Smart Cellulose‑Based Janus Fabrics with Switchable Liquid Transportation for Personal Moisture and Thermal Management

The Janus fabrics designed for personal moisture/thermal regulation have garnered significant attention for their potential to enhance human comfort.However,the development of smart and dynamic fabrics capable of managing personal moisture/thermal comfort in response to changing external environments remains a challenge.Herein,a smart cellulose-based Janus fabric was designed to dynamically manage personal moisture/heat.The cotton fabric was grafted with N-isopropylacrylamide to construct a temperature-stimulated transport channel.Subsequently,hydrophobic ethyl cellulose and hydrophilic cellulose nanofiber were sprayed on the bottom and top sides of the fabric to obtain wettability gradient.The fabric exhibits anti-gravity directional liquid transportation from hydrophobic side to hydrophilic side,and can dynamically and continuously control the transportation time in a wide range of 3–66 s as the temperature increases from 10 to 40℃.This smart fabric can quickly dissipate heat at high temperatures,while at low temperatures,it can slow down the heat dissipation rate and prevent the human from becoming too cold.In addition,the fabric has UV shielding and photodynamic antibacterial properties through depositing graphitic carbon nitride nanosheets on the hydrophilic side.This smart fabric offers an innovative approach to maximizing personal comfort in environments with significant temperature variations.

Multi-Layer Feature Extraction with Deformable Convolution for Fabric Defect Detection

In the textile industry,the presence of defects on the surface of fabric is an essential factor in determining fabric quality.Therefore,identifying fabric defects forms a crucial part of the fabric production process.Traditional fabric defect detection algorithms can only detect specific materials and specific fabric defect types;in addition,their detection efficiency is low,and their detection results are relatively poor.Deep learning-based methods have many advantages in the field of fabric defect detection,however,such methods are less effective in identifying multiscale fabric defects and defects with complex shapes.Therefore,we propose an effective algorithm,namely multilayer feature extraction combined with deformable convolution(MFDC),for fabric defect detection.In MFDC,multi-layer feature extraction is used to fuse the underlying location features with high-level classification features through a horizontally connected top-down architecture to improve the detection of multi-scale fabric defects.On this basis,a deformable convolution is added to solve the problem of the algorithm’s weak detection ability of irregularly shaped fabric defects.In this approach,Roi Align and Cascade-RCNN are integrated to enhance the adaptability of the algorithm in materials with complex patterned backgrounds.The experimental results show that the MFDC algorithm can achieve good detection results for both multi-scale fabric defects and defects with complex shapes,at the expense of a small increase in detection time.

Effect of Phase Change Materials on the Thermal Protective Performance of the Multi-layered Fabrics Examined by TPP Tester under Flash Fire

Cotton fabrics treated with phase change materials( PCMs)were used in multi-layered fabrics of the fire fighter protective clothing to study its effect on thermal protection. The thermal protective performance( TPP) of the multi-layered fabrics was measured by a TPP tester under flash fire. Results showed that the utilization of the PCM fabrics improved the thermal protective performance of the multi-layered fabrics. The fabric with a PCM add on of 41. 9% increased the thermal protection by 50. 6% and reduced the time to reach a second degree burn by 8. 4 s compared with the reference fabrics( without PCMs). The employment of the PCM fabrics also reduced the blackened areas on the inner layers. The PCM fabrics with higher PCM melting temperature could bring higher thermal protective performance.

Optimization Design of the Multi-Layer Cross-Sectional Layout of An Umbilical Based on the GA-GLM

Marine umbilical is one of the key equipment for subsea oil and gas exploitation,which is usually integrated by a great number of different functional components with multi-layers.The layout of these components directly affects manufacturing,operation and storage performances of the umbilical.For the multi-layer cross-sectional layout design of the umbilical,a quantifiable multi-objective optimization model is established according to the operation and storage requirements.Considering the manufacturing factors,the multi-layering strategy based on contact point identification is introduced for a great number of functional components.Then,the GA-GLM global optimization algorithm is proposed combining the genetic algorithm and the generalized multiplier method,and the selection operator of the genetic algorithm is improved based on the steepest descent method.Genetic algorithm is used to find the optimal solution in the global space,which can converge from any initial layout to the feasible layout solution.The feasible layout solution is taken as the initial value of the generalized multiplier method for fast and accurate solution.Finally,taking umbilicals with a great number of components as examples,the results show that the cross-sectional performance of the umbilical obtained by optimization algorithm is better and the solution efficiency is higher.Meanwhile,the multi-layering strategy is effective and feasible.The design method proposed in this paper can quickly obtain the optimal multi-layer cross-sectional layout,which replaces the manual design,and provides useful reference and guidance for the umbilical industry.

