Complicated Architecture Research Articles

Development of Biomimetic Hepatic Lobule-Like Constructs on Silk-Collagen Composite Scaffolds for Liver Tissue Engineering.

Constructing an engineered hepatic lobule-mimetic model is challenging owing to complicated lobular architecture and crucial hepatic functionality. Our previous study has demonstrated the feasibility of using silk fibroin (SF) scaffolds as functional templates for engineering hepatic lobule-like constructs. But the unsatisfactory chemical and physical performances of the SF-only scaffold and the inherent defect in the functional activity of the carcinoma-derived seeding cells remain to be addressed to satisfy the downstream application demand. In this study, SF-collagen I (SFC) composite scaffolds with improved physical and chemical properties were fabricated, and their utilization for bioengineering a more hepatic lobule-like construct was explored using the immortalized human hepatocyte-derived liver progenitor-like cells (iHepLPCs) and endothelial cells incorporated in the dynamic culture system. The SFC scaffolds prepared through the directional lyophilization process showed radially aligned porous structures with increased swelling ratio and porosity, ameliorative mechanical stiffness that resembled the normal liver matrix more closely, and improved biocompatibility. The iHepLPCs displayed a hepatic plate-like distribution and differentiated into matured hepatocytes with improved hepatic function in vitro and in vivo. Moreover, hepatocyte–endothelial cell interphase arrangement was generated in the co-culture compartment with improved polarity, bile capillary formation, and enhanced liver functions compared with the monocultures. Thus, a more biomimetic hepatic lobule-like model was established and could provide a valuable and robust platform for various applications, including bioartificial liver and drug screening.

Forming‐Free, Self‐Compliance, Bipolar Multi‐Level Resistive Switching in WO3–x Based MIM Device

AbstractResistive switching (RS) in metal oxides, which offers self‐compliance and multiple resistance states without the requirement of any high voltage forming step, holds the potential of application in selector less high density resistive random access memory (RRAM) devices. Typically, operation of metal oxide‐based RS devices requires the integration of additional oxide layers or circuit elements to achieve current compliance and complicated device architecture for high‐density memory applications. In this study, a self‐compliance, and multi‐level RS is demonstrated that does not require high voltage forming in a single layer non‐stoichiometric WO3–x. This study suggests that high oxygen vacancy (VO) concentration in the pristine WO3–x layer leads to its forming‐free filamentary switching characteristics, whereas reversible formation and annihilation of an oxygen‐rich region in the filament at the WO3–x/Pt junction has been envisaged to be responsible for self‐compliance set and voltage controlled multiple reset resistance states. The results demonstrate non‐stoichiometric WO3–x with an active metal/oxide interface permeable to reversible oxygen migration can pave the way for producing high density, reliable RRAM devices.

Performance enhancement of multilayer MoS2 phototransistors via photoresist encapsulation

We propose the encapsulation of bottom-gate multilayer MoS2 phototransistors with an AZ®5214E photoresist as an effective device design to enhance the optoelectronic properties of the phototransistors. The photoresist-encapsulated MoS2 phototransistors, based on mechanically exfoliated MoS2 crystals, exhibited an improved device performance. After the photoresist encapsulation, the responsivity and detectivity of the device increased by seven-fold to 3.2 × 103 A W−1 and by five-fold to 2.3 × 1012 Jones, respectively, under a 650-nm laser with an incident power density of 2.1 mW cm−2. We attribute the observed enhancement in the phototransistor performance to the enhanced electrical properties owing to the n-type doping via photoresist encapsulation. These results demonstrate that MoS2 phototransistors can achieve high performance without complicated device architecture and process, and thus, photoresist encapsulation presents an effective method for developing high-performance two-dimensional optoelectronic devices.

Genomic insights into variation in thermotolerance between hybridizing swordtail fishes.

Understanding how organisms adapt to changing environments is a core focus of research in evolutionary biology. One common mechanism is adaptive introgression, which has received increasing attention as a potential route to rapid adaptation in populations struggling in the face of ecological change, particularly global climate change. However, hybridization can also result in deleterious genetic interactions that may limit the benefits of adaptive introgression. Here, we used a combination of genome-wide quantitative trait locus mapping and differential gene expression analyses between the swordtail fish species Xiphophorus malinche and X. birchmanni to study the consequences of hybridization on thermotolerance. While these two species are adapted to different thermal environments, we document a complicated architecture of thermotolerance in hybrids. We identify a region of the genome that contributes to reduced thermotolerance in individuals heterozygous for X. malinche and X. birchmanni ancestry, as well as widespread misexpression in hybrids of genes that respond to thermal stress in the parental species, particularly in the circadian clock pathway. We also show that a previously mapped hybrid incompatibility between X. malinche and X. birchmanni contributes to reduced thermotolerance in hybrids. Together, our results highlight the challenges of understanding the impact of hybridization on complex ecological traits and its potential impact on adaptive introgression.

