Membranes with Molecular Gatekeepers for Efficient CO2 Capture and H2 Purification.

The urgent need for CO2 capture and hydrogen energy has attracted great attention owing to greenhouse gas emissions and global warming problems. Efficient CO2 capture and H2 purification with membrane technology will reduce greenhouse gas emissions and help reach a carbon-neutral society. Here, 4-sulfocalix[4]arene (SC), which has an intrinsic cavity, was embedded into the Matrimid membrane as a molecular gatekeeper for CO2 capture and H2 purification. The interactions between SC and the Matrimid polymer chains immobilize SC molecules into the interchain gaps of the Matrimid membrane, and the strong hydrogen and ionic bondings were able to form homogeneous mixed-matrix membranes. The incorporation of the SC molecular gatekeeper with exceptional molecular-sieving properties improved the gas separation performance of the mixed-matrix membranes. Compared with that of the Matrimid membrane, the CO2 permeability of the Matrimid-SC-3% membrane increased from 16.75 to 119.78 Barrer, the CO2/N2 selectivity increased from 29.39 to 106.95, and the CO2/CH4 selectivity increased from 29.91 to 140.92. Furthermore, when the permeability of H2 was increased to 172.20 Barrer, the H2/N2 and H2/CH4 selectivities reached approximately 153.75 and 202.59, respectively, which are far superior to those of most existing Matrimid-based materials. The mixed-matrix membranes also exhibited excellent long-term operation stability, with separation performance for several important gas pairs still overtaking the Robeson upper limit after aging for 400 days.

Newcastle disease virus activates diverse signaling pathways via Src to facilitate virus entry into host macrophages.

As an intrinsic cellular mechanism responsible for the internalization of extracellular ligands and membrane components, caveolae-mediated endocytosis (CavME) is also exploited by certain pathogens for endocytic entry [e.g., Newcastle disease virus (NDV) of paramyxovirus]. However, the molecular mechanisms of NDV-induced CavME remain poorly understood. Herein, we demonstrate that sialic acid-containing gangliosides, rather than glycoproteins, were utilized by NDV as receptors to initiate the endocytic entry of NDV into HD11 cells. The binding of NDV to gangliosides induced the activation of a non-receptor tyrosine kinase, Src, leading to the phosphorylation of caveolin-1 (Cav1) and dynamin-2 (Dyn2), which contributed to the endocytic entry of NDV. Moreover, an inoculation of cells with NDV-induced actin cytoskeletal rearrangement through Src to facilitate NDV entry via endocytosis and direct fusion with the plasma membrane. Subsequently, unique members of the Rho GTPases family, RhoA and Cdc42, were activated by NDV in a Src-dependent manner. Further analyses revealed that RhoA and Cdc42 regulated the activities of specific effectors, cofilin and myosin regulatory light chain 2, responsible for actin cytoskeleton rearrangement, through diverse intracellular signaling cascades. Taken together, our results suggest that an inoculation of NDV-induced Src-mediated cellular activation by binding to ganglioside receptors. This process orchestrated NDV endocytic entry by modulating the activities of caveolae-associated Cav1 and Dyn2, as well as specific Rho GTPases and downstream effectors. IMPORTANCE: In general, it is known that the paramyxovirus gains access to host cells through direct penetration at the plasma membrane; however, emerging evidence suggests more complex entry mechanisms for paramyxoviruses. The endocytic entry of Newcastle disease virus (NDV), a representative member of the paramyxovirus family, into multiple types of cells has been recently reported. Herein, we demonstrate the binding of NDV to induce ganglioside-activated Src signaling, which is responsible for the endocytic entry of NDV through caveolae-mediated endocytosis. This process involved Src-dependent activation of the caveolae-associated Cav1 and Dyn2, as well as specific Rho GTPase and downstream effectors, thereby orchestrating the endocytic entry process of NDV. Our findings uncover a novel molecular mechanism of endocytic entry of NDV into host cells and provide novel insight into paramyxovirus mechanisms of entry.

An Investigation into the Application of Acceleration Responses' Trendline for Bridge Damage Detection Using Quadratic Regression.