Multi-layer perceptron-based data-driven multiscale modelling of granular materials with a novel Frobenius norm-based internal variable

One objective of developing machine learning(ML)-based material models is to integrate them with well-established numerical methods to solve boundary value problems(BVPs).In the family of ML models,recurrent neural networks(RNNs)have been extensively applied to capture history-dependent constitutive responses of granular materials,but these multiple-step-based neural networks are neither sufficiently efficient nor aligned with the standard finite element method(FEM).Single-step-based neural networks like the multi-layer perceptron(MLP)are an alternative to bypass the above issues but have to introduce some internal variables to encode complex loading histories.In this work,one novel Frobenius norm-based internal variable,together with the Fourier layer and residual architectureenhanced MLP model,is crafted to replicate the history-dependent constitutive features of representative volume element(RVE)for granular materials.The obtained ML models are then seamlessly embedded into the FEM to solve the BVP of a biaxial compression case and a rigid strip footing case.The obtained solutions are comparable to results from the FEM-DEM multiscale modelling but achieve significantly improved efficiency.The results demonstrate the applicability of the proposed internal variable in enabling MLP to capture highly nonlinear constitutive responses of granular materials.

Multifunctional and Reconfigurable Electronic Fabrics Assisted by Artificial Intelligence for Human Augmentation

Noninvasive human augmentation,namely a desirable approach for enhancing the quality of life,can be achieved through wearable electronic devices that interact with the external environment.Wearable electronic devices endure limitations,such as unreliable signal interaction when bent or deformed,excessive wiring requirements,and lack of programmability and multifunctionality.Herein,we report an intelligent and programmable(IP)fabric sensor with bending insensitivity that overcomes these challenges associated with a rapid response time(<400μs)and exceptional durability(>20,000 loading-unloading cycles).A single-layer parallel electrical bilateral structure is utilized to design the IP fabric sensor with reconfigurability and only two electrodes,which caters to the requirement of stable interactions and simple wiring.The multifunctionality of the IP fabric sensor is demonstrated by designing a closed-loop interactive entertainment system,a smart home system,and a user identification and verification system.This integrated system reveals the potential of combining Internet of Things technology and artificial intelligence(AI).Hopefully,the integration of the noninvasive IP fabric sensor with AI will facilitate the advancement of interactive systems for human augmentation.

Modeling Locking Angle of the Multi-layered Biaxial Weft Knitted Fabric in Shear Deformation

This paper introduces the construction of the multi-layered biaxial weft knitted fabric （MBWK fabric） and studies the locking angle of this kind of fabric. Moreover, a locking angle model of the MBWK fabric is established for the first time according to its unique construction. Two kinds of locking angles are considered under different restraint conditions: the locking angle θ1 controlled by the inserting yarns and the locking angle θ2 controlled by the stitch yarns. It is concluded that the ultimate value of the locking angle θ is the larger one of the two angles.

Multi-layer phenomena in petawatt laser-driven acceleration of heavy ions

Laser-accelerated high-flux-intensity heavy-ion beams are important for new types of accelerators.A particle-in-cell program(Smilei) is employed to simulate the entire process of Station of Extreme Light(SEL) 100 PW laser-accelerated heavy particles using different nanoscale short targets with a thickness of 100 nm Cr, Fe, Ag, Ta, Au, Pb, Th and U, as well as 200 nm thick Al and Ca. An obvious stratification is observed in the simulation. The layering phenomenon is a hybrid acceleration mechanism reflecting target normal sheath acceleration and radiation pressure acceleration, and this phenomenon is understood from the simulated energy spectrum,ionization and spatial electric field distribution. According to the stratification, it is suggested that high-quality heavy-ion beams could be expected for fusion reactions to synthesize superheavy nuclei. Two plasma clusters in the stratification are observed simultaneously, which suggest new techniques for plasma experiments as well as thinner metal targets in the precision machining process.

Strength prediction of multi-layered copper-based composites fabricated by accumulative roll bonding

This work aims to evaluate the feasibility of the fabrication of nanostructured Cu/Al/Ag multi-layered composites by accumulative roll bonding(ARB),and to analyze the tensile properties and electrical conductivity of the produced composites.A theoretical model using strengthening mechanisms and some structural parameters extracted from X-ray diffraction is also developed to predict the tensile strength of the composites.It was found that by progression of ARB,the experimental and calculated tensile strengths are enhanced,reach a maximum of about 450 and 510 MPa at the fifth cycle of ARB,respectively and then are reduced.The electrical conductivity decreased slightly by increasing the number of ARB cycles at initial ARB cycles,but the decrease was intensified at the final ARB cycles.In conclusion,the merit of ARB to fabricate this type of multi-layered nanocomposites and the accuracy of the developed model to predict tensile strength were realized.