90 nm CMOS Implementation of Multiplicative Inverse of the S-Box for AES Algorithm Using Six Transistor XOR Gate

The Substitution-BOX is the most difficult architecture and is at the heart of any Advanced Encryption Standard algorithm implementation. It is the most complicated non-linear architecture using multiplicative inverse. It consumes maximum amount of the energy and power budget of the algorithm. This paper introduces a full-custom CMOS implementation of Multiplicative Inverse module for Substitution/ Inverse Substitution transformation in composite field arithmetic using Galois field GF (28). The multiplicative inversion module utilizes large number of XOR gates in its implementation. This paper introduces implementation of a novel XOR gate using six transistors. Using this six transistor XOR gate, the multiplicative inverse module is implemented in 90 nm CMOS technology. Simulation of the proposed design is achieved using Tanner EDA v.16 software. The area of the multiplicative inverse circuit is 39.92 µm2 requiring 776 transistors. With 0.6 Volts supply voltage, the design shows a power dissipation of 2.386 µWatts making it ideal for applications such as smart cards and RFID tags.

Automated Registration of Multiangle SAR Images Using Artificial Intelligence

Traditionally, nonlinear data processing has been approached via the use of polynomial filters, which are straightforward expansions of many linear methods, or through the use of neural network techniques. In contrast to linear approaches, which often provide algorithms that are simple to apply, nonlinear learning machines such as neural networks demand more computing and are more likely to have nonlinear optimization difficulties, which are more difficult to solve. Kernel methods, a recently developed technology, are strong machine learning approaches that have a less complicated architecture and give a straightforward way to transforming nonlinear optimization issues into convex optimization problems. Typical analytical tasks in kernel-based learning include classification, regression, and clustering, all of which are compromised. For image processing applications, a semisupervised deep learning approach, which is driven by a little amount of labeled data and a large amount of unlabeled data, has shown excellent performance in recent years. For their part, today’s semisupervised learning methods operate on the assumption that both labeled and unlabeled information are distributed in a similar manner, and their performance is mostly impacted by the fact that the two data sets are in a similar state of distribution as well. When there is out-of-class data in unlabeled data, the system’s performance will be adversely affected. When used in real-world applications, the capacity to verify that unlabeled data does not include data that belongs to a different category is difficult to obtain, and this is especially true in the field of synthetic aperture radar image identification (SAR). Using threshold filtering, this work addresses the problem of unlabeled input, including out-of-class data, having a detrimental influence on the performance of the model when it is utilized to train the model in a semisupervised learning environment. When the model is being trained, unlabeled data that does not belong to a category is filtered out by the model using two different sets of data that the model selects in order to optimize its performance. A series of experiments was carried out on the MSTAR data set, and the superiority of our method was shown when it was compared against a large number of current semisupervised classification algorithms of the highest level of sophistication. This was especially true when the unlabeled data had a significant proportion of data that did not fall into any of the categories. The performance of each kernel function is tested independently using two metrics, namely, the false alarm (FA) and the target miss (TM), respectively. These factors are used to calculate the proportion of incorrect judgments made using the techniques.

Improvement of the medical fusion process of images by fuzzy entropy and transformation of the contourlet

<span>Many types of medical pictures have to be fused, as single-modality medical images can give limited information because of the imagery and the complicated architecture of the human organ. This study proposes to offer a platform on which to make clinical diagnoses and to increase the accuracy of the target identification and the quality of the fused pictures by combining the benefits of nonsubsampled contourlet transform (NSCT) and fuzzy entropy. A picture is first broken down into low frequency or high frequency subbands through NSCT. In line with the various features of the low and high frequency components the respective fusion rules must be implemented. It calculates the level of membership of low frequency coefficients. The fusion of coefficients is also calculated and then utilized to retain picture features. By increasing regional energy, high-frequency components are merged. Inverse transformation produces the final fused picture. Experimental results have shown that, based on subjective visual effect and objective assessment standards, the suggested technique produces a satisfactory fusion effect. This process may also achieve high average gradient, standard deviation (SD), and edge preservation and maintain the fused picture features well. Effective reference can be provided by the outcome of the suggested algorithm for patients' assessment.</span>