It has been proven that structural damage can be successfully identified using trendlines of structural acceleration responses. In previous numerical and experimental studies, the Savitzky-Golay filter and moving average filter were adjusted to determine suitable trendlines and locate structural damage in a simply supported bridge. In this study, the quadratic regression technique was studied and employed to calculate the trendlines of the bridge acceleration responses. The normalized energies of the resulting trendlines were then used as a damage index to identify the location and severity of the structural bridge damage. An ABAQUS model of a 25 m simply supported bridge under a truckload with different velocities was used to verify the accuracy of the proposed method. The structural damage was numerically modeled as cracks at the bottom of the bridge, so the stiffness at the damage positions was decreased accordingly. Four different velocities from 1 m/s to 8 m/s were used. The proposed method can identify structural damage in noisy environments without monitoring the dynamic modal parameters. Moreover, the accuracy of the newly proposed trendline-based method was increased compared to the previous method. For velocities up to 4 m/s, the damage in all single- and multiple-damage scenarios was successfully identified. For the velocity of 8 m/s, the damage in some scenarios was not located accurately. Additionally, it should be noted that the proposed method can be categorized as an online, quick, and baseline-free structural damage-detection method.

Dynamic Splitting Performance and Energy Dissipation of Fiber-Reinforced Concrete under Impact Loading.

In this paper, the influence of different fiber materials on the dynamic splitting mechanical properties of concrete was investigated. Brazil disc dynamic splitting tests were conducted on plain concrete, palm fiber-reinforced concrete, and steel fiber-reinforced concrete specimens using a split Hopkinson pressure bar (SHPB) test device with a 100 mm diameter and a V2512 high-speed digital camera. The Digital Image Correlation (DIC) technique was used to analyze the fracture process and crack propagation behavior of different fiber-reinforced concrete specimens and obtain their dynamic tensile properties and energy dissipation. The experimental results indicate that the addition of fibers can enhance the impact toughness of concrete, reduce the occurrence of failure at the loading end of specimens due to stress concentration, delay the time to failure of specimens, and effectively suppress the expansion of cracks. Steel fibers exhibit a better crack-inhibiting effect on concrete compared to palm fibers. The incident energy for the three types of concrete specimens is roughly the same under the same impact pressure. Compared with plain concrete, the energy absorption rate of palm fiber concrete is decreased, while that of steel fiber concrete is increased. Palm fiber-reinforced concrete and steel fiber-reinforced concrete have lower peak strains than plain concrete under the same loading duration. The addition of steel fibers significantly impedes the internal cracking process of concrete specimens, resulting in a relatively slow growth of damage variables.

Nonsingleton Gaussian type-3 fuzzy system with fractional order NTSMC for path tracking of autonomous cars.

Path-tracking and lane-keeping tasks are critical to guarantee safety and navigation performance considerations for deploying autonomous cars. This paper presents a novel control framework for the path-tracking control of high-speed autonomous cars with structured uncertainties. This study introduces a nonlinear adaptive control system based on a fractional-order terminal sliding mode system while incorporating a novel Gaussian Nonsingleton type-3 fuzzy system (FOTSM-NT3FS). Therefore, the proposed controller is independent of the information about the ego vehicle's dynamic information, and instead, the dynamics are approximated through a developed NT3FLS. The developed control system exhibits robustness to measurement errors and faulty sensors, because the inputs to the NT3FS are uncertain. In order to guarantee the boundedness of the adaptation parameters, the σ-mod approach is employed. The Lyapunov stability theorem and Barbalat's lemma are used to ensure the uniform ultimate boundedness of the closed loop system and the convergence of tracking errors to the origin in finite time. High-fidelity co-simulations with CarSim and MATLAB are performed to verify the effectiveness of the proposed control scheme and are also compared to other reported methods in the literature. Based on the obtained results, the schemed controller exhibits competitive effectiveness in path-tracking tasks and strong efficiency under various road conditions, parametric uncertainties, and unknown disturbances.

The role of lncRNA in the pathogenesis of chronic obstructive pulmonary disease.

Chronic obstructive pulmonary disease (COPD) is characterized by progressive and irreversible airflow obstruction with abnormal lung function. Because its pathogenesis involves multiple aspects of oxidative stress, immunity and inflammation, apoptosis, airway and lung repair and destruction, the clinical approach to COPD treatment is not further updated. Therefore, it is crucial to discover a new means of COPD diagnosis and treatment. COPD etiology is associated with complex interactions between environmental and genetic determinants. Numerous genes are involved in the pathogenic process of this illness in research samples exposed to hazardous environmental conditions. Among them, Long non-coding RNAs (lncRNAs) have been reported to be involved in the molecular mechanisms of COPD development induced by different environmental exposures and genetic susceptibility encounters, and some potential lncRNA biomarkers have been identified as early diagnostic, disease course determination, and therapeutic targets for COPD. In this review, we summarize the expression profiles of the reported lncRNAs that have been reported in COPD studies related to environmental risk factors such as smoking and air pollution exposure and provided an overview of the roles of those lncRNAs in the pathogenesis of the disease.

