Hybrid Combination Research Articles

Single-center Experience on the Elective Hybrid Combination of Single Perclose + Angio- Seal VIP 8F Compared to Standard Dual Perclose During Percutaneous Endovascular Aortic Aneurysm Repair

Single-center Experience on the Elective Hybrid Combination of Single Perclose + Angio- Seal VIP 8F Compared to Standard Dual Perclose During Percutaneous Endovascular Aortic Aneurysm Repair

Exploring UMAP in hybrid models of entropy-based and representativeness sampling for active learning in biomedical segmentation

In this work, we study various hybrid models of entropy-based and representativeness sampling techniques in the context of active learning in medical segmentation, in particular examining the role of UMAP (Uniform Manifold Approximation and Projection) as a technique for capturing representativeness. Although UMAP has been shown viable as a general purpose dimension reduction method in diverse areas, its role in deep learning-based medical segmentation has yet been extensively explored. Using the cardiac and prostate datasets in the Medical Segmentation Decathlon for validation, we found that a novel hybrid combination of Entropy-UMAP sampling technique achieved a statistically significant Dice score advantage over the random baseline (3.2% for cardiac, 4.5% for prostate), and attained the highest Dice coefficient among the spectrum of 10 distinct active learning methodologies we examined. This provides preliminary evidence that there is an interesting synergy between entropy-based and UMAP methods when the former precedes the latter in a hybrid model of active learning.

A Secure and Efficient Multi-Step Multi-Index Image Encryption Scheme Using Hybrid Combination of Substitution, Permutation and Hyperchaotic Structure

A Secure and Efficient Multi-Step Multi-Index Image Encryption Scheme Using Hybrid Combination of Substitution, Permutation and Hyperchaotic Structure

Наследование массы 1000 зерен гибридами ярового ячменя

The purpose of research is to study the nature of inheritance of the trait “weight of 1000 grains” by hybrids of spring barley in the conditions of the Krasnoyarsk forest-steppe. The experiments were carried out in the forest-steppe zone of the Krasnoyarsk Region in 2019–2021 on ordinary thin chernozem: humus content (according to Tyurin) – 4.67 %, N-NO3 (ionometric express method) – 10.5 mg/kg soil, P2O5 and K2O (according to Chirikov) – 21.2 and 16.4 mg/100 g of soil, respectively, the reaction of the soil solution in the water extract is neutral (pH 6.6). The predecessor is pure fallow. The weather conditions of the growing seasons during the years of study were characterized by uneven precipitation between months and were similar: 2020 – excess moisture (HTC = 1.62) and 2021. – sufficient moisture (HTC = 1.38). Four parental forms took part in the genetic analysis – Olenek, E-76-5695, Bagrets and Kalita; crosses were carried out according to a complete 4×4 diallelic scheme. The weight of 1000 grains in most hybrid combinations is inherited according to the type of intermediate inheritance (Hp = –0.5…+0.5). For this trait, the variety Kalita (46.4; 53.0 g) and the hybrids obtained with its participation were identified: Olenek × Kalita (52.3 g), Kalita × Olenek (54.3 g), E-76-5695 × Kalita (50.0), Kalita × Bagrets (51.3). In the first generation (F1), types of inheritance of the number of grains in an ear were noted – from depression (D–) to positive overdominance (SD+), 25.0 % of combinations showed true heterosis (Hp from +1.42 to +11.83). The stu¬died trait is controlled predominantly by a dominant genetic system with a predominance of recessive gene effects (F = –5.24 and –4.20 < 0) in both generations. The variety Kalita ((Veresk × Roland) × Gonar) of Chelyabinsk selection is recommended as a donor for increasing the weight of 1000 grains of barley (gi – 2.3354 and 1.1188).

