Applications In Industry Research Articles

Comprehensive Analysis of Phycoerythrin 545 Stability and the Apoptotic Impact of Its Degradation Products on HT29 Cells.

This study investigates the properties and potential applications of phycoerythrin 545, a naturally occurring light-harvesting pigment protein from Rhodomonas salina. Phycoerythrin 545, characterized by its bright red color and maximum absorption wavelength at 545 nm, was extracted using freeze-thawing methods, further purified, and analyzed using chromatographic, spectroscopic techniques, and sodium dodecyl sulfate-polyacrylamide gel electrophoresis. Phycoerythrin 545 consists of two subunits, primarily α and β, but lacks the γ subunit, and is stable at 4 °C within a pH range of 3-10. To further characterize it, its susceptibility to degradation by trypsin was assessed. The biological activity of phycoerythrin 545 and its degradation products were investigated in HT29 human colon cancer cells. The results showed that the degradation products, particularly those within 3-10 kDa, significantly decreased the viability of HT29 cells by inducing apoptosis. Mechanistic studies indicated these effects were mediated through the activation of mitogen-activated protein kinase (MAPK)/extracellular signal-regulated kinases and MAPK/c-Jun N-terminal Kinase signaling pathways and involved the regulation of key apoptotic proteins such as p53, Bim, Bad, Bak, and Bax, leading to the activation of the Caspase-3 apoptotic pathway. These findings contribute to understanding the structural and functional properties of phycoerythrin 545, laying a foundation for its exploration in food industry applications and cancer therapy supplementation.

Efficient and low-drift point-cloud mapping method for GNSS weak and obstructed areas

Abstract Road modelling based on point-cloud data often encounters signal-rejection areas, where the absence of absolute position constraints leads to rapid error accumulation in the constructed map. To mitigate error growth, the manual setting of control points is necessary, which compromises automation in the mapping process and causes a challenging trade-off between efficiency and accuracy. To address these issues, in the present study, we designed a fast and efficient map-construction method that provides absolute pose constraints for simultaneous localisation and mapping (SLAM) based on the bidirectional filter position of global navigation satellite system (GNSS)/inertial measurement unit (IMU) integrated navigation. First, we designed an absolute position constraint factor (BF_Factor) updated at each epoch according to the reference-position difference and time-delay error, which can significantly mitigate error divergence during mobile mapping in a GNSS-rejected region. An adaptive noise model was then constructed for the above factors so that the weights of different factors could be adjusted automatically to achieve a more accurate mapping result. Finally, we conducted experimental tests on the proposed mapping approach in two scenarios that included multiple GNSS-rejected areas. Even when several hundred metres of GNSS signal-obstructed areas were present in the scene, the relocation results indicated that the three-dimensional position error obtained using point-cloud maps was <0.15 m and the pitch/roll error was better than 2°. Compared with Fastlio2 and Fastlio2-SC (Fastlio2 with Scan Context), the proposed method had a better mapping effect and higher mapping accuracy in the aforementioned scenarios. The method proposed in this paper can reduce the human cost in traditional modeling and can provide more rapid and accurate map for related industry applications. The method proposed in this paper can reduce the human cost in traditional modeling and can provide more rapid and accurate map for related industry applications.

An Integration of UPLC-Q-TOF-MS, GC-MS, Electronic Nose, Electronic Tongue, and Molecular Docking for the Study of the Chemical Properties and Flavor Profiles of Moringa oleifera Leaves