Dynamic Compressive and Flexural Behaviour of Re-Entrant Auxetics: A Numerical Study.

Re-entrant auxetics offer the potential to address lightweight challenges while exhibiting superior impact resistance, energy absorption capacity, and a synclastic curvature deformation mechanism for a wide range of engineering applications. This paper presents a systematic numerical study on the compressive and flexural behaviour of re-entrant honeycomb and 3D re-entrant lattice using the finite element method implemented with ABAQUS/Explicit, in comparison with that of regular hexagonal honeycomb. The finite element model was validated with experimental data obtained from the literature, followed by a mesh size sensitivity analysis performed to determine the optimal element size. A series of simulations was then conducted to investigate the failure mechanisms and effects of different factors including strain rate, relative density, unit cell number, and material property on the dynamic response of re-entrant auxetics subjected to axial and flexural loading. The simulation results indicate that 3D re-entrant lattice is superior to hexagonal honeycomb and re-entrant honeycomb in energy dissipation, which is insensitive to unit cell number. Replacing re-entrant honeycomb with 3D re-entrant lattice leads to an 884% increase in plastic energy dissipation and a 694% rise in initial peak stress. Under flexural loading, the re-entrant honeycomb shows a small flexural modulus, but maintains the elastic deformation regime over a large range of strain. In all cases, the compressive and flexural dynamic response of re-entrant auxetics exhibits a strong dependence on strain rate, relative density, and material property. This study provides intuitive insight into the compressive and flexural performance of re-entrant auxetics, which can facilitate the optimal design of auxetic composites.

A dual ultra-broadband switchable high-performance terahertz absorber based on hybrid graphene and vanadium dioxide.

A tunable dual broadband switchable terahertz absorber based on vanadium dioxide and graphene is proposed. The tunability of graphene and the phase transition properties of vanadium dioxide are used to switch broadband absorption between low-frequency and high-frequency, as well as the absorption rate tuning function. The simulation results indicate that when vanadium dioxide is in the insulating phase and the graphene Fermi energy is 0.7 eV, the absorber achieves low-frequency broadband absorption within the range of 2.6-4.2 THz with an absorptance greater than 90%; when vanadium dioxide is in the metallic phase and the graphene Fermi energy is 0 eV, the absorber achieves high-frequency broadband absorption within the range of 4.9-10 THz with an absorptance greater than 90%. Furthermore, the absorptance can be tuned by adjusting the conductivity of vanadium dioxide or the Fermi energy of graphene. Due to the central symmetry of the proposed structure, the absorber is completely insensitive to polarization. For TE and TM polarized waves, both low and high-frequency broadband absorption are maintained over a range of incident angles from 0° to 50°. The simple structure, tunable absorption rate, insensitivity to polarization angle and incident angle properties are advantages of our proposed absorber. It has broad application prospects in adjustable filters and electromagnetic shielding.

Numerical Analysis of Low-Velocity Impact Behaviour of Protective Concrete-Filled Steel Plates Composite Wall.

In this research, a protective concrete-filled steel plate composite wall (PSC) is developed, consisting of a core concrete-filled bilateral steel plate composite shear wall and two lateral replaceable surface steel plates with energy-absorbing layers. The PSC wall is characterised by high in-plane seismic performance as well as out-of-plane impact performance. Therefore, it could be employed primarily in high-rise constructions, civil defence initiatives, and buildings with stringent structural safety criteria. To investigate the out-of-plane low-velocity impact behaviour of the PSC wall, fine finite element models are validated and developed. Then, the influence of geometrical and dynamic loading parameters on its impact behaviour is investigated. The results show that the replaceable energy-absorbing layer could significantly decrease the out-of-plane displacement and plastic displacement of the PSC wall due to its large plastic deformation, which could absorb a significantly large amount of impact energy. Meanwhile, the PSC wall could maintain high in-plane seismic performance when subjected to impact load. The plastic yield-line theoretical model is proposed and utilised to predict the out-of-plane displacement of the PSC wall, and the calculated results agree very well with the simulated results.