Experimental study on seismic performance of squat RC shear walls reinforced with hybrid steel and GFRP rebars

This laboratory study investigates the effect of using steel and glass fiber reinforced polymer (GFRP) rebars in shear walls with aspect ratios less than two. The study evaluates six full-scale shear walls subjected to constant axial load and cyclic lateral loading, with aspect ratios of 1.08 and 1.75. The aim is to analyze how the use of steel and GFRP rebars affects crack patterns, fracture, seismic performance, dissipated energy, and ductility of shear walls. The six specimens consist of two reference ones (SRC1 and SRC2) reinforced with steel rebars, two specimens (GRC1 and GRC2) reinforced with GFRP rebars, and two specimens (S-GRC1 and S-GRC2) reinforced with a hybrid of steel and GFRP rebars. The conclusions drawn suggest that employing a hybrid combination of steel and GFRP rebars resulted in a notable alteration of crack propagation behavior and failure mode, transitioning from flexural compression to flexural tension. Additionally, compared to the specimens reinforced solely with GFRP rebars, there was an observed expansion of the hysteresis curve. Additionally, parameters such as cumulative dissipated energy, ductility, and load modification factor based on ductility increased in the S-GRC1 and S-GRC2 specimens compared to the GRC1 and GRC2.

Energy Load Forecasting Techniques in Smart Grids: A Cross-Country Comparative Analysis

Energy management systems allow the Smart Grids industry to track, improve, and regulate energy use. Particularly, demand-side management is regarded as a crucial component of the entire Smart Grids system. Therefore, by aligning utility offers with customer demand, anticipating future energy demands is essential for regulating consumption. An updated examination of several forecasting techniques for projecting energy short-term load forecasts is provided in this article. Each class of algorithms, including statistical techniques, Machine Learning, Deep Learning, and hybrid combinations, are comparatively evaluated and critically analyzed, based on three real consumption datasets from Spain, Germany, and the United States of America. To increase the size of tiny training datasets, this paper also proposes a data augmentation technique based on Generative Adversarial Networks. The results show that the Deep Learning-hybrid model is more accurate than traditional statistical methods and basic Machine Learning procedures. In the same direction, it is demonstrated that more comprehensive datasets assisted by complementary data, such as energy generation and weather, may significantly boost the accuracy of the models. Additionally, it is also demonstrated that Generative Adversarial Networks-based data augmentation may greatly improve algorithm accuracy.

Long-term levels of protection of different types of immunity against the Omicron variant: a rapid literature review.

With the emergence of newer SARS-CoV-2 variants and their substantial effects on the levels and duration of protection against infection, an understanding of these characteristics of the protection conferred by humoral and cellular immunity can aid in the proper development and implementation of vaccine and safety guidelines. We conducted a rapid literature review and searched five electronic databases weekly from 1 November 2021 to 30 September 2022. Studies that assessed the humoral or cellular immunity conferred by infection, vaccination or a hybrid (combination of both) in adults and risk groups (immunocompromised and older populations) were identified. Studies were eligible when they reported data on immunological assays of COVID-19 (related to vaccination and/or infection) or the effectiveness of protection (related to the effectiveness of vaccination and/or infection). We screened 5103 studies and included 205 studies, of which 70 provided data on the duration of protection against SARS-CoV-2 infection. The duration of protection of adaptive immunity was greatly impacted by Omicron and its subvariants: levels of protection were low by 3-6 months from exposure to infection/vaccination. Although more durable, cellular immunity also showed signs of waning by 6 months. First and second mRNA vaccine booster doses increased the levels of protection against infection and severe disease from Omicron and its subvariants but continued to demonstrate a high degree of waning over time. All humoral immunities (infection-acquired, vaccine-acquired and hybrid) waned by 3-6 months. Cellular immunity was more durable but showed signs of waning by 6 months. Hybrid immunity had the highest magnitude of protection against SARS-CoV-2 infection. Boosting may be recommended as early as 3-4 months after the last dose, especially in risk groups.

Minimisation of the Energy Expenditure of Electric Vehicles in Municipal Service Companies, Taking into Account the Uncertainty of Charging Point Operation

This article presents an original method for minimising the energy expenditure of electric vehicles used in municipal service undertakings, taking into account the uncertainty in the functioning of their charging points. The uncertainty of the charging points’ operation was presented as the probability of the occurrence of an emergency situation hindering a point’s operation, e.g., a breakdown or lack of energy supply. The problem is how to calculate the driving routes of electric vehicles so that they will arrive at charging points at times at which there is a minimal probability of breakdowns. The second aspect of this problem to be solved is that the designated routes are supposed to ensure the minimum energy expenditure that is needed for the vehicles to complete the tasks assigned. The developed method is based on two heuristic algorithms, i.e., the ant algorithm and genetic algorithms. These algorithms work in a hybrid combination, i.e., the ant algorithm generates the initial population for the genetic algorithm. An important element of this method is the decision-making model for defining the driving routes of electric vehicles with various restrictions, e.g., their battery capacity or the permissible risk of charging point breakdown along the routes of the vehicles. The criterion function of the model was defined as the minimisation of the energy expenditure needed by the vehicles to perform their transport tasks. The method was verified against real-life data, and its effectiveness was confirmed. The authors presented a method of calibrating the developed optimisation algorithms. Theoretical distributions of the probability of charging point failure were determined based on the Statistica 13 program, while a graphical implementation of the method was carried out using the PTV Visum 23 software.