Deep Learning-Based Algal Detection Model Development Considering Field Application

Algal blooms have various effects on drinking water supply systems; thus, proper monitoring is essential. Traditional visual identification using a microscope is a time-consuming method and requires extensive labor. Recently, advanced machine learning algorithms have been increasingly applied for the development of object detection models. The You-Only-Look-Once (YOLO) model is a novel machine learning algorithm used for object detection; it has been continuously improved in newer versions, and a tiny version of each standard model presented. The tiny versions applied a less complicated architecture using a smaller number of convolutional layers to enable faster object detection than the standard version. This study compared the applicability of the YOLO models for algal image detection from a practical aspect in terms of classification accuracy and inference time. Therefore, automated algal cell detection models were developed using YOLO v3 and YOLO v4, in which a tiny version of each model was also applied. The cell images of 30 algal genera were used for training and testing the models. The model performances were compared using the mean average precision (mAP). The mAP values of the four models were 40.9, 88.8, 84.4, and 89.8 for YOLO v3, YOLO v3-tiny, YOLO v4, and YOLO v4-tiny, respectively, demonstrating that YOLO v4 is more precise than YOLO v3. The tiny version models presented noticeably higher model accuracy than the standard models, allowing up to ten times faster object detection time. These results demonstrate the practical advantage of tiny version models for the application of object detection with a limited number of object classes.

On the perception of leaf morphology and visible light by Tetranychus urticae Koch (Acariformes, Tetranychidae)

The two-spotted spider mite Tetranychus urticae is one of the most polyphagous and most damaging pests worldwide. The behaviour of the mite differs on its various host plants and this could influence crop defence strategies. We investigated the perception of leaf morphology by T. urticae in relation to the effects of visible light on the selection of sites on which females prefer to live and feed. Mite females chose the shaded surface when smooth and glabrous lemon leaves were offered as a surface for living and feeding, showing a photophobic behaviour. However, they clearly preferred the abaxial surface of bean leaves regardless of the presence or absence of visible light. As no differences were found regarding the food quality between the adaxial and abaxial leaf surfaces, mite behaviour seems to be related firstly to the perception of leaf morphology and secondly to visible light. As regards the first factor, T. urticae prefers the abaxial surface of leaves with a complicated architecture, i.e. veins in relief, on which they can easily build their dense web, and this factor has priority in comparison to the negative effect of the visible radiations. The second factor is important on smooth and glabrous leaves, these being unsuitable for building the complicated characteristic webs of T. urticae.

LAEDNet: A Lightweight Attention Encoder–Decoder Network for ultrasound medical image segmentation

Automatic ultrasound image segmentation plays an important role in early diagnosis of human diseases. This paper introduces a novel and efficient encoder–decoder network, called Lightweight Attention Encoder–Decoder Network (LAEDNet), for automatic ultrasound image segmentation. In contrast to previous encoder–decoder networks that involve complicated architecture with numerous parameters, our LAEDNet adopts lightweight version of EfficientNet as encoder. On the other hand, a Lightweight Residual Squeeze-and-Excitation (LRSE) block is employed in decoder. To achieve trade-off between segmentation accuracy and implementing efficiency, we also present a family of models, from light to heavy (denoted as LAEDNet-S, LAEDNet-M, and LAEDNet-L, respectively), with varying lightweight version of EfficientNet backbones. To evaluate LAEDNet, we have conducted extensive experiments on Brachial Plexus Dataset (BP), Breast Ultrasound Images Dataset (BUSI), and Head Circumference Ultrasound Images Dataset (HCUS), where ultrasound images are suffered from high noise, blurred borders and low contrast. The experiments show that, compared with U-Net and its variants, e.g., M-Net, U-Net++ and TransUNet, our LAEDNet achieves better results in terms of Dice Coefficient (DSC) and running speed. Particularly, LAEDNet-M only has 10.75M model parameters with 40.7 FPS, yet obtaining 73.0%, 73.8% and 91.3% DSC on BP, BUSI and HCUS datasets, respectively.

