Possibility Of Application Research Articles

Video2Haptics: Converting Video Motion to Dynamic Haptic Feedback with Bio-Inspired Event Processing.

In cinematic VR applications, haptic feedback can significantly enhance the sense of reality and immersion for users. The increasing availability of emerging haptic devices opens up possibilities for future cinematic VR applications that allow users to receive haptic feedback while they are watching videos. However, automatically rendering haptic cues from real-time video content, particularly from video motion, is a technically challenging task. In this article, we propose a novel framework called "Video2Haptics" that leverages the emerging bio-inspired event camera to capture event signals as a lightweight representation of video motion. We then propose efficient event-based visual processing methods to estimate force or intensity from video motion in the event domain, rather than the pixel domain. To demonstrate the application of Video2Haptics, we convert the estimated force or intensity to dynamic vibrotactile feedback on emerging haptic gloves, synchronized with the corresponding video motion. As a result, Video2Haptics allows users not only to view the video but also to perceive the video motion concurrently. Our experimental results show that the proposed event-based processing methods for force and intensity estimation are one to two orders of magnitude faster than conventional methods. Our user study results confirm that the proposed Video2Haptics framework can considerably enhance the users' video experience.

Releasing Charge Transport Barriers at Low Bias in Perpendicularly Aligned 2D Halide Perovskite

2D halide perovskites have garnered significant interest due to the great potential in optoelectronic devices. The 2D Perovskites samples are structured as n layers of [MX6]4− octahedral sheets inducing strong quantum confinement effects. Here, we have synthesized a series of 2D (BA)2MAn-1PbnI3n+1 and its 3D counterpart by controlling the particles alignment to the substrate. We have performed a comprehensive study involving electrical transport and photoconductivity effect as a function of the particle's organization: parallel, perpendicular, and disordered orientation to the electrodes. We have observed that for intermediary value of n, the 2D layered particles are aligned perpendicular to the electrode and parallel to the transport electrical current. A distinct and complex S-shape behavior in V-i curves is disclosed, suggesting two electrical resistance regimes. The sharp increase in resistance suggests the presence of electronic barriers for all samples, which is suppressed for columnar arrays of n = 2 and 3 under illumination. We suggest that this linear behavior in the photoconductivity effect is close related to the absence of electrical barriers at the interface of the particles due to perpendicular alignment to the electrodes. The absence of interfaces in a more columnar array facilitates ionic conductivity enhancing photoelectric effect. Our findings reveal distinct differences in charge transport for perpendicularly aligned 2D samples, opening up possibilities for important applications at low bias.

First principle investigation of Sr and Eu doped La2NiMnO6: Structural, Electronic, Optical, Magnetic, and Spintronics properties

This study investigates the effects of substituting La₂NiMnO₆ (LNMO) with Strontium (Sr) and Europium (Eu) dopants at the A-site, focusing on changes in material properties. Utilizing Density Functional Theory (DFT) with Local Density Approximation and Hubbard U Correction (LDA + U), we analyze the significant impact of these dopants on LNMO's optical, magnetic, electrical, and spintronic properties. Sr and Eu doping modifies the electronic structure of LNMO, enhancing its dielectric properties, which is crucial for applications requiring a high dielectric constant. The improved optical conductivity of doped LNMO makes it more efficient in light absorption and photoconductivity, beneficial for optoelectronic devices. Doping also enhances the magnetic properties of LNMO, advantageous for spintronics where spin control is essential. These dual improvements in electrical and magnetic properties suggest that Sr and Eu-doped LNMO are highly effective for energy storage technologies. Additionally, the mechanical properties of doped LNMO show improved resilience and stability, key indicators for the durability and longevity of devices. This comprehensive analysis highlights the potential of Sr and Eu-doped LNMO for advancements in spintronics, optoelectronics, and energy storage. The findings provide valuable insights into tuning material properties through doping, paving the way for synthesizing new multifunctional materials. This study not only enhances our understanding of the impact of dopants in double perovskites but also expands possibilities for innovative applications in high-tech fields, offering the potential for creating versatile materials and future innovations.