Moringa oleifera leaves (MOLs) have gained significant attention due to their nutritional and biological activity. Therefore, this study aimed to examine its flavor characteristics and underlying compositions. In this study, we used ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS), gas chromatography-mass spectrometry (GC-MS), electronic nose, electronic tongue, and molecular docking to comprehensively investigate the chemical properties and flavor profiles of MOLs. UPLC-Q-TOF-MS and GC-MS were instrumental in identifying the 20 non-volatile and 19 volatile constituents of MOLs, respectively. The electronic nose and electronic tongue systems provided an objective evaluation of the sweet, bitter, and spicy attributes and flavor characteristics of MOLs. Concurrently, molecular docking was employed to elucidate the material basis of flavor profiles. It revealed that glucosinolates and flavonoids are probably the key components for the bitter taste of MOLs. The sweet taste may be attributed to glucosinolates and flavonoids. The spicy scent appears to be linked to the presence of glucosinolates and alkaloids. The integration of these techniques confers a thorough understanding of the chemical composition and sensory properties of MOLs. These findings have significant implications for innovative applications in the food industry as well as pharmaceuticals and agriculture sectors; furthermore, they contribute towards enhancing the perception of Moringa oleifera as a valuable natural resource.

Odour characterisation of recycled HDPE in different washing and processing processes

The waste of polymeric materials in our society is increasing year after year, generating a serious pollution problem. One way to deal with this waste problem is to recycle and reuse these materials. This process of recovery of used plastic materials aims to minimise their impact on the environment and reduce the energy consumption required for the generation of new consumer products. Recycling companies that recover these plastic materials must take into account some aspects such as transparency and colour, cleanliness, size, odour and sorting. One of the major disadvantages in accepting these recycled materials in the production processes is their odour, which in some cases causes the rejection of materials with comparable mechanical characteristics. High-density polyethylene, HDPE, is one of the polymeric wastes generated in the packaging industry. The aim of this work is to eliminate the bad odour of HDPE from waste collection plants for application in the recovery and reuse industry. HDPE supplied by a recycling company was washed, characterised and processed, and the odour was analysed by gas chromatography at each stage and by olfactory panel. In view of the results, it was observed that the washing processes managed to reduce the odour. Likewise, the processing of this waste by extrusion and injection managed to further reduce this effect, even eliminating some of the components responsible for odour by treating the samples with acetone and then extruding and injecting these samples. These results have a direct application in the packaging industry with significant shares of recycled material.

Multi-Period Optimal Transmission Switching with Voltage Stability and Security Constraints by the Minimum Number of Actions

Due to the ever-growing load demand and the deregulation of the electricity market, power systems often run near the stability boundaries, which deteriorates system voltage stability and raises voltage issues for the stable operations of power systems. Transmission switching (TS) has been applied to improve economic benefits and security operations for many applications. In this paper, a multi-period voltage stability-constrained problem (MP-VSTS) is established, intending to improve voltage security and the stability of a power system. Considering the online application of transmission switching, the minimum number of switching actions is taken as the objective function of the proposed MP-VSTS problem, which extends the TS application for real industries. The proposed model provides the switching lines for the upcoming period and the state of power systems for several successive periods. To overcome the solving difficulties of the proposed model, a two-stage approach is presented, which balances speed and accuracy. Numerical studies on the IEEE 118- and 662-bus power systems have demonstrated the proposed approach’s performance.

REVOLUTIONIZING SEMICONDUCTOR DESIGN AND MANUFACTURING WITH AI

The semiconductor industry plays a vital role in driving technological advancements, and the incorporation of AI (Artificial Intelligence) can greatly enhance its efficiency and productivity. Through optimizing material usage and reducing defects, AI can significantly reduce costs and enhance production efficiency and product quality. However, despite the increasing interest in AI applications in the semiconductor industry, comprehensive reviews are lacking to systematically analyze existing research and identify the challenges and opportunities in this field. This review aims to bridge this gap by providing a thorough overview of AI-driven techniques in optimizing semiconductor manufacturing and offering valuable insights for future research directions. The integration of Artificial Intelligence (AI) into chip design marks a transformative phase for the semiconductor industry. Traditional design methodologies, often labor-intensive and time-consuming, are increasingly constrained by human expertise and iterative processes. Generative AI, utilizing advanced machine learning models such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), offers innovative approaches to automate and optimize various stages of chip design. This paper examines how generative AI can revolutionize chip design by automating complex tasks, including architecture exploration, circuit optimization, and layout generation. Through case studies, we demonstrate significant improvements in design efficiency, performance optimization, and reduced time-to-market. Additionally, we address challenges such as data availability, model interpretability, and the integration of AI-generated designs into existing verification workflows. The findings highlight the potential of generative AI to enhance design capabilities, reduce development costs, and accelerate innovation in semiconductor technology.