Aspergichromones A-E, Five Chromone Derivatives with Complicated Polycyclic Architecture from Aspergillus deflectus.

Five unprecedented chromone derivatives involving a 6/6/5/5/5/6 hexacyclic scaffold (1, 2), 6/6/5/6/6/6/6 heptacyclic scaffold (3), and 6/6/6/5/5/6 hexacyclic scaffold (4, 5) were obtained from the fungus Aspergillus deflectus NCC0415. Their structures were identified using comprehensive spectroscopic analysis, single-crystal X-ray diffraction, and electronic circular dichroism calculations. Except for 3, the other compounds, especially the 6/6/6/5/5/6 hexacyclic derivatives (4 and 5), exhibited potent inosine-5'-monophosphate dehydrogenase (IMPDH) inhibitory activities.

Distributed event-triggered average consensus control strategy with fractional-order local controllers for DC microgrids

Recently, direct current (DC) microgrids have become prominent due to their less complicated architecture and better integration of renewable energy sources (RES). Energy storage systems (ESSs) and RES, such as photovoltaic (PV) panels and fuel cells (FCs), are internally DC. Hence, it is easier for them to be integrated into DC microgrids compared to AC systems. However, DC microgrids have low inertia, and voltage stability is harder to achieve due to the inexistence of reactive power compensators. Therefore, this paper suggests a novel distributed control system for DC microgrids to achieve voltage stabilization and accurate energy balancing of ESSs based on event-triggered average consensus protocol and fractional-order proportional-integral (FOPI) local controllers. The fractional-order controller has many advantages over the integer-order classical controller, such as achieving better performance, flexibility, and a higher degree of freedom in the controller design. In this regard, the FOPI local controllers provide accurate load sharing for the droop mechanism. The distributed secondary layer controller compensates for the voltage offset, meanwhile balancing the energy level of ESSs. In the conventional distributed control strategies, the communication between the agents is periodically at discrete sampling time, resulting in network traffic increase and excessive energy consumption in the devices. Therefore, in this paper, the average consensus protocol is event-based, saving considerable network capacity for other microgrid communication needs. The effectiveness of the proposed control strategy is confirmed by the simulation of a 380V DC microgrid in MATLAB/Simulink that shows the achievement of the control objectives. Simulation results show the proper voltage stabilization and accurate energy balancing of ESSs in both islanded and grid-connected modes and a significant reduction in the transmitted information between agents.

Complexing the Pre-assembled Brush-like siRNA with Poly(β-amino ester) for Efficient Gene Silencing.

Small interfering RNA (siRNA) has been emerging as a highly selective and effective pharmaceutics for treating broad classes of diseases. However, the practical application of siRNA agent is often hampered by its poor crossing of the cellular membrane barrier and ineffective releasing from endosome to cytoplasm, leading to low gene silencing efficacy for clinical purposes. Thus far, cationic lipid and polymer-based vectors have been extensively explored for gene delivery. Yet condensing the rigid and highly negatively charged siRNA duplex to form a stable complex vehicle usually requires a large load of cationic carriers, prone to raising the toxicity issue for delivery. Herein, we develop a simple strategy that can efficiently condense the siRNAs into nanoparticle vehicles for target gene regulation. In this approach, we first employ a DNA-grafted polycaprolactone (DNA-g-PCL) brush as template to organize the small rigid siRNAs into a large brush-like structure (siRNA-brush) through nucleic acid hybridization. Then, the siRNA-brush assembly is condensed by an ionizable and biodegradable polymer (poly(β-amino ester), PBAE) under acidic buffer condition to form a stable nanoparticle for siRNA delivery. Compared to the free siRNAs with poor complexing capability with PBAE, the large brush-like siRNA assemblies with more complicated topological architecture significantly promotes their electrostatic interaction with PBAE, enabling the formation of complexed nanoparticles at low weight ratio of polymer to siRNA. Additionally, PBAE/siRNA-brush complexes exhibit good biocompatibility and stability under physiological condition, as well as enhanced cellular internalization. When equipped with functional siRNAs, the obtained delivery system demonstrates excellent downregulation of target genes both in vitro and in vivo, through which the progression of hypertrophic scars can be retarded with negligible adverse effects in an xenografted mouse model.