Research on the process of fabricating a multi-layer metal micro-structure based on UV-LIGA overlay technology

In this paper,we report the study of the process of fabricating a multi-layermetal micro-structure using UV-LIGA overlay technology,includingmask fabrication,substrate treatment,and UV-LIGA overlay processes.To solve the process problems in the masking procedure,the swelling problemof the first layer of SU-8 thick photoresist was studied experimentally.The 5μmline-width compensation and closed 20μmand 30μmisolation strips were designed and fabricated around the micro-structure pattern.The pore problemin the Ni micro-electroforming layer was analyzed and the electroforming parameters were improved.The pH value of the electroforming solution should be controlled between 3.8 and 4.4 and the current density should be below 3 A/dm^2.To solve the problems of high inner stress and incomplete development of the micro-cylinder hole array with a diameter of 30μm,the lithography process was optimized.The pre-baking temperature was increased via gradient heating and rose every 5℃ from 65℃ to 85℃ and then remained at 85℃ for 50 min–1 h.In addition,the full contact exposure was used.Finally,a multi-layer metal micro-structure with high precision and good quality of microelectroforming layer was fabricated using UV-LIGA overlay technology.

Target Controllability of Multi-Layer Networks With High-Dimensional Nodes

This paper studies the target controllability of multilayer complex networked systems,in which the nodes are highdimensional linear time invariant(LTI)dynamical systems,and the network topology is directed and weighted.The influence of inter-layer couplings on the target controllability of multi-layer networks is discussed.It is found that even if there exists a layer which is not target controllable,the entire multi-layer network can still be target controllable due to the inter-layer couplings.For the multi-layer networks with general structure,a necessary and sufficient condition for target controllability is given by establishing the relationship between uncontrollable subspace and output matrix.By the derived condition,it can be found that the system may be target controllable even if it is not state controllable.On this basis,two corollaries are derived,which clarify the relationship between target controllability,state controllability and output controllability.For the multi-layer networks where the inter-layer couplings are directed chains and directed stars,sufficient conditions for target controllability of networked systems are given,respectively.These conditions are easier to verify than the classic criterion.

Robust,Flexible,and Superhydrophobic Fabrics for High-Efficiency and Ultrawide-Band Microwave Absorption

Microwave absorption(MA)materials are essential for protecting against harmful electromagnetic radiation.In this study,highly efficient and ultrawide-band microwave-absorbing fabrics with superhydrophobic surface features were developed using a facile dip-coating method involving in situ graphene oxide(GO)reduction,deposition of TiO_(2) nanoparticles,and subsequent coating of a mixture of polydimethylsiloxane(PDMS)and octadecylamine(ODA)on polyester fabrics.Owing to the presence of hierarchically structured surfaces and low-surface-energy materials,the resultant reduced GO(rGO)/TiO_(2)-ODA/PDMS-coated fabrics demonstrate superhydrophobicity with a water contact angle of 159°and sliding angle of 5°.Under the synergistic effects of conduction loss,interface polarization loss,and surface roughness topography,the optimized fabrics show excellent microwave absorbing performances with a minimum reflection loss(RL_(min))of47.4 dB and a maximum effective absorption bandwidth(EAB_(max))of 7.7 GHz at a small rGO loading of 6.9 wt%.In addition,the rGO/TiO_(2)-ODA/PDMS coating was robust,and the coated fabrics could withstand repeated washing,soiling,long-term ultraviolet irradiation,and chemical attacks without losing their superhydrophobicity and MA properties.Moreover,the coating imparts self-healing properties to the fabrics.This study provides a promising and effective route for the development of robust and flexible materials with microwave-absorbing properties.