SYNTHESIS OF N‐SULFOPROPYLATED HYPERBRANCHED POLYETHYLENEIMINE WITH ENHANCED BIOCOMPATIBILITY AND ANTIMICROBIAL ACTIVITY

Hyperbranched polyethyleneimine having 25,000Da molecular weight was functionalized by a simple sulfopropylation reaction, affording a novel N‐sulfopropylated PEI derivative (PEI‐SO3‐). The successful introduction of N‐sulfopropyl and sulfobetaine groups to the amino groups of PEI was spectroscopically confirmed. Furthermore, the antibacterial and anti‐cyanobacterial activity of PEI‐SO3‐ in comparison to the parent PEI were investigated on two type heterotrophic bacteria, i.e., Gram (‐) Escherichia coli and Gram (+) Staphylococcus Aureus bacteria, and one type of autotrophic cyanobacterium, i.e. Synechococcus sp. PCC 7942. Both PEI‐SO3‐ and PEI showed an enhanced, concentration‐dependent antibacterial and anti‐cyanobacterial activity against the tested bacteria strains, with PEI‐SO3‐ exhibiting higher activity than the parent PEI, signifying that the introduction of the sulfopropyl and sulfobetaine groups to the PEI amino groups enhanced the antibacterial anti‐cyanobacterial properties of PEI. In the case of cyanobacteria, PEI‐SO3‐ was found to affect the integrity of the photosynthetic system by the inhibition of Photosystem‐II electron transport activity. Cytocompatibility and hemocompatibility studies revealed that PEI‐SO3‐ exhibits high biocompatibility, suggesting that PEI‐SO3‐ could be considered as an attractive antibacterial and anti‐cyanobacterial candidate for various applications in the disinfection industry and also against the harmful cyanobacterial blooms.

Heat transfer mechanism of superabsorbent polymers phase change energy storage cold-formed steel wall under fire

The superabsorbent polymer phase change materials (SAP materials) exhibit exceptional water absorption and retention properties, offering substantial potential for fire protection in steel structures, thereby reducing the need for sheathing boards and fire-retardant coatings, while minimizing energy consumption. Understanding the heat-mass exchange theory of SAP materials is crucial for enhancing their application in the building industry. This study develops a comprehensive theoretical model for heat-mass exchange in cold-formed steel (CFS) walls incorporating SAP materials. An equilibrium phase change model and a simplified thermal radiation model were developed to capture the water loss and dynamic heat exchange processes in SAP materials. These models were integrated into a transient fluid-solid-thermal coupling model using Ansys Fluent and User Defined Functions. In the case study, the newly developed numerical models can accurately predict temperature field changes in CFS walls with SAP materials, demonstrating the heat transfer mechanisms of hot and cold flanges at different temperature rise stages. The developed numerical model was used to investigate the effects of factors such as the moisture content of the SAP material, web depth, and flange width on the fire resistance of the CFS wall. Results indicated that increasing the wall stud height improves fire resistance, while increasing flange width has little effect on hot flange temperature. These findings provide a theoretical foundation and practical guidelines for the application of SAP phase change insulation materials in the building industry, promoting energy reduction and supporting sustainable construction practices.

Elementary steps of catalytic reactions occurring on metallic alloy nanoparticles

Catalytic reactions occurring in an adsorbed overlayer on metallic alloy nanoparticles are of high interest in the context of applications in the chemical industry. The understanding of the corresponding kinetics is, however, still limited. One of the reasons of this state of the art is the interplay between adsorption and adsorbate‐influenced segregation of metal atoms inside alloy nanoparticles. I scrutinize this interplay by using a generic field model of segregation and the mean‐field approximation in order to describe adsorption, desorption, and elementary catalytic reactions. Under steady‐state conditions, the segregation is demonstrated to be manifested in the change of the dependence of the activation energies of desorption or elementary reactions on coverage, and the sign of this change is positive. The effect of this change on the apparent reaction orders is briefly discussed as well.

