Distinct Architectures Research Articles

Distinct and shared genetic architectures of gestational diabetes mellitus and type 2 diabetes

Gestational diabetes mellitus (GDM) is a common metabolic disorder affecting more than 16 million pregnancies annually worldwide1,2. GDM is related to an increased lifetime risk of type 2 diabetes (T2D)1–3, with over a third of women developing T2D within 15 years of their GDM diagnosis. The diseases are hypothesized to share a genetic predisposition1–7, but few studies have sought to uncover the genetic underpinnings of GDM. Most studies have evaluated the impact of T2D loci only8–10, and the three prior genome-wide association studies of GDM11–13 have identified only five loci, limiting the power to assess to what extent variants or biological pathways are specific to GDM. We conducted the largest genome-wide association study of GDM to date in 12,332 cases and 131,109 parous female controls in the FinnGen study and identified 13 GDM-associated loci, including nine new loci. Genetic features distinct from T2D were identified both at the locus and genomic scale. Our results suggest that the genetics of GDM risk falls into the following two distinct categories: one part conventional T2D polygenic risk and one part predominantly influencing mechanisms disrupted in pregnancy. Loci with GDM-predominant effects map to genes related to islet cells, central glucose homeostasis, steroidogenesis and placental expression.

Surface Potential Analysis of Dual Material Gate Silicon-Based Ferroelectric TFET for Biosensing Application

By means of a dielectric modulation method, this research offers the first ever 2D analytical model for the surface potential of a dual material gate Ferroelectric-TFET (DMG-Fe-TFET) device used in an enzyme-free biosensor. Compared to a device with a single material gate, the sensitivity of a device with a gate made of two distinct metals (M1-M2) is improved by an increase in tunnelling width at the secondary tunnelling junction. This model accounts for the change in surface potential caused by varying the value, position, and fill factor of the target biomolecules. Several distinct device architectures are used to enhance the efficiency of the envisaged Fe-TFET in the nanoscale range. We also investigated how the dimensions of the Nano cavity and other parts of the device affect its sensitivity. In addition, simulation findings using the SILVACO Atlas platform are used to confirm the results obtained when testing the DMG Fe-TFET’s sensitivity in comparison to other recently manufactured FETs.

Deep Neural Network Discipline and Consequence to be Achieved through Internet Implementation Dropdown

The difficulty of automatically modifying and updating operations within Deep Learning (DL) frameworks can slow down the performance of Deep Neural Network processing (DNNs). This research presents a novel approach to software optimization by leveraging dynamically collected profile data. A unique online auto-tuning system for DNNs was developed to enhance both the training and inference phases. Python Distributed Training of Neural Networks (PyDTNN) is a lightweight toolkit designed for distributed DNN training and estimation. It is utilized to evaluate the VGG19 model on two distinct multi-core architecture options. In testing, our auto-tuning system performs comparably, if not better, than a static selection strategy. The performance of each variation of PyDTNN that employs static selection remains consistently high throughout execution. Conversely, the auto-tuned version initially performs at a set level and progressively improves as more feasible choices become available. While both variations yield similar results in training, the selection strategy outperforms all other inference options by autonomously determining the best strategy for each layer in VGG19. The new online implementation selection tool assists in choosing the best performance option from numerous alternatives while the program is running. Its key features include constructing layered judgments and thoroughly examining 35 possibilities. Our advanced systems represent the optimal choice for monitoring sustainable environmental systems with maximum effectiveness, efficiency, and timeliness.

Redefining Cataract Diagnosis: Leveraging MobileNetV2-Based CNN for Automated Detection from Fundus Images

Cataracts remain a prevalent cause of global blindness, emphasizing the urgent need for timely and accurate diagnostic solutions. This study introduces an innovative automated cataract detection method using fundus images, employing three distinct convolutional neural network architectures: MobileNetV2, EfficientNetB0, and ResNet50. Through transfer learning, these types of models were developed using a dataset containing both cataract and non-cataract fundus images. The outcomes show how successful the suggested models were in precisely classifying fundus images, with notable performance indicators including F1-score, recall, accuracy, and precision. The MobileNetV2-based model achieved 98% accuracy, EfficientNetB0 attained 68% accuracy, while the ResNet50 model, enhanced with additional dense layers, attained 69% accuracy. Furthermore, comprehensive evaluation including classification reports and confusion matrices validates the robustness and generalization capabilities of the models, confirmed through cross-validation on independent test sets. This convolutional neural network-based approach holds promise for scalable, cost-effective automated cataract detection in clinical settings, with the potential for further advancements in model interpretability and dataset diversification to enhance its applicability in diverse populations.