Programable shape recovery properties and snap-through instabilities of viscoelastic and shape-memory NS metamaterials

The combination of smart material and mechanical metamaterial has the potential to bring novel properties and additional functionalities, which, however, has been ignored for a long time. This work numerically, experimentally, and analytically examines the negative stiffness (NS) metamaterials based on shape memory polymers (SMPs) with a focus on the tunable and temperature-dependent properties induced by the interaction of material and geometry nonlinearity. A universal discrete model of a series of shape-memory NS metamaterial is developed by separating the bending effect and stretching effect, where the viscoelastic material is described by the generalized Maxwell-Wiechert model, and an SMP constitutive model is proposed based on multiplicative decomposition of the deformation gradient. The mechanical and shape-memory properties influenced by temperature, geometry, material and loading rate are experimentally observed through relaxation tests and cyclic compression tests. Then the discrete model and finite element analysis (FEA) is adopted to examine the novel mechanical responses in a wide range of parameters. Results show that viscoelasticity and loading rate play a significant role in mechanical responses, and tunable load-capacity and stability can be achieved for shape-memory metamaterial. A phase diagram of different stabilities is constructed showing strictly monotonic, S-shaped, and snapping responses. Additionally, repeated compression and recovery performance proves the possibility of reusable applications. This work provides new opportunities for functional metamaterial to obtain programmable shape recovery and mechanical responses.

Multimodal fusion-powered English speaking robot

IntroductionSpeech recognition and multimodal learning are two critical areas in machine learning. Current multimodal speech recognition systems often encounter challenges such as high computational demands and model complexity.MethodsTo overcome these issues, we propose a novel framework-EnglishAL-Net, a Multimodal Fusion-powered English Speaking Robot. This framework leverages the ALBEF model, optimizing it for real-time speech and multimodal interaction, and incorporates a newly designed text and image editor to fuse visual and textual information. The robot processes dynamic spoken input through the integration of Neural Machine Translation (NMT), enhancing its ability to understand and respond to spoken language.Results and discussionIn the experimental section, we constructed a dataset containing various scenarios and oral instructions for testing. The results show that compared to traditional unimodal processing methods, our model significantly improves both language understanding accuracy and response time. This research not only enhances the performance of multimodal interaction in robots but also opens up new possibilities for applications of robotic technology in education, rescue, customer service, and other fields, holding significant theoretical and practical value.

Neuroendocrine tumors of the stomach and pancreas. Possibilities of application of radiomics. Problems and solutions.

Background. Radiomics is promising tool contributing to the diagnosis and treatment of neuroendocrine neoplasms of various localizations. This method is often used for differential diagnosis of gastrointestinal neoplasms from other neoplasms of this localization.Aim. To demonstrate difficulties in imaging of gastrointestinal neuroendocrine tumors, as well as possibilities of radiomics in differential diagnosis.Materials and methods. Data of 12 patients with morphologically confirmed gastric neoplasms (6 - with neuroendocrine tumor, 6 - with adenocarcinoma) and data of 22 patients with morphologically confirmed pancreatic neoplasms (11 - with neuroendocrine tumor, 11 - with adenocarcinoma) who underwent CT examination of abdominal cavity organs with intravenous contrast before treatment at the Russian Scientific Center of Roentgenoradiology were used in this study. Radiomic features were extracted from the area of manually segmented gastric and pancreatic tumors in the native phase of the CT scans. Processing of the features and statistical analysis were performed using Microsoft Office Excel and free open-source software development environment for the R programming language - R-Studio.Results. Typical and atypical imaging features of gastric and pancreatic neuroendocrine tumors, peculiarities of contrast, localization and structure of neoplasms were demonstrated on the examples of CT scans. There were 15 radiomic features that were statistically significantly different in patients with neuroendocrine tumors and gastric adenocarcinomas. In the group of patients with neuroendocrine tumors and pancreatic adenocarcinomas, statistically significant differences were observed in 14 radiomic features.Conclusion. Neuroendocrine tumors of the stomach and pancreas are rare neoplasms that are not only clinically silent in most cases, but also difficult to visualize due to their small size and poor contrast characteristics. Texture analysis is a promising tool for the differential diagnosis of gastrointestinal neoplasms from other neoplasms of this localization, especially considering the difficulties of biopsy.