Flat Photonic Crystal Fiber Plasmonic Sensor for Simultaneous Measurement of Temperature and Refractive Index with High Sensitivity.

A compact temperature-refractive index (RI) flat photonic crystal fiber (PCF) sensor based on surface plasmon resonance (SPR) is presented in this paper. Sensing of temperature and RI takes place in the x- and y- polarization, respectively, to avoid the sensing crossover, eliminating the need for matrix calculation. Simultaneous detection of dual parameters can be implemented by monitoring the loss spectrum of core modes in two polarizations. Compared with the reported multi-function sensors, the designed PCF sensor provides higher sensitivities for both RI and temperature detection. A maximum wavelength sensitivity of -5 nm/°C is achieved in the temperature range of -30-40 °C. An excellent optimal wavelength sensitivity of 17,000 nm/RIU is accomplished in the RI range of 1.32-1.41. The best amplitude sensitivity of RI is up to 354.39 RIU-1. The resolution of RI and temperature sensing is 5.88 × 10-6 RIU and 0.02 °C, respectively. The highest value of the figure of merit (FOM) is 216.74 RIU-1. In addition, the flat polishing area of the gold layer reduces the manufacturing difficulty. The proposed sensor has the characteristics of high sensitivity, simple structure, good fabrication repeatability, and flexible operation. It has potential in medical diagnosis, chemical inspection, and many other fields.

Tunable wideband-narrowband switchable absorber based on vanadium dioxide and graphene.

A functionally tunable and absorption-tunable terahertz (THz) metamaterial absorber based on vanadium dioxide (VO2) and graphene is proposed and verified numerically. Based on phase transition properties of VO2 and tunability of graphene, the switching performance between ultra-broadband and narrow-band near-perfect absorption can be achieved. We simulate and analyze the characteristics of the constructed model by finite element analysis. Theoretical calculations show that when VO2 is in the metallic state and the graphene Fermi energy is 0 eV, the designed absorber can perform ultra-broadband absorption. The absorber achieves greater than 95% absorption in the 2.85 - 10THz range. When VO2 is in the insulating state and the graphene Fermi energy is 0.7 eV, more than 99.5% absorption can be achieved at 2.3 THz. The absorption rate can be tuned by changing the conductivity of VO2 and the Fermi energy of graphene. Moreover, the proposed absorber displays good polarization insensitivity and wide incident angle stability. The design may have potential applications in terahertz imaging, sensing, electromagnetic shielding and so on.

Study on the Classification of Metal Objects by a Fluxgate Magnetometer Cube Structure.

After wars, some unexploded bombs remained underground, and these faulty bombs seriously threaten the safety of people. The ability to accurately identify targets is crucial for subsequent mining work. A deep learning algorithm is used to recognize targets, which significantly improves recognition accuracy compared with the traditional recognition algorithm for measuring the magnetic moment of the target and the included geomagnetism angle. In this paper, a ResNet-18-based recognition system is presented for classifying metallic object types. First, a fluxgate magnetometer cube arrangement structure (FMCAS) magnetic field feature collector is constructed, utilizing an eight-fluxgate magnetometer sensor array structure that provides a 400 mm separation between each sensitive unit. Magnetic field data are acquired, along an east-west survey line on the northern side of the measured target using the FMCAS. Next, the location and type of targets are modified to create a database of magnetic target models, increasing the diversity of the training dataset. The experimental dataset is constructed by constructing the magnetic flux density tensor matrix. Finally, the enhanced ResNet-18 is used to train the data for the classification recognition recognizer. According to the test findings of 107 validation set groups, this method's recognition accuracy is 84.1 percent. With a recognition accuracy rate of 96.3 percent, a recall rate of 96.4 percent, and a precision rate of 96.4 percent, the target with the largest magnetic moment has the best recognition impact. Experimental findings demonstrate that our enhanced RestNet-18 network can efficiently classify metallic items. This provides a new idea for underground metal target identification and classification.

Deep learning fuzzy immersion and invariance control for type-I diabetes.