Deep learning-based Alzheimer's disease detection: reproducibility and the effect of modeling choices.

Machine Learning (ML) has emerged as a promising approach in healthcare, outperforming traditional statistical techniques. However, to establish ML as a reliable tool in clinical practice, adherence to best practices in data handling, and modeling design and assessment is crucial. In this work, we summarize and strictly adhere to such practices to ensure reproducible and reliable ML. Specifically, we focus on Alzheimer's Disease (AD) detection, a challenging problem in healthcare. Additionally, we investigate the impact of modeling choices, including different data augmentation techniques and model complexity, on overall performance. We utilize Magnetic Resonance Imaging (MRI) data from the ADNI corpus to address a binary classification problem using 3D Convolutional Neural Networks (CNNs). Data processing and modeling are specifically tailored to address data scarcity and minimize computational overhead. Within this framework, we train 15 predictive models, considering three different data augmentation strategies and five distinct 3D CNN architectures with varying convolutional layers counts. The augmentation strategies involve affine transformations, such as zoom, shift, and rotation, applied either concurrently or separately. The combined effect of data augmentation and model complexity results in up to 10% variation in prediction accuracy. Notably, when affine transformation are applied separately, the model achieves higher accuracy, regardless the chosen architecture. Across all strategies, the model accuracy exhibits a concave behavior as the number of convolutional layers increases, peaking at an intermediate value. The best model reaches excellent performance both on the internal and additional external testing set. Our work underscores the critical importance of adhering to rigorous experimental practices in the field of ML applied to healthcare. The results clearly demonstrate how data augmentation and model depth-often overlooked factors- can dramatically impact final performance if not thoroughly investigated. This highlights both the necessity of exploring neglected modeling aspects and the need to comprehensively report all modeling choices to ensure reproducibility and facilitate meaningful comparisons across studies.

Betz cells of the primary motor cortex.

Betz cells, named in honor of Volodymyr Betz (1834-1894), who described them as "giant pyramids" in the primary motor cortex of primates and other mammalian species, are layer V extratelencephalic projection (ETP) neurons that directly innervate α-motoneurons of the brainstem and spinal cord. Despite their large volume and circumferential dendritic architecture, to date, no single molecular criterion has been established that unequivocally distinguishes adult Betz cells from other layer V ETP neurons. In primates, transcriptional signatures suggest the presence of at least two ETP neuron clusters that contain mature Betz cells; these are characterized by an abundance of axon guidance and oxidative phosphorylation transcripts. How neurodevelopmental programs drive the distinct positional and morphological features of Betz cells in humans remains unknown. Betz cells display a distinct biphasic firing pattern involving early cessation of firing followed by delayed sustained acceleration in spike frequency and magnitude. Few cell type-specific transcripts and electrophysiological characteristics are conserved between rodent layer V ETP neurons of the motor cortex and primate Betz cells. This has implications for the modeling of disorders that affect the motor cortex in humans, such as amyotrophic lateral sclerosis (ALS). Perhaps vulnerability to ALS is linked to the evolution of neural networks for fine motor control reflected in the distinct morphomolecular architecture of the human motor cortex, including Betz cells. Here, we discuss histological, molecular, and functional data concerning the position of Betz cells in the emerging taxonomy of neurons across diverse species and their role in neurological disorders.

Review of MOFs and their applications in the preparation of loose nanofiltration membranes for dye and salt fractionation

The improper disposal of unused dyes by various industries is a significant contributor to water pollution. Thus, it is very important to remove the dyes from water. For the treatment of dyes, a number of technologies have been developed, including coagulation/flocculation, adsorption, and chemical oxidation. The main drawbacks of such techniques are their ineffective secondary pollution control and low treatment efficiency. Nanofiltration technology, in comparison, has a high removal rate and is simple to use. Due to its superior selective separation performance, loose nanofiltration (NF) is a potential technique for the fractionation of dyes and salts. Thus, the loose nanofiltration membranes need to be researched and developed more. However, the MOFs are often used in the preparation of loose nanofiltration membranes till now. So, it is necessary to get to know the chemical structure, prospect and challenges in the applications of loose nanofiltration membranes. Metal-organic frameworks, often known as MOFs, are completely novel family of hybrid materials used as adsorbents that are comprised of organic linkers and metal ions or clusters. MOFs and their derivatives have shown unprecedented potential in membrane applications due to their distinct porosity architectures and unique characteristics. They have the benefits of Highly porous, a huge area, flexible pore size, and strong stability due to their unique structural composition. This review provides an overview of MOF membrane fabrication techniques and kinds of MOFs like; UiO-66, MILs, HKUST-1, ZIFs. The review discusses MOF-based membranes for dye/saltwater separation, highlighting challenges and applications. It also discusses modified MOF membranes and their prospects for implementation. While challenges remain, ongoing research and advancements in membrane fabrication techniques hold the key to unlocking the promise of membranes based on MOF for various environmental and industrial applications.