Breeding value of potato hybrid clones from the VIR collection revealed in the environments of Kamchatka Territory

Background. Potato breeding involves planned artificial hybridization between previously selected parental lines. A way to make potato breeding more efficient is to use the diversity of the germplasm and broaden the gene pool of parental lines. Interspecific potato hybrids originated on the elite potato germplasm display a set of valuable traits. The current challenge is to assess the performance of potato hybrids under specific soil and climate conditions to clarify their breeding value.The objective of the study was to evaluate potato hybrids in the environments of Kamchatka Territory according to a set of traits, identify the best parental lines, develop new hybrid combinations, and select promising breeding material.Materials and methods. Eleven potato hybrids from VIR were studied over three years (2020–2022) in the fields of Kamchatka Research Institute of Agriculture. The hybrids were benchmarked against two reference potato cultivars, ‘Fresco’ and ‘Vulkan’, belonging to the early and mid-early maturity groups, respectively. Crosses between the hybrids and 28 potato cultivars were carried out in the greenhouse. The new hybrid material was tested in a preliminary trial nursery for yield and earliness.Results. Hybrid 117-2 was better in yield (899 g/plant) than cvs. ‘Vulkan’ and ‘Fresco’ (700–739 g/plant). Hybrids 94-5, 99-10- 1, 8-1-2004, 8-3-2004, 8-5-2004, 135-3-2005, 135-5-2005, 99-6-6, and 134-2-2006 in most traits matched the potato cultivars, manifesting good berry formation and seed setting when crossed. Out of 140 crosses between 11 hybrids and 28 cultivars, 35 crosses between 10 hybrids and 13 cultivars were successful. Most hybrids were effectively used in crossings as maternal forms.Conclusion. VIR’s hybrids 94-5, 8-1-2004, and 8-5-2004 proved the most promising as source material for the development of new early potatoes under the conditions of Kamchatka Territory.

Glass–Carbon–Kevlar fiber reinforced hybrid polymer composite (HPC): Part (A) mechanical and thermal characterization for high GSM laminates

The quest for light weight hybrid polymer composites (HPC) has resulted into multiple materials and structural configurations for achieving high performance in automotive and aerospace applications. Incidentally, the past reported work has (in general) involved variable GSM while investigating reinforced fiber layers. The variable GSM may lead to a random response of each layer to the applied forces and thermal degradation, due to which attributing the role of various layers to results/properties is difficult to ascertain. This research employs a uniform/consistent approach with high GSM (400) based HPC with multiple stacking sequences of glass (G), carbon (C), and Kevlar (K) to investigate thermo-mechanical properties. The research first focuses on fabrication of eleven stacking sequences of hybrid combinations, followed by identification of an optimal sequence based on mechanical (tensile strength, flexural strength, charpy impact resistance) and thermogravimetric analysis (TGA) characterization. Additionally, scanning electron microscopy (SEM) for fracture mechanisms of hybrid composites showing fiber pull out, matrix crack, and delamination. Results show that the tertiary combination having 2 Glass, 5 Carbon and 5 Kevlar layers (G2C5K5) named H9 herein provides a good balance of tensile, flexural, impact resistance and thermal properties; its deviation from the best of each category is within approximately 5, 13.5, 9.9 and 10.9 % (for tensile, flexural, impact, TGA) respectively. The range of properties evaluated in this study is deemed suitable for lightweight aircraft structures.

