Nb-doping of BF–BT ceramics yielded reduced electronic conductivity and dielectric loss, improved electrical breakdown strength and enhanced dielectric energy storage characteristics. These effects are attributed to the charge compensation of pentavalent Nb donor defects by bismuth vacancies, which suppresses the formation of oxygen vacancies and the associated electron hole conduction mechanism. The relatively high recoverable energy density (Wrec = 2.01 J cm−3) and energy storage efficiency (η = 68%) of the 0.7BiFeO3–0.3BaTiO3 binary system were achieved at 75 °C under an electric field of 15 kV mm−1. This material demonstrates the greatest potential for applications in energy storage capacitors and temperature-stable dielectrics. [ABSTRACT FROM AUTHOR]

Acoustic backscatter data are widely applied to study the distribution characteristics of seabed sediments. However, the ghosting and mosaic errors in backscatter images lead to interference information being introduced into the feature extraction process, which is conducted with a convolutional neural network or auto encoder. In addition, the performance of the existing classifiers is limited by such incorrect information, meaning it is difficult to achieve fine classification in survey areas. Therefore, we propose a sediment classification method based on the acoustic backscatter image by combining a stacked denoising auto encoder (SDAE) and a modified extreme learning machine (MELM). The SDAE is used to extract the deep-seated sediment features, so that the training network can automatically learn to remove the residual errors from the original image. The MELM model, which integrates weighted estimation, a Parzen window and particle swarm optimization, is applied to weaken the interference of mislabeled samples on the training network and to optimize the random expression of input layer parameters. The experimental results show that the SDAE-MELM method greatly reduces mutual interference between sediment types, while the sediment boundaries are clear and continuous. The reliability and robustness of the proposed method are better than with other approaches, as assessed by the overall classification effect and comprehensive indexes. [ABSTRACT FROM AUTHOR]