Deep neural network based distribution system state estimation using hyperparameter optimization

In the past decade, distribution system state estimation has become a crucial topic in power system research due to the increasing importance of distribution networks amidst the decline of centralized energy production. This paper addresses a gap in the literature regarding the application of modern hyperparameter optimization techniques in low-voltage distribution system state estimation using deep neural networks. In particular, it demonstrates the use of the Tree-structured Parzen Estimator algorithm, which is a Bayesian hyperparameter optimization method, for distribution system state estimation on real Hungarian low-voltage networks. The study uses data from four real-life low-voltage supply areas in Hungary, which were modeled to address the challenges in obtaining network information. Compared to traditional methods like the weighted least squares method, the Tree-structured Parzen Estimator algorithm significantly improves the accuracy of the voltage amplitude and angle estimations, reducing the relative error by 14–73%. Additionally, it is shown that TPE outperforms simpler methods like Random Search in hyperparameter optimization. The results also reveal connections between the distribution system size and optimal hyperparameters, such as batch size, learning rate, and hidden layer configuration. The proposed non-iterative algorithm, combined with the parallel computation capabilities of deep neural networks utilizing GPU, resulted in four orders of magnitude improvement in runtime. These advancements make the proposed approach a valuable tool for renewable energy integration planning and real-time monitoring, highlighting its potential for practical applications in the power industry.

Lilium brownii/Baihe as Nutraceuticals: Insights into Its Composition and Therapeutic Properties.

Nutraceuticals are compounds or components in food that offer health benefits. They can be incorporated into food to make it functional or used as supplements or medicine. Lilium brownii/Baihe is one of the classic nutraceuticals. The chemical composition of Lilium is complex and has a variety of pharmacological effects. Moreover, the compound preparation based on Lilium has been used in the treatment of respiratory diseases in traditional Chinese medicine. In addition, Lanzhou lily has become food on the dinner table. Therefore, Lilium brownii/Baihe is a nutraceutical with a long history. Based on the current understanding of Lilium, this review provides an in-depth discussion of the bioactive components and pharmacological effects of Lilium. This is important to provide theoretical reference for the in-depth study of Lilium as well as its development and application in medicine, food, and other industries.

Nanoemulgel: A Comprehensive Review of Formulation Strategies, Characterization, Patents and Applications

<p>Background: Delivering hydrophobic or poorly soluble drugs has become increasingly challenging, with issues like stability and bioavailability complicating the process. Among various strategies devised to address these problems, nanoemulgels have proven effective. Nanoemulgels combine a gel base and an emulsion at the nanoscale, making them excellent for drug delivery. The nanoemulsion component protects the active ingredient from degradative reactions like hydrolysis and enzymatic degradation. Meanwhile, the gel base enhances the emulsion's thermodynamic stability by increasing the viscosity of the aqueous phase and reducing surface and interfacial tension. </p><p> Objective: The primary objective of this review was to explore nanoemulgels as a drug delivery system in the pharmaceutical industry. It delves into the advantages and applications of nanoemulgels in various medical fields, compares them with conventional emulgel, and examines formulation strategies, preparation methods, patent trends, future prospects, and evaluation methods in detail. </p><p> Method: An exhaustive literature survey was conducted keeping in view the various aspects of nanoemulgel. Information from various resources, such as books, review articles, scientific reports, research articles, and patents, were searched, read, analyzed, and summarized. </p><p> Conclusion: This review article thoroughly examines nanoemulgels, discussing their formulation strategies, characterization techniques, and applications in various fields. It highlights their benefits, such as enhanced drug solubility, controlled release, improved stability, and targeted delivery. The article also covers patents related to nanoemulgel technology and explores its future prospects, emphasizing potential applications in pharmaceuticals, cosmetics, dermatology, and other industries.</p>