Exploring the Therapeutic Potential of Theobroma cacao L.: Insights from In Vitro, In Vivo, and Nanoparticle Studies on Anti-Inflammatory and Anticancer Effects

Theobroma cacao L., commonly known as cocoa, has been an integral part of human culture and diet for thousands of years. However, recent scientific research has highlighted its potential therapeutic properties, particularly in the treatment of cancer and inflammatory diseases. This comprehensive review analyzes the anti-inflammatory and anticancer effects of Theobroma cacao extracts combined with nanoparticles using in vitro and in vivo studies. Its diverse biological activity can be attributed to its rich phytochemical profile, including polyphenols, flavonoids, and alkaloids. In vitro studies have found that cocoa extracts, alone or in combination with nanoparticles, inhibit cancer cell proliferation, induce apoptosis and modulate key signaling pathways in various cancer cell lines. The extracts have also been found to reduce tumor growth and enhance the efficacy of conventional chemotherapeutic agents, potentially reducing their side effects, in vivo. Its anti-inflammatory properties are based on its ability to modulate inflammatory mediators, inhibit NF-κB signaling, and regulate macrophage polarization. These effects have been observed in both cellular and animal models of inflammation. This review opens up new possibilities for future research and therapeutic applications, highlighting the potential of Theobroma cacao as a valuable complementary approach in the treatment and prevention of cancer and inflammatory diseases.

Digital Technologies in the National Cadastre System of Uzbekistan: Issues of Legal Regulation

Objective: to critically analyze the state of national legislation of Uzbekistan in terms of legal regulation of digitalization and the use of artificial intelligence in the cadastral sphere.Methods: the research is based on such methods of scientific cognition as formal-legal and comparative-legal analysis, induction and deduction.Results: the provisions that regulate digitalization and the use of artificial intelligence in the cadastral sphere were analyzed, legal gaps were identified. It was determined that the practical application of artificial intelligence technologies outpaces its legal regulation. The shortcomings of legal regulation in this sphere were noted (lacking legal definition of the legal status of artificial intelligence in the national legislation; regulation of business entities’ participation in the management of artificial intelligence, etc.). The said shortcomings hinder its full application and harmonization with traditional sources of cadastral information. The author substantiated the need for universal digitization of the national cadastre and predicts the possibility of wider application of artificial intelligence in the natural-resource cadastral system. It is argued that the existing system in its current state may lead to wrong decisions and cadastral errors, hence, it is necessary to improve the legal regulation of cadastre.Scientific novelty: for the first time the results of the national cadastre digitization were assessed. Forecasts were given about the possibility of using artificial intelligence in this area, subject to further improvement of legal regulation. The latter is fundamentally important for reforming the cadastral system, since the technological basis of this system does not fully meet the needs of the digital economy.Practical significance: it is due to the lack of legal regulation of the artificial intelligence concept and legal status in the national legislation, as well as a unified approach to the cadastral system digitalization. Modern technologies are actively used in practice, but lack a sufficient legal basis. The main conclusions, proposals and recommendations of the study can be a basis for further improvement of the legal framework of Uzbekistan in terms of the application of artificial intelligence technologies.

Advances in the study of DNA-metal complexes in biomedical applications

DNA-metal composites are emerging as emerging materials for biomedical applications due to their unique DNA and metal nanoparticle properties. DNA is a multifunctional macromolecule that enables targeted drug delivery through aptamer selection and gene silencing through nuclease activity. These properties make it highly selective and targeted in therapy. While conventional chemotherapy faces significant drawbacks, including poor targeting and severe side effects, DNA-metal composites with their excellent targeting and therapeutic efficiencies can significantly enhance therapeutic efficacy. This paper first describes the structural properties, synthesis methods of DNA-metal composites and their therapeutic applications. The combination of DNA and metal nanoparticles provides a novel approach to improve the effectiveness of anticancer and antimicrobial therapies, addressing key limitations in current therapeutic strategies. Innovative applications of DNA-metal composites in the anticancer and antimicrobial fields are then explored, highlighting their multiple synthesis and application possibilities through interactions with metal ions or particles. DNA-metal composites address several of the limitations of conventional chemotherapeutic and antimicrobial drugs, providing better targeting and resolving the issue of drug resistance. DNA-metal composites in biomedicine show DNA-metal composites show great promise in the biomedical field, and future research will continue to promote their application and development in clinical therapy.

Rapid preparation and mechanism investigation of covalent organic framework membranes by 3D printing based on electrostatic spraying

Covalent organic framework (COF) has great advantages in the field of membrane separation, but the efficient and convenient preparation of COF membranes is still a challenge. Herein, we propose a novel method with green solvent for the preparation of COF membranes based on electrostatic spraying, which can be successfully prepared within 10 min, forming the TpPa COF with a pore size of 1.8 nm. Molecular dynamics simulations demonstrate that electrostatic spraying enhances the reaction rate by lowering the energy barrier and increasing the movement of reacting monomers. Atomization of the solvent in electrostatic spraying causes the rapid volatilization of the solvent, resulting in supersaturation of the generated TpPa, precipitation and crystallization, and promotes the formation of TpPa COF structure. The optimal COF membrane for the separation of Congo red wastewater is prepared by adjusting the voltage, concentration, and spraying time, with selectivity of Na2SO4 for Congo Red attaining 118 and water permeance reaching 70.2 L m-2 h-1 bar-1. This work not only elaborates the membrane preparation mechanism in the electrostatic spraying process, but also greatly shortens the time of COF membrane preparation compared to the traditional method (2–3 days), which contributes to the possibility for practical application of COF membranes.