Combining MobileNetV1 and Depthwise Separable convolution bottleneck with Expansion for classifying the freshness of fish eyes

Image classification using Convolutional Neural Network (CNN) achieves optimal performance with a particular strategy. MobileNet reduces the parameter number for learning features by switching from the standard convolution paradigm to the depthwise separable convolution (DSC) paradigm. However, there are not enough features to learn for identifying the freshness of fish eyes. Furthermore, minor variances in features should not require complicated CNN architecture. In this paper, our first contribution proposed DSC Bottleneck with Expansion for learning features of the freshness of fish eyes with a Bottleneck Multiplier. The second contribution proposed Residual Transition to bridge current feature maps and skip connection feature maps to the next convolution block. The third contribution proposed MobileNetV1 Bottleneck with Expansion (MB-BE) for classifying the freshness of fish eyes. The result obtained from the Freshness of the Fish Eyes dataset shows that MB-BE outperformed other models such as original MobileNet, VGG16, Densenet, Nasnet Mobile with 63.21% accuracy.

Design and modeling of a single-balanced high-Tc superconducting Josephson-junction terahertz mixer

High-Tc superconducting (HTS) receivers are promising candidates for terahertz (THz) communication applications due to their superior sensitivity and the cooling attainability with a miniature cryogenic system. Nevertheless, currently reported HTS mixers are mostly single-junction devices, and a more complicated architecture like the balanced configuration has not been investigated. This paper presents the design of a 600-GHz single-balanced HTS mixer, where detailed electromagnetic simulations are carried out to optimize the coupling efficiency and port isolation, as well as desired amplitude and phase relationship. To predict the noise and conversion performance, we present an innovative multiport network interleaving-based modeling method that enables powerful simulation verifications for single-balanced HTS Josephson mixers. Simulation results show that the presented device exhibits very good HTS mixer performance at the 600-GHz band, which validates the effectiveness of the design and the potential of the mixer for THz communication applications.

High‐Precision Direction‐of‐Arrival Estimations Using Digital Programmable Metasurface

Commonly, phased array antennas are used to provide spatial resolution for direction of arrival (DOA) estimations. However, the phase‐array‐based DOA estimations usually require complicated system architecture and signal processing. Digital programmable metasurfaces (DPMs) can realize flexible spatial modulations of electromagnetic (EM) waves without using massive transceivers, and hence can be used for DOA estimations with much simplified architectures. Here, a mechanism of DPM‐based DOA estimations at Ka band is proposed. A reflective DPM is designed to produce a series of dual beams in random, so as to establish a sensing matrix and sample the incident wave. Orthogonal matched pursuit algorithm is used to estimate DOA from the sparsely sampled datasets. The performances of the DPM‐based DOA estimations are demonstrated by simulations and experiments for the cases of single source and double sources. This work promotes the development of DPMs and is expected to find important applications in radar and wireless communication systems, as well as the joint radar and communication systems.

DiverGAN: An Efficient and Effective Single-Stage Framework for Diverse Text-to-Image Generation

In this paper, we concentrate on the text-to-image synthesis task that aims at automatically producing perceptually realistic pictures from text descriptions. Recently, several single-stage methods have been proposed to deal with the problems of a more complicated multi-stage modular architecture. However, they often suffer from the lack-of-diversity issue, yielding similar outputs given a single textual sequence. To this end, we present an efficient and effective single-stage framework (DiverGAN) to generate diverse, plausible and semantically consistent images according to a natural-language description. DiverGAN adopts two novel word-level attention modules, i.e., a channel-attention module (CAM) and a pixel-attention module (PAM), which model the importance of each word in the given sentence while allowing the network to assign larger weights to the significant channels and pixels semantically aligning with the salient words. After that, Conditional Adaptive Instance-Layer Normalization (CAdaILN) is introduced to enable the linguistic cues from the sentence embedding to flexibly manipulate the amount of change in shape and texture, further improving visual-semantic representation and helping stabilize the training. Also, a dual-residual structure is developed to preserve more original visual features while allowing for deeper networks, resulting in faster convergence speed and more vivid details. Furthermore, we propose to plug a fully-connected layer into the pipeline to address the lack-of-diversity problem, since we observe that a dense layer will remarkably enhance the generative capability of the network, balancing the trade-off between a low-dimensional random latent code contributing to variants and modulation modules that use high-dimensional and textual contexts to strength feature maps. Inserting a linear layer after the second residual block achieves the best variety and quality. Both qualitative and quantitative results on benchmark data sets demonstrate the superiority of our DiverGAN for realizing diversity, without harming quality and semantic consistency.