Analysis of Combining Ability to Obtain Tropical Carrot Hybrids for Production Traits

Carrots (Daucus carota L.), a globally significant vegetable, lack extensive research on heterotic groups and diallel analysis to generate hybrid combinations. Thus, the objective of this study was to assess combining abilities and identify optimal carrot parents for producing hybrids suitable for tropical climates with elevated metabolite levels. Twenty carrot hybrids, ten parent plants, and three commercial cultivars were evaluated during the summers of 2020/2021 and 2021/2022. Agronomic evaluations were carried out and chlorophyll and carotenoid levels were determined, followed by a diallel analysis using Griffing’s Method III and GGE biplot analysis. There were significant general combining ability (GCA) effects for various agronomic traits, suggesting additive genetic effects. Based on GCA, cultivars 5, 4, and 2 were the most promising parents. Specific combining ability (SCA) revealed that hybrids 1 × 2 and 3 × 5 stood out in environment 1, whereas hybrids 1 × 5 and 5 × 3 performed well in environment 2. The GGE biplot analysis showed that hybrids 1 × 2 and 3 × 2 displayed larger average root diameters, belonged to the group with the best bolting percentages, and exhibited stability across environments. Moreover, hybrids 2 × 4, 3 × 1, 4 × 1, and 4 × 2 exhibited higher metabolite levels. These findings suggest the feasibility of obtaining superior hybrids tailored for the tropical carrot market.

Evaluation of specific combining ability and heterosis of 6 melon (Cucumis melo L. var. inodorus) self-pollinated lines at the 5th generation

The study was carried out from August 2022 to February 2023 at the Biotechnology Center of Ho Chi Minh City with the objective of determining inbred melon lines with high combining ability and heterosis. The experiment was executed in plastic house condition and laid out in a completely randomized design with single factor with 3 replications, including 16 treatments corresponding to 15 hybrid combinations from 6 melon self-pollinated lines at the 5th generation (S5) and Kim Hong Ngoc variety as control. The results indicated that the melon line DLE06 used as female showed a high positive specific combining ability (SCA) for both fruit weight and the Brix of fruit with the DLE04 line used as male (THL14). Two hybrid combinations with high heterosis for fruit weight and the Brix of fruit flesh, including THL11 (1416.7 g; 16.33%) and THL14 (1520.0 g; 16.57%). Briefly, THL14 shows good growth and development potential in plastic houses, with morphological characteristics meeting the consumers’ preferences, and has potential to be developed into a new variey for production.

Physics informed machine learning based applications for the stability analysis of breakwaters

ABSTRACT One of the key aspects in designing and stability analysis of breakwater structures is predicting the stability number of their armour blocks. This study presents a novel approach called physics informed deep neural network, for the stability analysis of rubble-mound breakwaters. The present work makes two main contributions. Firstly, it proposes a method for creating hybrid combinations of theoretical models or physical models and deep neural network architectures, leveraging the advantages of both physics and data. This framework incorporates the output of physics-based simulations and observational features into a hybrid modelling setup. Secondly, the framework employs physics-based loss functions in the learning objective of these deep neural networks, which not only demonstrate lower errors on the training set but also adhere to the established physical relations. The proposed study may have the potential to address the existing limitations in this field and provide better accuracy in estimating the stability number.

A Novel Hybrid Vision Transformer CNN for COVID-19 Detection from ECG Images

The emergence of the novel coronavirus in Wuhan, China since 2019, has put the world in an exotic state of emergency and affected millions of lives. It is five times more deadly than Influenza and causes significant morbidity and mortality. COVID-19 mainly affects the pulmonary system leading to respiratory disorders. However, earlier studies indicated that COVID-19 infection may cause cardiovascular diseases, which can be detected using an electrocardiogram (ECG). This work introduces an advanced deep learning architecture for the automatic detection of COVID-19 and heart diseases from ECG images. In particular, a hybrid combination of the EfficientNet-B0 CNN model and Vision Transformer is adopted in the proposed architecture. To our knowledge, this study is the first research endeavor to investigate the potential of the vision transformer model to identify COVID-19 in ECG data. We carry out two classification schemes, a binary classification to identify COVID-19 cases, and a multi-class classification, to differentiate COVID-19 cases from normal cases and other cardiovascular diseases. The proposed method surpasses existing state-of-the-art approaches, demonstrating an accuracy of 100% and 95.10% for binary and multiclass levels, respectively. These results prove that artificial intelligence can potentially be used to detect cardiovascular anomalies caused by COVID-19, which may help clinicians overcome the limitations of traditional diagnosis.