Engineering graphene oxide and hydrogel coatings on fabrics for smart Janus textiles with superior thermal regulation

Fabric multifunctionality offers resource savings and enhanced human comfort.This study innovatively integrates cooling,heating,and antimicrobial properties within a Janus fabric,surpassing previous research focused solely on cooling or heating.Different effects are achieved by applying distinct coatings to each side of the fabric.One graphene oxide(GO)coating exhibits exceptional light-to-heat conversion,absorbing and transforming light energy into heat,thereby elevating fabric temperature by 15.4℃,22.7℃,and 43.7℃ under 0.2,0.5,and 1 sun irradiation,respectively.Conversely,a hydrogel coating on one side absorbs water,facilitating heat dissipation through evaporation upon light exposure,reducing fabric temperature by 5.9℃,8.4℃,and 7.1℃ in 0.2,0.5,and 1 sun irradiation,respectively.Moreover,both sides of Janus fabric exhibit potent antimicrobial properties,ensuring fabric hygiene.This work presents a feasible solution to address crucial challenges in fabric thermal regulation,providing a smart approach for intelligent adjustment of body comfort in both summer and winter.By integrating heating and cooling capabilities along with antimicrobial properties,this study promotes sustainable development in textile techniques.

A flexible ultra-broadband multi-layered absorber working at 2 GHz-40 GHz printed by resistive ink

A flexible extra broadband metamaterial absorber(MMA)stacked with five layers working at 2 GHz–40 GHz is investigated.Each layer is composed of polyvinyl chloride(PVC),polyimide(PI),and a frequency selective surface(FSS),which is printed on PI using conductive ink.To investigate this absorber,both one-dimensional analogous circuit analysis and three-dimensional full-wave simulation based on a physical model are provided.Various crucial electromagnetic properties,such as absorption,effective impedance,complex permittivity and permeability,electric current distribution and magnetic field distribution at resonant peak points,are studied in detail.Analysis shows that the working frequency of this absorber covers entire S,C,X,Ku,K and Ka bands with a minimum thickness of 0.098λ_(max)(λ_(max) is the maximum wavelength in the absorption band),and the fractional bandwidth(FBW)reaches 181.1%.Moreover,the reflection coefficient is less than-10 dB at 1.998 GHz–40.056 GHz at normal incidence,and the absorptivity of the plane wave is greater than 80%when the incident angle is smaller than 50°.Furthermore,the proposed absorber is experimentally validated,and the experimental results show good agreement with the simulation results,which demonstrates the potential applicability of this absorber at 2 GHz–40 GHz.

Dynamic Multi-Layer Perceptron for Fetal Health Classification Using Cardiotocography Data

Fetal health care is vital in ensuring the health of pregnant women and the fetus.Regular check-ups need to be taken by the mother to determine the status of the fetus’growth and identify any potential problems.To know the status of the fetus,doctors monitor blood reports,Ultrasounds,cardiotocography(CTG)data,etc.Still,in this research,we have considered CTG data,which provides information on heart rate and uterine contractions during pregnancy.Several researchers have proposed various methods for classifying the status of fetus growth.Manual processing of CTG data is time-consuming and unreliable.So,automated tools should be used to classify fetal health.This study proposes a novel neural network-based architecture,the Dynamic Multi-Layer Perceptron model,evaluated from a single layer to several layers to classify fetal health.Various strategies were applied,including pre-processing data using techniques like Balancing,Scaling,Normalization hyperparameter tuning,batch normalization,early stopping,etc.,to enhance the model’s performance.A comparative analysis of the proposed method is done against the traditional machine learning models to showcase its accuracy(97%).An ablation study without any pre-processing techniques is also illustrated.This study easily provides valuable interpretations for healthcare professionals in the decision-making process.

Multi-layer network embedding on scc-based network with motif

Interconnection of all things challenges the traditional communication methods,and Semantic Communication and Computing(SCC)will become new solutions.It is a challenging task to accurately detect,extract,and represent semantic information in the research of SCC-based networks.In previous research,researchers usually use convolution to extract the feature information of a graph and perform the corresponding task of node classification.However,the content of semantic information is quite complex.Although graph convolutional neural networks provide an effective solution for node classification tasks,due to their limitations in representing multiple relational patterns and not recognizing and analyzing higher-order local structures,the extracted feature information is subject to varying degrees of loss.Therefore,this paper extends from a single-layer topology network to a multi-layer heterogeneous topology network.The Bidirectional Encoder Representations from Transformers(BERT)training word vector is introduced to extract the semantic features in the network,and the existing graph neural network is improved by combining the higher-order local feature module of the network model representation network.A multi-layer network embedding algorithm on SCC-based networks with motifs is proposed to complete the task of end-to-end node classification.We verify the effectiveness of the algorithm on a real multi-layer heterogeneous network.