In situ energy absorption and negative Poisson’s ratio behaviors of 3D weft knitted auxetic fabrics under variant measurements

Speculative negative Poisson’s ratio (NPR) materials exhibit an auxetic response to strain in a predetermined direction due to their unusual morphologies, making them highly suitable for various specialized applications. For the rationale, these materials have immense potential in various specialized contexts. In weft-knitted auxetic fabrics, uneven tension in the purl loops leads to the distinctive curling architecture. Due to their inherent curly structure and adaptability, weft-knitted fabrics are an excellent choice for emulating NPR materials. In this study, 3D weft-knitted auxetic fabrics were created using flatbed purl knitting technology. Two distinctive patterns, comprising knit and tuck stitches, were employed. The number of stitches per repeat (SPR) was varied to assess the impact of grid size on NPR behavior. Additionally, the variation in measurement points within standard-sized characterization specimens was analyzed, revealing slight differences in auxeticity. Increasing the SPR size initially enhanced NPR behavior, up to a certain limit, beyond which the trend declined. A significant shift in NPR was observed due to material variation and inter-yarn friction, with a 27% and 28% shift of Poisson’s ratio was experienced above and below the mean, respectively. Poisson’s ratio was also positively correlated with energy absorption, suggesting that the designed materials could be effective for protective and energy-absorbing applications.

Scion–Rootstock Combination Determines Pruning Responses in Young Almond Trees

Almond growers are seeking ways to reduce costs but maintain yield. Intensive planting systems with greater planting densities using trees on growth-controlling rootstocks, combined with mechanical pruning and shake-and-catch harvesting are becoming popular. In this study we examined the responses of six almond cultivars with distinctive architecture grafted onto five rootstocks with varying degrees of vigor control. Trees were planted in 2018 in a nursery row and left to grow without pruning until Winter 2021. Pruning involved a rudimentary hedging treatment akin to mechanical pruning. Branching and tree structure were recorded in 2020, before pruning, and again at the end of 2021, after one season’s growth following pruning. A rating system was developed to record qualitative data on central leader dominance and the number, length, basal diameter, and, in some cases, branching angle of axillary shoots and including scaffold branches. Relatively few changes were recorded in the basic growth habit of these trees in response to pruning. Before pruning, the most common rootstock effect was on axillary shoot production. After pruning, the most common rootstock effects were on scaffold branching and the length of subterminal axillary shoots. Further studies are required to determine how these differences produced by the interaction between pruning and rootstock may affect the productivity of fruit-bearing trees. Although in this study with young trees we were not able to record crop yield, the results highlight that it is mainly the scion–rootstock combination, with or without pruning, that determines the potential productivity of fruiting canopies. Scion–rootstock combinations that produce narrow upright canopies naturally with strong central leader dominance and highly branched canopies are preferred for superintensive growing systems with or without use of mechanical hedging.

Availability and Reliability Modeling of Mobile Cloud Architectures

In recent years, mobile cloud computing has attracted researchers and industries' interest since it maximizes gains with mobile computing in different areas, such as healthcare, sports, and business. However, the inherent challenges associated with mobile computing (e.g., battery provisioning and wireless network instability) may limit mobile cloud achievements. This paper provides an availability and reliability study of mobile clouds and evaluates distinct architectures and strategies. Our evaluation is based on hierarchical heterogeneous models and focuses on steady-state availability, reliability, annual downtime, and costs of implementation and provisioning of the cloud infrastructure. The results point out that service downtime may be significantly decreased by adopting actions such as spare batteries for mobile devices and the adoption of distinct redundancy strategies. We also propose a way to support decision-making considering private and public clouds by comparing some architectural choices while considering factors such as budget expenses for hardware purchases and power consumption. After 36 months, the private cloud has a cumulative cost that is smaller than the cost of a public cloud service.