In this study, a novel approach is proposed for glucose regulation in type-I diabetes patients. Unlike most studies, the glucose-insulin metabolism is considered to be uncertain. A new approach on the basis of the Immersion and Invariance (I&I) theorem is presented to derive the adaptation rules for the unknown parameters. Also, a new deep learned type-II fuzzy logic system (T2FLS) is proposed to compensate the estimation errors and guarantee stability. The suggested T2FLS is tuned by the singular value decomposition (SVD) method and adaptive tuning rules that are extracted from stability investigation. To evaluate the performance, the modified Bergman model (BM) is applied. Besides the dynamic uncertainties, the meal effect on glucose level is also considered. The meal effect is defined as the effect of edibles. Similar to the patient activities, the edibles can also have a major impact on the glucose level. Furthermore, to assess the effect of patient informal activities and the effect of other illnesses, a high random perturbation is applied to glucose-insulin dynamics. The effectiveness of the suggested approach is demonstrated by comparing the simulation results with some other methods. Simulations show that the glucose level is well regulated by the suggested method after a short time. By examination on some patients with various diabetic condition, it is seen that the suggested approach is well effective, and the glucose level of patients lies in the desired range in more than 99% h.

Influence of nanoparticles on freezing inside container equipped with fins.

With loading of different shapes of nanoparticles, the solidification speed can be changed which was scrutinized in current work. Although the nanoparticles dispersion can decline the heat capacity, the conduction mode can be improved with such technique and changing the styles of nano-powders can alter the strength of conduction. The velocity terms were neglected in freezing, thus, the main equations include two equations with unsteady form for scalars of solid fraction and temperature. Grid adaption with position of ice front has been considered in simulations utilizing FEM. The upper sinusoidal and inner rectangular walls maintain cold temperature and freezing starts from these regions. Adding nanomaterial can expedite the process around 15.75% (for m = 4.8) and 29.8% (for m = 8.6). Also, utilizing particles with shapes of blade form can augment the freezing rate around 16.69%. The efficacy of m on freezing process rises around 4% with elevate of concentration of nanoparticles.

Surveillance of Class I Newcastle Disease Virus at Live Bird Markets in China and Identification of Variants with Increased Virulence and Replication Capacity.

In this study, 232 class I Newcastle disease viruses (NDVs) were identified from multiple bird species at nationwide live bird markets (LBMs) from 2017 to 2019 in China. Phylogenetic analysis indicated that all 232 isolates were clustered into genotype 1.1.2 of class I on the basis of the fusion (F) gene sequences, which were distinct from the genotypes identified in other countries. Most of the isolates (212/232) were shown to have the typical F gene molecular characteristics of class I NDVs, while a few (20/232) contained mutations at the site of the conventional start codon of the F gene, which resulted in open reading frames (ORFs) altered in length. The isolates with ACG, CTA, and ATA mutations showed different levels of increased virulence and replication capacity, suggesting that these viruses may be transitional types during the evolution of class I NDVs from avirulent to virulent. Further evaluation of biological characteristics with recombinant viruses obtained by reverse genetics demonstrated that the ATG located at genomic positions 4523 to 4525 was the authentic start codon in the F gene of class I NDV, and the specific ATA mutations which contributed to the expression of F protein on the surface of infected cells were the key determinants of increased replication capacity and virulence. Interestingly, the mutation at the corresponding site of genotype II LaSota of class II had no effects on the virulence and replication capacity in chickens. Our results suggest that the alteration of virulence and replication capacity caused by specific mutations in the F gene could be a specific characteristic of class I NDVs and indicate the possibility of the emergence of virulent NDVs due to the persistent circulation of class I NDVs. IMPORTANCE The available information on the distribution, genetic diversity, evolution, and biological characteristics of class I Newcastle disease viruses (NDVs) in domestic poultry is currently very limited. Here, identification of class I NDVs at nationwide live bird markets (LBMs) in China was performed and representative isolates were characterized. A widespread distribution of genotype 1.1.2 of class I NDVs was found in multiple bird species at LBMs in China. Though most isolates demonstrated typical molecular characteristics of class I NDVs, a few that contained specific mutations at the site of the conventional start codon of the fusion gene with increased virulence and replication capacity were identified for the first time. Our findings indicate that the virulence of class I NDVs could have evolved, and the widespread transmission and circulation of class I NDVs may represent a potential threat for disease outbreaks in poultry.

Phase unwrapping algorithm based on phase edge tracking for dynamic measurement.