Diagnosis from CT scan images in complex biological media using deep learning and wave application: a Hunger Games search-based approach

Deep Convolutional Neural Networks (DCNNs) are robust models in image processing, real-time processing has become challenging due to their complicated architecture. In order to address these shortcomings, this paper proposes the use of the Extreme Learning Machine (ELM) as a substitute for the fully connected layer. this replacement dramatically increases processing speed, the random adjustment of weights and biases of the ELM input layer reduces the reliability of this model; thereby, in the next phase, the novel Hunger Games Search is exploited to tune the aforementioned parameters to increase the model’s reliability and stability. to evaluate the performance of the proposed compound model, it is tested on the SARS-CoV-2 Computed Tomography (CT) scan images and COVID-CT dataset. Afterward, the performance of the designed model is compared with standard DCNN, DCNNELM, and DCNNELMs, which are optimized by Cuckoo Search (CS), Genetic Algorithm (GA), Chimp Optimization Algorithm (ChOA), Whale Optimization Algorithm (WOA), and Sine-Cosine Algorithm (SCA). The results confirm that the proposed model outperforms other comparison models with a final accuracy of 98.56% on the SARS-CoV-2 CT-scan dataset and 98.45 on the COVID-CT dataset. This improvement in accuracy only increases the training and testing time by 0.0748 and 0.1184 milliseconds, respectively.

Modeling and analysis of a high-Tc superconducting polarization-manipulating single-sideband mixer for terahertz communications

This paper presents systematic modeling and performance analyses of a 340-GHz dual-polarized antenna-coupled single-sideband (SSB) high-Tc superconducting (HTS) mixer for terahertz (THz) communication systems. The mixer features a fully monolithic-integrated circuit layout, which enables both sensitive heterodyne detection and high image rejection by appropriate polarization-manipulating operation. To predict the comprehensive performance of such a HTS mixer with relatively complicated network architecture and polarization relationships, an innovative modified three-port modeling approach is presented and developed by theoretical derivations. Using the measured Josephson junction parameters of our previous experimentally demonstrated HTS mixer module at the same band, the best conversion gain and SSB noise temperature of the presented mixer are predicted to be around −5 dB and 1200 K at the operating temperature of 40 K, respectively. In addition, the mixer exhibits a flat frequency response over a wide intermediate-frequency range from 15 to 25 GHz and high image rejection of more than 17 dB over the radio frequency operation range. The SSB HTS Josephson mixer will be useful for THz wireless communication applications.

Morphology, internal architectures, and formation mechanisms of mega-pockmarks on the northwestern South China Sea margin

Pockmarks, as depression morphology related to fluid escape on the seafloor, are revealed by 3D seismic data on the northwestern South China Sea (SCS) margin. The pockmarks can be classified into two groups based on their various shapes in plan view: the circular group and the elongating group. These pockmarks in the study area could be defined as mega-pockmarks because their maximum diameters can reach to 7.5 km. They commonly develop more than one crater, which are called the central crater and the secondary crater. The seismic data illuminated their complicated internal architectures in the subsurface, as well as their evolution periods, such as the initiation stage, mature stage, and abandonment stage. According to the buried structures and their genesis mechanism, mega-pockmarks could be classified into linear faults-associated pockmarks and volcano-associated pockmarks. The linear fault-associated pockmarks root on the top Middle Miocene, where the linear faults are distributed. The linear faults on the top of fluid reservoir in Middle Miocene act as conduits for fluid seepage. The fluid seepage is driven by the break of balance between the hydrostatic and pore pressure. When the fluid seepage initiates, they will migrate along the linear faults, making the linear feature of pockmarks on the seafloor. Thermogenic gas from deep intervals and biogenic gas from shallow intervals may be fluid sources for the genesis of pockmarks. However, the volcanic activities control the genesis and evolution of volcano-associated pockmarks. Volcano-associated pockmarks root on the craters of volcanoes. The volcanoes underneath the pockmarks provide volcanic hydrothermal solutions, such as phreatomagmatic eruptions through the volcanic craters. The confined fluid seepages make the pockmarks exhibit a more circular shape on the seafloor. Long-term, multiepisode fluid expulsions generate the complicated internal architecture that leads to multicratered mega-pockmarks on the northwestern margin of SCS.