Theoretical assessment of a novel NaXH3 and KXH3 (X = Tc, Ru and Rh) perovskite hydrides for hydrogen storage

Utilizing the Cambridge Serial Total Energy Package (CASTEP), we examined various properties of the compounds NaXH3 and KXH3 (where X represents Tc, Ru and Rh). These properties include structural, thermodynamic, hydrogen storage, optical, mechanical, and electronic. The studied compounds exhibit stables cubic crystal structures with lattice constants between 3.53 Å and 3.77 Å. Their mechanical and thermodynamic stability is confirmed by their elastic constants, phonon dispersion, and negative formation energies. The gravimetric hydrogen storage capacities are 2.44, 2.38, 2.35, 2.16, 2.11 and 2.08 wt percent for NaTcH3, NaRuH3, NaRhH3, KTcH3, KRuH3 and KRhH3, respectively. Notably, KTcH3 has the most practical hydrogen desorption temperature of 365 K. Electronic properties reveal a consistent metallic behavior across all compounds, opening possibilities for applications beyond hydrogen storage. Their calculated optical properties, including conductivity, absorption, and refractive index, suggest that KTcH3 may be suitable for use in optoelectronic devices and solar cells. Mechanical analysis highlights NaRuH3 as the hardest material, based on the investigated elastic moduli. These findings underscore the potential of sodium- and potassium-based perovskite hydrides not only for hydrogen storage but also for advanced energy systems, electronics, and optics.

Electropolymerization of a New Diketopyrrollopyrrole Derivative into Inherent Chiral Polymer Films

Electropolymerization is a convenient way to obtain conducting polymers (CPs) directly adhered to an electrode surface. CPs are well-known for their various application fields in photovoltaic cells, chemical sensors, and electronics. By implementing chirality into a CP, the application possibilities will spread further onto chiral sensors or optoelectronics. In this work, we introduce a new inherently chiral polymer based on a macrocyclic 3,4-ethylenedioxythiophene-diketopyrrolopyrrole-3,4-ethylenedioxythiophene triad (EDOT-DPP-EDOT) fused by 1,4-phenylene groups, which was prepared via oxidative electropolymerization directly on the electrode surface. The investigation of the chiroptical properties was performed by circular dichroism spectroscopy in the solid state. The enantiomeric pure polymer films obtained showed dissymmetry factors of up to −2.71 × 10−4, whereby linear dichroism contributions can be widely excluded.

Choice of an environmentally friendly method of producing cellulose from flax biomass (<i>Linum usitatissimum linaceae</i>) grown in different regions of the Volga region

The experimental assessment of prospects of receiving cellulose material on the basis of use of vegetable raw materials – straw of a flax-dolgunetc is carried out. Results of researches showed a possibility of application for extraction of cellulose of the water solutions containing hydrogen peroxide, hydroxide and sodium bisulfite in the conditions of high-temperature influence within 2 hours with receiving the technical cellulose suitable for production of cardboard and packaging materials. For purification of technical cellulose, it was carried out additional processing by solution of hydrogen peroxide and acetic acid in similar parametrical conditions that allowed to receive a target product with high extent of maintenance of the main substance, at insignificant amount of the accompanying impurity. The presented results show a possibility of the solution of questions of environmental safety at the organization of process of receiving cellulose on the basis of renewable vegetable raw materials with use of the chemical reagents which are not containing compounds of sulfur.

Contemporary and prospective use of azathioprine (AZA) in viral, rheumatic, and dermatological disorders: a review of pharmacogenomic and nanotechnology applications.