Comparative fracture properties of ambient-cured geopolymer concrete containing four different fibers in mono and hybrid combinations using digital image correlation

This paper presents an investigation on the effects of four different fibers (polypropylene (PP), polyvinyl alcohol (PVA), recycled polypropylene (RPP), and steel (S) fibers) on the fracture behaviour of ambient-cured fly ash geopolymer concrete (GPC). The four fibers were added into GPC by both mono at 1.0% volume and hybrid combinations with steel fiber at 0.5% of each other type of fiber. The full-field strain distribution and the crack mouth opening displacement (CMOD) were monitored by digital image correlation (DIC) technology. The results reveal that adding fibers was effective in enhancing the fracture performance and hindering the cracking behaviour of GPC. Furthermore, 1.0% volume steel fiber exhibited to be most advantageous in enhancing the flexural strength by 100%, flexural toughness by 4000%, fracture energy by 3897%, and critical stress intensity factor by 98.8%. GPC with 1.0% volume PP fiber exhibited a characteristic length of 7909 mm, improving the characteristic length of GPC by about 2925%. Additionally, the CMOD was found to have a strong linear relationship with deflection which is unaffected by other factors. The presence of fibers significantly enhanced the ultimate strain. The steel fibers exhibited better performance in hindering the crack propagation. The fracture process zone (FPZ) characteristics differed between specimens with non-metallic and steel fibers, with the latter exhibiting more tortuous shapes and multiple cracks. Notably, a hybridization of steel and non-metallic fibers proves better than mono non-metallic fibers in improving strength, toughness, and ductility. This research provides valuable insights for concrete design and the selection of appropriate fibers based on specific requirements.

Enhanced freeze-thaw resilience of cement mortars through Nano-SiO2 and single/hybrid basalt fiber incorporation: Assessing workability, strength, durability

This study investigated how freeze-thaw cycles (FTC) impact concrete durability and structural integrity, exploring novel approaches like integrating nanomaterials and fibers into concrete compositions. After subjecting the materials to FTC, the study evaluated mortars enriched with nano-SiO2 (NS) and micro- and macro-basalt (BA) fibers. NS was incorporated by substituting 1 % and 2 % of the cement content. Meanwhile, 24 mm in length macro-BA fibers were added individually or in hybrid compositions with 6 mm micro-BA fibers at 0.5 % and 1 % volumetric ratios. The results revealed significant improvements in both the strength and durability of mortar specimens post-FTC, attributed to the enhancing properties of NS. Its capacity to fill voids and substantial pozzolanic activity notably improved the material's performance. However, adding BA fibers adversely affected mortar workability, causing a decrease in flow diameters (FD) as the fiber ratio increased.Nevertheless, BA fibers effectively maintained specimen integrity post-FTC despite leading to decreased residual compressive (RCS) and flexural strengths (RFS). This reduction was linked to the robust bonding between BA fibers and the matrix, impeding crack propagation post-FTC. Interestingly, combining BA fibers in hybrid configurations improved workability and significantly enhanced residual strength characteristics, surpassing singular applications. For instance, after 180 FTC, the K0 control specimen exhibited a 34.17 % and 34.56 % reduction in RCS and RFS, respectively. In contrast, the K10 specimen with 2 % NS and a hybrid combination of BA fibers at a volumetric ratio of 1 % displayed notably lower reduction rates of 23.69 % and 16.99 %, respectively.

Meta-Analysis of the Effect of Inorganic Micro/Nanomaterial Additives on Polyaniline Based NH<sub>3</sub> Gas Sensor Performance

Nowadays conducting polymer based nanocomposites become promising materials for various field of applications like energy harvesting, electronics, and gas sensing devices. This work focuses on the meta-analysis of the effect of different inorganic micro/nano-material additives on polyaniline (PAni) based nanocomposite for ammonia (NH3) gas sensor application at room temperature. The considered NH3 sensors performance parameters are sensitivity, limit of detection (LOD), response time, and recovery time. These parameters show a significant change when inorganic materials like graphene, metal oxides and ternary hybrid materials are mixed with PAni as compared to pure PAni due to the synergetic effect of the micro/nano hybrid combination. The changes in the sensitivity, LOD, response time, and recovery time are elaborated by considering different inorganic micro/nano-material additives in PAni in the framework of pure PAni as a reference point. It is found from analysis that a micro/nano additive in the PAni matrix serve as catalyst and create more active sites in the system, which improves the sensitivity in the range of 23-130 times and LOD is highly reduced by 10-1 to 10-3 order when compared with the sensitivity and LOD of pure PAni. Hence these additives in PAni-based nanocomposite are very crucial and make nanocomposite cost-effective compared to conventional NH3 gas sensors while working at room temperature.