NeuroWave-Net: Enhancing epileptic seizure detection from EEG brain signals via advanced convolutional and long short-term memory networks

<abstract> <p>This study presented a new approach to seizure classification utilizing electroencephalogram (EEG) data. We introduced the NeuroWave-Net, an innovative hybrid model that seamlessly integrates convolutional neural networks (CNN) and long short-term memory (LSTM) architectures. Unlike conventional methods, our model capitalized on CNN's proficiency in feature extraction and LSTM's prowess in classifying seizure. The key strength of the NeuroWave-Net lies in its ability to combine these distinct architectures, synergizing their capabilities for enhanced accuracy in identifying seizure conditions within EEG data. Our proposed model exhibited outstanding performance, achieving a classification accuracy of 99.48%. This study contributed to the advancement of seizure classification models, providing a robust and streamlined approach for accurate categorization within EEG datasets. NeuroWave-Net stands as a testament to the potential of hybrid neural network architectures in neurological diagnostics.</p> </abstract>

Electron Delocalization and Transfer Across Polyoxometalates-Based Subnanomaterials in Catalytic Reactions.

Due to their identical building blocks and high surface-to-volume ratio, subnanomaterials exhibit significant properties compared to their bulk nanomaterial counterparts. The interactions between these building blocks can result in either equal or unequal sharing of electrons, leading to electron transfer in heterojunctions or electron delocalization within symmetric structures. Clusters, possessing electronic properties akin to atoms, can serve as reservoirs of electrons to stabilize crucial intermediates in catalytic reactions. This perspective provides a novel understanding of well-defined subnanomaterials with distinct architectures, such as cluster-based constructions and co-assembled heterojunctions, emphasizing the relationship between electronic structures and catalytic properties. The objective is to provide novel perspectives on the realm of subnanomaterials and cluster-based architectures.

Leveraging Deep Neural Networks for Estimating Vickers Hardness from Nanoindentation Hardness.

This research presents a comprehensive analysis of deep neural network models (DNNs) for the precise prediction of Vickers hardness (HV) in nitrided and carburized M50NiL steel samples, with hardness values spanning from 400 to 1000 HV. By conducting rigorous experimentation and obtaining corresponding nanoindentation data, we evaluated the performance of four distinct neural network architectures: Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short-Term Memory network (LSTM), and Transformer. Our findings reveal that MLP and LSTM models excel in predictive accuracy and efficiency, with MLP showing exceptional iteration efficiency and predictive precision. The study validates models for broad application in various steel types and confirms nanoindentation as an effective direct measure for HV hardness in thin films and gradient-variable regions. This work contributes a validated and versatile approach to the hardness assessment of thin-film materials and those with intricate microstructures, enhancing material characterization and potential application in advanced material engineering.

Coaxial core-sheath shaped supercapacitor based on polypyrrole functionalized graphene/carbon nanotubes hollow fibers with ultrahigh length specific capacitance and energy density for wearable electronics

Coaxial fiber-shaped supercapacitors (FSCs) have received considerable attention due to the huge application potential in smart electronics, but their fabrications still remain challenging. Herein, using two polypyrrole functionalized graphene/carbon nanotubes hollow fibers (PPy/GCHF) with different diameters as electrodes, a coaxial FSC with a distinct core-sheath architecture is prepared. The outer scabbard electrode, PPy-750/GCHF1.0–8, in three-electrode, depicts a great linear capacitance of 791.9 mF cm−1 at 1 mA cm−1 with a capacity retention up to 93.2 % after 5000 charge–discharge cycles, which obviously exceeds most of the constructed fiber-shaped counterparts. Thanks to this skillful design, the PPy-750/GCHF0.3–8//PPy-750/GCHF1.0–8 FSC with efficiently shortened ion migration pathway and maximized contact area between gel electrolyte and electrodes shows an excellent linear capacitance of 561.9 mF cm−1 at 0.5 mA cm−1 with a great energy density of 49.9 μWh cm−1, standing for one of the highest levels in FSCs. In addition, the core-sheath shaped device also affords superior mechanical flexibility and capacitive stability with capacitance retentions of 100–88.7 % from 0 to 120° bending and 84.2 % after 5000 charge–discharge cycles. The good energy storage behaviors and ingenious structure endow PPy-750/GCHF0.3–8//PPy-750/GCHF1.0–8 to have promising application prospects in wearable electronics.