Azathioprine (AZA) has been extensively used for immunomodulatory effects in autoimmune disorders and transplantation. This article is proposed to review the contemporary and prospective use of AZA in viral, rheumatic, and dermatological disorders. The primary objective is to draw attention to possible developments in regards to AZA application in recent years, with an emphasis on the use of pharmacogenomics and nanotechnology to improve its efficacy in practice. This study reveals that AZA, having the active metabolites 6-mercaptopurine (6-MP) and 6-thioguanine (6-TG), may be useful in the treatment of systemic lupus erythematosus (SLE), pemphigus vulgaris, and psoriasis. Pharmacogenomic testing of thiopurine methyltransferase (TPMT) and Nudix hydrolase 15 (NUDT15) genotypes minimizes the occurrence of myelosuppression. Furthermore, new formulations of AZA using biocompatible polymers and nanoparticles for drug delivery were reported to improve its efficacy and lower systemic toxicity. This paper aims to establish the multifunctional nature of AZA in modern medicine, thus emphasizing its potential for other applications. Through the combination of pharmacogenomic analysis along with nanotechnology application, AZA makes the promise of enhancing patients' treatment efficacy and extending the stock of medical information available. These advancements offer new possibilities for application of precision medicine and improvements in the use of AZA therapy.

An evaluate of ORR catalysts in fuel cell: Current Advances and Applications

Abstract. This thesis presents a comprehensive review of fuel cell technology, with a specific emphasis at the latest trends and programs of oxygen reduction response (ORR) catalysts. The examine starts offevolved with the aid of using evaluating the benefits and drawbacks of hydrogen fuel cells with different alternative power sources, which include sun power and secondary batteries, highlighting the ability of fuel cells to update traditional inner combustion engines because of their high power density and environmental benefits. The thesis then explores the essential running ideas of fuel cells and examines their operational mechanisms in numerous chemical environments. In the context of ORR catalysts, the assessment evaluates quite a number catalysts, which include precious metal catalysts, non-precious metal catalysts, and single-atom catalysts, that specialize in techniques to decorate their activity and stability. Finally, the thesis discusses the demanding situations and destiny possibilities for the industrial application of ORR catalysts in hydrogen fuel cells.

Enhancement of the photocatalytic efficiency of ZnO utilizing luminescent Y2O3 particles

Abstract This study explored the superior photocatalytic performance of the nanoscale zinc oxide-yttrium oxide (ZnO-Y2O3) based composite over ZnO nanoparticles (NPs). We investigated this by following the photocatalytic degradation efficiency of methylene blue (MB). The sol–gel synthesized Y2O3 and ZnO NPs were characterized by x-ray diffraction (XRD) and scanning electron microscopy (SEM) techniques. The spectral behaviour and photocatalytic efficiency of the proposed composite were investigated by UV–vis spectrophotometry, steady-state and time-resolved photoluminescence (PL) measurements, respectively. The results indicated the successful formation of Y2O3 and ZnO nanoparticles with desirable structural properties. The photocatalytic degradation efficiency of the MB solution was evaluated for different concentrations of the counterparts of the ZnO-Y2O3 composite. In particular, the ZnO-5Y2O3-based composite showed superior photocatalytic activity after 150 min of UV irradiation, achieving 98.4% degradation of the MB solution compared to the 77% degradation achieved by pure ZnO. Although Y2O3 alone does not exhibit photocatalytic activity, its combination with ZnO significantly enhances the photocatalytic performance of ZnO. This improvement was attributed to the luminescence properties of Y2O3. By elucidating this unique mechanism, the performance of photocatalytic materials can be significantly enhanced. In our study, the improvement of the photodegradation rate constant (kapp) of ZnO from 0.009 min−1 to 0.0242 min−1 demonstrated the promising photocatalytic efficiency of the ZnO-5Y2O3 based composite, opening up exciting possibilities for further applications in environmental remediation and other fields.

Advancements in Data Augmentation and Transfer Learning: A Comprehensive Survey to Address Data Scarcity Challenges

Abstract: Deep Learning (DL) models have demonstrated remarkable proficiency in image classification and recognition tasks, surpassing human capabilities. The observed enhancement in performance can be attributed to the utilization of extensive datasets. Nevertheless, DL models have huge data requirements. Widening the learning capability of such models from limited samples even today remains a challenge, given the intrinsic constraints of small datasets. The trifecta of challenges, encompassing limited labeled datasets, privacy, poor generalization performance, and the costliness of annotations, further compounds the difficulty in achieving robust model performance. Overcoming the challenge of expanding the learning capabilities of Deep Learning models with limited sample sizes remains a pressing concern even today. To address this critical issue, our study conducts a meticulous examination of established methodologies, such as Data Augmentation and Transfer Learning, which offer promising solutions to data scarcity dilemmas. Data Augmentation, a powerful technique, amplifies the size of small datasets through a diverse array of strategies. These encompass geometric transformations, kernel filter manipulations, neural style transfer amalgamation, random erasing, Generative Adversarial Networks, augmentations in feature space, and adversarial and meta- learning training paradigms. : Furthermore, Transfer Learning emerges as a crucial tool, leveraging pre-trained models to facilitate knowledge transfer between models or enabling the retraining of models on analogous datasets. Through our comprehensive investigation, we provide profound insights into how the synergistic application of these two techniques can significantly enhance the performance of classification tasks, effectively magnifying scarce datasets. This augmentation in data availability not only addresses the immediate challenges posed by limited datasets but also unlocks the full potential of working with Big Data in a new era of possibilities in DL applications.