Phase unwrapping is an essential procedure for fringe projection profilometry (FPP). To improve measurement efficiency and reduce phase unwrapping errors (PUEs) in dynamic measurement, a phase unwrapping algorithm based on phase edge tracking is proposed, which unwraps the current wrapped phase map with the aid of the previously unwrapped one. The phase edges are accurately tracked and their trajectories are used to divide the phase map into several regions, each of which is unwrapped either temporally or spatially according to its properties. It doesn't require extra patterns for phase unwrapping once the initial unwrapped phase map is obtained, thus significantly increasing the frame rate of the 3D result. Meanwhile, it greatly reduces the PUEs caused by noise amplification and motion-induced misalignment of phase edges. Experiments prove that it is capable of retrieving the absolute phase maps of complex dynamic scenes with high unwrapping accuracy and efficiency.

Nanostructured perovskites for nonvolatile memory devices.

Perovskite materials have driven tremendous advances in constructing electronic devices owing to their low cost, facile synthesis, outstanding electric and optoelectronic properties, flexible dimensionality engineering, and so on. Particularly, emerging nonvolatile memory devices (eNVMs) based on perovskites give birth to numerous traditional paradigm terminators in the fields of storage and computation. Despite significant exploration efforts being devoted to perovskite-based high-density storage and neuromorphic electronic devices, research studies on materials' dimensionality that has dominant effects on perovskite electronics' performances are paid little attention; therefore, a review from the point of view of structural morphologies of perovskites is essential for constructing perovskite-based devices. Here, recent advances of perovskite-based eNVMs (memristors and field-effect-transistors) are reviewed in terms of the dimensionality of perovskite materials and their potentialities in storage or neuromorphic computing. The corresponding material preparation methods, device structures, working mechanisms, and unique features are showcased and evaluated in detail. Furthermore, a broad spectrum of advanced technologies (e.g., hardware-based neural networks, in-sensor computing, logic operation, physical unclonable functions, and true random number generator), which are successfully achieved for perovskite-based electronics, are investigated. It is obvious that this review will provide benchmarks for designing high-quality perovskite-based electronics for application in storage, neuromorphic computing, artificial intelligence, information security, etc.

Desired tracking of delayed quadrotor UAV under model uncertainty and wind disturbance using adaptive super-twisting terminal sliding mode control.

In this study, the fully-actuated dynamic equation of quad-rotor as a type of Unmanned Aerial Vehicles (UAVs) is considered in the existence of input-delay, model uncertainty and wind disturbance. Then, a super-twisting terminal sliding mode control approach is planned with the aim of the finite-time attitude and position tracking of quad-rotor UAV considering input-delay, model uncertainty and wind disturbance. The finite time convergence of the tracking trajectory of quad-rotor is proved by Lyapunov theory concept. When the upper bound of the modeling uncertainty and wind disturbance is supposed to be unknown, an adaptive super-twisting terminal sliding mode control is proposed. Therefore, the unknown bounds of the model uncertainty and wind disturbance affecting the quad-rotor UAV are estimated using the adaptive-tuning control laws. Finally, simulation outcomes and experimental verifications are provided to demonstrate the validation and success of planned control technique.

Dynamic 3D measurement based on orthogonal fringe projection and geometric constraints.

Geometric constraint algorithms can solve phase ambiguity for fringe projection profilometry (FPP). However, they either require multiple cameras or suffer from a small measurement depth range. To overcome these limitations, this Letter proposes an algorithm combining orthogonal fringe projection and geometric constraints. A novel, to the best of our knowledge, scheme is developed to assess the reliabilities of the potential homologous points, which works with depth segmentation to determine the final HPs. With full consideration of lens distortions, the algorithm reconstructs two 3D results from every set of patterns. Experimental results verify that it can effectively and robustly measure discontinuous objects with complex motion over a large depth range.

Medical Image Interpolation Using Recurrent Type-2 Fuzzy Neural Network.

Image interpolation is an essential process for image processing and computer graphics in wide applications to medical imaging. For image interpolation used in medical diagnosis, the two-dimensional (2D) to three-dimensional (3D) transformation can significantly reduce human error, leading to better decisions. This research proposes the type-2 fuzzy neural networks method which is a hybrid of the fuzzy logic and neural networks as well as recurrent type-2 fuzzy neural networks (RT2FNNs) for advancing a novel 2D to 3D strategy. The ability of the proposed methods in the approximation of the function for image interpolation is investigated. The results report that both proposed methods are reliable for medical diagnosis. However, the RT2FNN model outperforms the type-2 fuzzy neural networks model. The average squares error for the recurrent network and the typical network reported 0.016 and 0.025, respectively. On the other hand, the number of fuzzy rules for the recurrent network and the typical network reported 16 and 22, respectively.