Blend of Deep Features and Binary Tree Growth Algorithm for Skin Lesion Classification

One of the most frequently identified cancers globally is skin cancer (SC). The computeraided categorization of numerous skin lesions via dermoscopic images is still a complicated problem. Early recognition is crucial since it considerably increases the survival chances. In this study, we introduce an approach for skin lesion categorization where, at first, a powerful hybrid deep-feature set is constructed, and then a binary tree growth (BTG)-based optimization procedure is implemented using a support vector machine (SVM) classifier with an intention to compute the categorizing error and build symmetry between categories, for selecting the most significant features which are finally fed to a multi-class SVM for classification. The hybrid deep-feature set is constructed by utilizing two pre-trained models, i.e., Densenet-201, and Inception-v3, that are fine-tuned on skin lesion data. These two deep-feature models have distinct architectures that characterize dissimilar feature abstraction strengths. This effective deep feature framework has been tested on two publicly available challenging datasets, i.e., ISIC2018 and ISIC2019. The proposed framework outperforms many existing approaches and achieves notable {accuracy, sensitivity, precision, specificity} values of {98.50%, 96.60%, 97.84%, 99.59%} and {96.60%, 94.21%, 96.38%, 99.39%} for the ISIC2018 and ISIC2019 datasets, respectively. The proposed implementation of the BTG-based optimization algorithm performs significantly better on the proposed feature blend for skin lesion classification.

The Architectural Legacy of Lithuanians in the United States during the Post-World War II Era: A Monument to the Cold War

The architectural heritage directly related to the refugees from Europe who came to the USA as a result of World War II is still an under-researched topic. New post-war arrivals from the displaced persons camps resulted in a sizeable growth of the already well-established Lithuanian community, infusing it with highly educated professionals. This also included many architects who needed to adapt and continue their practice in a different environment while also finding a way to be useful for the objectives of their national group. The aim of this paper is to examine the architectural legacy of the Lithuanian community in the post-war decades in the USA, emphasizing buildings that were designed with a specific aspiration to implement national character. Research finds that buildings built for the Lithuanian community carried a strong symbolical language that was a peculiar, yet enriching case, in regard to the then-dominant mid-century modernist trends. These structures show the determination of the national group to use their built environment as a medium to reinforce their identity and use architecture as a political statement. The paper proposes to interpret this politically motivated and stylistically distinctive architecture as monuments testifying to the political atmosphere of the Cold War. In this way, the heritage value of these buildings is linked not to avant-garde architectural styles, but to the political needs of a specific community in exile.

Classifying stages in the gonotrophic cycle of mosquitoes from images using computer vision techniques

The ability to distinguish between the abdominal conditions of adult female mosquitoes has important utility for the surveillance and control of mosquito-borne diseases. However, doing so requires entomological training and time-consuming manual effort. Here, we design computer vision techniques to determine stages in the gonotrophic cycle of female mosquitoes from images. Our dataset was collected from 139 adult female mosquitoes across three medically important species—Aedes aegypti, Anopheles stephensi, and Culex quinquefasciatus—and all four gonotrophic stages of the cycle (unfed, fully fed, semi-gravid, and gravid). From these mosquitoes and stages, a total of 1959 images were captured on a plain background via multiple smartphones. Subsequently, we trained four distinct AI model architectures (ResNet50, MobileNetV2, EfficientNet-B0, and ConvNeXtTiny), validated them using unseen data, and compared their overall classification accuracies. Additionally, we analyzed t-SNE plots to visualize the formation of decision boundaries in a lower-dimensional space. Notably, ResNet50 and EfficientNet-B0 demonstrated outstanding performance with an overall accuracy of 97.44% and 93.59%, respectively. EfficientNet-B0 demonstrated the best overall performance considering computational efficiency, model size, training speed, and t-SNE decision boundaries. We also assessed the explainability of this EfficientNet-B0 model, by implementing Grad-CAMs—a technique that highlights pixels in an image that were prioritized for classification. We observed that the highest weight was for those pixels representing the mosquito abdomen, demonstrating that our AI model has indeed learned correctly. Our work has significant practical impact. First, image datasets for gonotrophic stages of mosquitoes are not yet available. Second, our algorithms can be integrated with existing citizen science platforms that enable the public to record and upload biological observations. With such integration, our algorithms will enable the public to contribute to mosquito surveillance and gonotrophic stage identification. Finally, we are aware of work today that uses computer vision techniques for automated mosquito species identification, and our algorithms in this paper can augment these efforts by enabling the automated detection of gonotrophic stages of mosquitoes as well.