Advancing Photodynamic Therapy with Nano-Conjugated Hypocrellin: Mechanisms and Clinical Applications.

Hypocrellin-based photodynamic therapy (PDT) is developing as a viable cancer therapeutic option, especially when enhanced by nanoconjugation. This review investigates the methods by which nano-conjugated hypocrellin enhances therapeutic efficacy and precision when targeting cancer cells. These nanoconjugates encapsulate or covalently bind hypocrellin photosensitizers (PSs), allowing them to accumulate preferentially in malignancies. When activated by light, the nanoconjugates produce singlet oxygen and other reactive oxygen species (ROS), resulting in oxidative stress that selectively destroys cancer cells while protecting healthy tissues. We look at how they can be used to treat a variety of cancers. Clinical and preclinical studies show that they have advantages such as increased water solubility, improved tumor penetration, longer circulation times, and tailored delivery, all of which contribute to fewer off-target effects and overall toxicity. Ongoing research focuses on improving these nanoconjugates for better tumor targeting, drug release kinetics, and overcoming biological obstacles. Furthermore, the incorporation of developing technologies such as stimuli-responsive nanocarriers and combination therapies opens exciting opportunities for enhancing hypocrellin-based PDT. In conclusion, the combination of hypocrellin and nanotechnology constitutes a significant approach to cancer treatment, increasing the efficacy and safety of PDT. Future research will seek to create conjugates including hypocrellin, herceptin, and gold nanoparticles to induce apoptosis in human breast cancer cells in vitro, opening possibilities for therapeutic applications.

Delay-aware resource allocation for partial computation offloading in mobile edge cloud computing

Mobile Edge Cloud Computing (MECC), as a promising partial computing offloading solution, has provided new possibilities for compute-intensive and delay-sensitive mobile applications, which can simultaneously leverage edge computing and cloud services. However, designing resource allocation strategies for MECC faces an extremely challenging problem of simultaneously satisfying the end-to-end latency requirements and minimum resource allocation of multiple mobile applications. To address this issue, we comprehensively consider the randomness of computing request arrivals, service time, and dynamic computing resources. We model the MECC network as a two-level tandem queue consisting of two sequential computing processing queues, each with multiple servers. We apply a deep reinforcement learning algorithm called Deep Deterministic Policy Gradient (DDPG) to learn the computing speed adjustment strategy for the tandem queue. This strategy ensures the end-to-end latency requirements of multiple mobile applications while preventing overuse of the total computing resources of edge servers and cloud servers. Numerous simulation experiments demonstrate that our approach is significantly superior to other methods in dynamic network environments.

Unlocking the potential of copper-reinforced fibre metal laminates: The influence of stacking sequences

Fibre-Metal Laminates (FMLs) are a class of advanced hybrid materials consisting of a metal layer and fibre-reinforced polymers that give an excellent combination of mechanical properties. The present study deals with the development and characterization of copper-based FMLs developed through the hand layup method by considering multiple stacking sequences for improved mechanical properties and by incorporating carbon nanotubes. Extensive tensile, flexural, fatigue, and wear tests were carried out on the FMLs. SEM analysis was also done to investigate the fracture. According to the test results, in the Type III stacking sequence where optimum 0.5% CNT content is used, the best overall tensile strength together with good flexibility, fatigue resistance, and wear resistance could be obtained. The Type III stacking sequence exhibited a maximum tensile strength of 117.85 MPa, flexural strength of 168.0 MPa, and fatigue life of 22,00,000 cycles at 50 MPa stress amplitude. Further SEM analysis has shown that enhancement in microstructural integrity actually occurred with the inclusion of CNT, especially in fiber-matrix bonding improvement and reduction of voids. The results obtained in this work indicate the suitable possibility for application in high-performance aerospace and automotive industries of copper-based FMLs. This study highlights optimization issues related to both material composition and stacking sequence toward the attainment of superior mechanical properties in FMLs.