Dynamic interwell connectivity analysis of multi-layer waterflooding reservoirs based on an improved graph neural network

The analysis of interwell connectivity plays an important role in the formulation of oilfield development plans and the description of residual oil distribution. In fact, sandstone reservoirs in China's onshore oilfields generally have the characteristics of thin and many layers, so multi-layer joint production is usually adopted. It remains a challenge to ensure the accuracy of splitting and dynamic connectivity in each layer of the injection-production wells with limited field data. The three-dimensional well pattern of multi-layer reservoir and the relationship between injection-production wells can be equivalent to a directional heterogeneous graph. In this paper, an improved graph neural network is proposed to construct an interacting process mimics the real interwell flow regularity. In detail, this method is used to split injection and production rates by combining permeability, porosity and effective thickness, and to invert the dynamic connectivity in each layer of the injection-production wells by attention mechanism.Based on the material balance and physical information, the overall connectivity from the injection wells,through the water injection layers to the production layers and the output of final production wells is established. Meanwhile, the change of well pattern caused by perforation, plugging and switching of wells at different times is achieved by updated graph structure in spatial and temporal ways. The effectiveness of the method is verified by a combination of reservoir numerical simulation examples and field example. The method corresponds to the actual situation of the reservoir, has wide adaptability and low cost, has good practical value, and provides a reference for adjusting the injection-production relationship of the reservoir and the development of the remaining oil.

Target layer state estimation in multi-layer complex dynamical networks considering nonlinear node dynamics

In many engineering networks, only a part of target state variables are required to be estimated.On the other hand,multi-layer complex network exists widely in practical situations.In this paper, the state estimation of target state variables in multi-layer complex dynamical networks with nonlinear node dynamics is studied.A suitable functional state observer is constructed with the limited measurement.The parameters of the designed functional observer are obtained from the algebraic method and the stability of the functional observer is proven by the Lyapunov theorem.Some necessary conditions that need to be satisfied for the design of the functional state observer are obtained.Different from previous studies, in the multi-layer complex dynamical network with nonlinear node dynamics, the proposed method can estimate the state of target variables on some layers directly instead of estimating all the individual states.Thus, it can greatly reduce the placement of observers and computational cost.Numerical simulations with the three-layer complex dynamical network composed of three-dimensional nonlinear dynamical nodes are developed to verify the effectiveness of the method.

A Well Productivity Model for Multi-Layered Marine and Continental Transitional Reservoirs with Complex Fracture Networks

Using the typical characteristics of multi-layered marine and continental transitional gas reservoirs as a basis,a model is developed to predict the related well production rate.This model relies on the fractal theory of tortuous capillary bundles and can take into account multiple gas flow mechanisms at the micrometer and nanometer scales,as well as the flow characteristics in different types of thin layers(tight sandstone gas,shale gas,and coalbed gas).Moreover,a source-sink function concept and a pressure drop superposition principle are utilized to introduce a coupled flow model in the reservoir.A semi-analytical solution for the production rate is obtained using a matrix iteration method.A specific well is selected for fitting dynamic production data,and the calculation results show that the tight sandstone has the highest gas production per unit thickness compared with the other types of reservoirs.Moreover,desorption and diffusion of coalbed gas and shale gas can significantly contribute to gas production,and the daily production of these two gases decreases rapidly with decreasing reservoir pressure.Interestingly,the gas production from fractures exhibits an approximately U-shaped distribution,indicating the need to optimize the spacing between clusters during hydraulic fracturing to reduce the area of overlapping fracture control.The coal matrix water saturation significantly affects the coalbed gas production,with higher water saturation leading to lower production.

Matching Dyeing and Properties of Silk Fabrics with Natural Edible Pigments

The silk fabrics were matching dyed with three natural edible pigments(red rice red,ginger yellow and gardenia blue).By investigating the dyeing rates and lifting properties of these pigments,it was observed that their compatibilities were excellent in the dyeing process:dye dosage 2.5%(omf),mordant alum dosage 2.0%(omf),dyeing temperature 80℃and dyeing time 40 min.The silk fabrics dyed with secondary colors exhibited vibrant and vivid color owing to the remarkable lightness and chroma of ginger yellow.However,gardenia blue exhibited multiple absorption peaks in the visible light range,resulting in significantly lower lightness and chroma for the silk fabrics dyed with tertiary colors,thus making it suitable only for matte-colored fabrics with low chroma levels.In addition,the silk fabrics dyed with these three pigments had a color fastness that exceeded grade 3 in resistance to perspiration,soap washing and light exposure,indicating acceptable wearing properties.The dyeing process described in this research exhibited a wide range of potential applications in matching dyeing of protein-based textiles with natural colorants.