Investigating the effect of steel micro‐ and macro‐fibers on the bond behavior of steel rebar embedded in ultra‐high performance concrete

AbstractIn recent years, the use of ultra‐high performance concrete (UHPC) has been widely considered in special structures and retrofitting of existing structures. Since most of the relations proposed by the regulations are based on the behavior of concrete with normal strength concrete, it is necessary to conduct more experimental studies to predict the behavior of UHPC. One of these notable behaviors is the bond and cohesion between rebar and UHPC, which plays a critical role in the design and seismic behavior of structures. In the present study, a series of experimental tests of uniaxial compressive strength, splitting (Brazilian) tensile strength, elastic modulus, and pull‐out of ribbed steel bars from ultra‐high performance fiber‐reinforced concrete (UHPFRC) were performed. The tests were done considering the four‐volume fractions of hooked‐end steel macro‐fibers, straight steel micro‐fibers, a hybrid combination of these two fibers, three different diameters of ribbed steel bars, and several embedded lengths. Moreover, direct tensile and compressive stress–strain curves, Young's modulus, bonding properties of rebar with UHPFRC, and fracture mechanisms were considered. The results indicated that applying micro‐fibers may enhance the bond stress between rebar and UHPC better than macro‐fibers. Also applying hybrid fibers resulted in the concurrent enhancement of bond stress and the optimum point slip of UHPC in the stress–slip diagram. This finding maybe attributed to the satisfactory performance of micro‐fibers in reducing the growth speed of micro‐level cracks and the suitable effect of macro‐fibers in decreasing the progression speed of macro‐level cracks. Furthermore, it was revealed that the increase in rebar diameter and embedding length leads to the reduction of maximum bond stress in UHPFRC.

CFD modeling of a triple‐walled direct absorption evacuated tube solar collector based on hybrid nanofluid/microencapsulated PCM

AbstractNowadays, direct absorption solar collectors are a new concept of solar collectors which have attracted special attention. The type of heat transfer fluid (HTF) has a significant influence on the efficiency of this kind of collector. Therefore, in this study, a wide range of working fluids including mono nanofluid ( and CuO), binary nanofluid, and especially the incorporation of a new hybrid combination of nanofluid and microencapsulated phase change material (MPCM), are used as the working fluids of a triple‐walled direct absorption evacuated tube solar collector. The computational fluid dynamics (CFD) method is used for simulating the collector and investigating the effect of different parameters including volume fraction, base fluid, mass flow rate, and type of absorber tube structure (double‐ and triple‐walled) on collector thermal performance. Results show that binary nanofluids of 0.06% /0.002% CuO/water and 0.06% /0.002% CuO/ethylene glycol (EG) have the largest working fluid temperature differentials equal to 48.31 and 66.5 K, respectively. It was inferred that, at the mass flow rate of 2.7 , the efficiency is obtained to be 60.29% employing binary/EG nanofluid, which is 8.68% and 15.31% higher than CuO/EG and /EG nanofluids, respectively. Inserting the hybrid CuO nanofluid/MPCM leads to an improvement of 1.14% and 1.22% in the efficiency of triple‐ and double‐walled collectors, respectively, with respect to the individual usage of the nanofluid. The thermal efficiency of the double‐walled structure is higher than triple‐walled considering all HTFs.

Dual-Scale Spiral Material for Balancing High Load Bearing and Sound Absorption.

Porous materials with sound absorption and load-bearing capabilities are in demand in engineering fields like aviation and rail transportation. However, achieving both properties simultaneously is challenging due to the trade-off between interconnected pores for sound absorption and mechanical strength. Inspired by quilling art, a novel design using spiral material formed by rolling planar materials into helical structures is proposed. Experimental results show high structural strength through self-locking mechanisms, while double porosities from interlayer spiral slits and aligned submillimeter pores provide excellent sound absorption. These spiral sheets surpass foam aluminum in specific strength (up to 5.1MPa) and approach aerogels in sound absorption (average coefficient of 0.93 within 0-6400Hz). With its adaptability to various planar materials, this spiral design allows for hybrid combinations of different materials for multi-functionality, paving the way for designing advanced, lightweight porous materials for broad applications.