Feature Reduction Networks: A Convolution Neural Network-Based Approach to Enhance Image Dehazing

Image dehazing represents a dynamic area of research in computer vision. With the exponential development of deep learning, particularly convolutional neural networks (CNNs), innovative and effective image dehazing techniques have surfaced. However, in stark contrast to the majority of computer vision tasks employing CNNs, the output from a dehazing model is often treated as uninformative noise, even though the model’s filters are engineered to extract pertinent features from the images. The standard approach of end-to-end models for dehazing involves noise removal from the hazy image to obtain a clear one. Consequently, the model’s dehazing capacity diminishes, as the noise is progressively filtered out throughout the propagation phase. This leads to the conception of the feature reduction network (FRNet), which is a distinctive CNN architecture that incrementally eliminates informative features, thereby resulting in the output of noise. Our experimental results indicate that the CNN-driven FRNet surpasses previous state-of-the-art (SOTA) methods in terms of the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) evaluation metrics. This highlights the effectiveness of the FRNet across various image dehazing datasets. With its reduced overhead, the CNN-based FRNet demonstrates superior performance over current SOTA methods, thereby affirming the efficacy of CNNs in image dehazing tasks.

Fringing reef growth in the Mid-Devonian: An example from the southern Rhenish Massif, Germany

The Givetian Balduinstein Reef developed upon mixed felsic lavas and volcaniclastic tuffs. The Givetian age of the reef is proven by conodonts (Polygnathus rhenanus/varcus Zone to the Polygnathus ansatus Zone) and the occurrence of the brachiopod Stringocephalus burtini. Remains of land plants and subaerially ejected volcanic bombs in the vicinity of the outcrops document that the volcano was an emerged island surrounded by a fringing reef in an unstable tectonic environment. Reef development is characterised by a complex interaction of volcanism, erosion and the rate and pattern of sea-level change, which resulted in a variety of facies settings and rock-types. Microfacies analysis and rock-types prove that the fringing reef was backed by a shallow lagoon. A lime mudstone/alkali rhyolitic breccia represents a lava flow, or more probably a dome collapse breccia, which flowed into the lagoonal mud and confirms a contemporaneous occurrence of volcanism and reef growth. Reefs flourished during several episodes of inactivity of the volcano, particularly in the upper part of the sections, suggesting that the reef development kept up with sea-level changes and/or local events. Lithofacies of the upper part of the youngest section point to a regressive phase. The distinctive depositional architecture and evolution of the Balduinstein Reef are interpreted as having been controlled mainly by regional tectonics and volcanism with contributions from eustasy.

Fabrication of Porous Reduced Graphene Oxide Encapsulated Cu(OH)2 Core-shell Structured Carbon Fiber-Based Electrodes for High-Performance Flexible Supercapacitors.

To explore next-generation flexible supercapacitors, lightweight, superior conductivity, low cost, and excellent capacitance are the preconditions for practical use. However, subjected to unsatisfactory conductivity, limited surface areas, and poor porosity leading to long ion transport channels, carbon fiber (CF)-based flexible supercapacitors need to further boost the electrochemical properties. Hence, a porous reduced graphene oxide encapsulated Cu(OH)2 core-shell structured CF-based electrode was fabricated through a scalable approach. The inexpensive Cu(OH)2 nanoarrays were controllably grown in situ on a CF substrate, with residual Cu promoting conductivity. Porous graphene oxide (PrGO), which served as the shell, was realized by Ni nanoparticle etching, which not only provided more active sites for capacitance as well as shortened accessible pathways for the ion transport but also effectively alleviated the exfoliation of the internal active materials. Moreover, thanks to this distinctive core-shell architecture, the extra space between the outer PrGO layer and the internal ordered Cu(OH)2 nanoarrays provided increased space for capacitance storage. The assembled PrGO/Cu(OH)2/Cu@CF electrode exhibited an excellent areal capacitance, reaching up to 722 mF cm-2 at a current density of 0.5 mA cm-2, attributed to its superior structure and materials advantages. The resulting PrGO/Cu(OH)2/Cu@CF//AC//CF asymmetric flexible all-solid-state supercapacitor achieved a high energy density of 0.052 mWh cm-2 and exhibited long-term durability. This work proposes a low-cost and effective way to fabricate hierarchically structured electrodes for wearable CF-based supercapacitors.