Process mining-enhanced quality management in food processing industries

PurposeThe purpose of this study is to present a methodical procedure on how to prepare event logs and analyse them through process mining, statistics and visualisations. The aim is to derive roots and patterns of quality deviations and non-conforming finished products as well as best practice facilitating employee training in the food processing industry. Thereby, a key focus is on recognising tacit knowledge hidden in event logs to improve quality processes.Design/methodology/approachThis study applied process mining to detect root causes of quality deviations in operational process of food production. In addition, a data-ecosystem was developed which illustrates a continuous improvement feedback loop and serves as a role model for other applications in the food processing industry. The approach was applied to a real-case study in the processed cheese industry.FindingsThe findings revealed practical and conceptional contributions which can be used to continuously improve quality management (QM) in food processing. Thereby, the developed data-ecosystem supports production and QM in the decision-making processes. The findings of the analysis are a valuable basis to enhance operational processes, aiming to prevent quality deviations and non-conforming finished products.Originality/valueProcess mining is still rarely used in the food industry. Thereby, the proposed method helps to identify tacit knowledge in the food processing industry, which was shown by the framework for the preparation of event logs and the data ecosystem.

Streptomyces as a Novel Biotool for Azo Pigments Remediation in Contaminated Scenarios.

Azo pigments are widely used in the textile and leather industry, and they generate diverse contaminants (mainly in wastewater effluents) that affect biological systems, the rhizosphere community, and the natural activities of certain species. This review was performed according to the Systematic Reviews and Meta Analyses (PRISMA) methodology. In the last decade, the use of Streptomyces species as biological azo-degraders has increased, and these bacteria are mainly isolated from mangroves, dye-contaminated soil, and marine sediments. Azo pigments such as acid orange, indigo carmine, Congo red, and Evans blue are the most studied compounds for degradation, and Streptomyces produces extracellular enzymes such as peroxidase, laccase, and azo reductase. These enzymes cleave the molecule through asymmetric cleavage, followed by oxidative cleavage, desulfonation, deamination, and demethylation. Typically, some lignin-derived and phenolic compounds are used as mediators to improve enzyme activity. The degradation process generates diverse compounds, the majority of which are toxic to human cells and, in some cases, can improve the germination process in some horticulture plants. Future research should include analytical methods to detect all of the molecules that are generated in degradation processes to determine the involved reactions. Moreover, future studies should delve into consortium studies to improve degradation efficiency and observe the relationship between microorganisms to generate scale-up biotechnological applications in the wastewater treatment industry.

Enhanced functionalization of superparamagnetic Fe3O4 nanoparticles for advanced drug enrichment and separation applications

Backgroundsuperparamagnetic ferroferric oxide (Fe3O4) nanoparticles can be extensively functionalized for applications in drug enrichment and separation. Their high magnetic responsiveness and controllable surface modification enable rapid drug enrichment and separation under external magnetic fields. This study aimed to enhance the application potential of superparamagnetic Fe3O4 nanoparticles in the field of drug enrichment and separation by functionalizing these nanoparticles to improve their biocompatibility and targeting capabilities.Methodssuperparamagnetic Fe3O4 nanoparticles functionalized with dopamine were synthesized using benzyl alcohol as the solvent and iron acetylacetonate as the precursor. The dopamine-functionalized superparamagnetic iron oxide nanoparticles were used to analyze protein enrichment and separation. Characterization of the nanoparticles was conducted, including analysis of particle size distribution, Zeta potential, and fluorescence spectra using a fluorescence spectrophotometer.Resultsthe Fe3O4 nanoparticles maintained high magnetism from the original material and exhibited uniform particle size distribution and stable Zeta potential. The saturation magnetization of dopamine-functionalized superparamagnetic Fe3O4 nanoparticles showed no significant difference compared to before coating, indicating minimal influence of dopamine on the internal magnetic core of the nanoparticles. The Fe3O4 nanoparticles demonstrated good biocompatibility and stability.Conclusionfunctionalization of superparamagnetic Fe3O4 nanoparticles significantly enhances their efficiency in drug enrichment and separation processes, suggesting broad applications in the pharmaceutical industry.

Enhancing the Realisation of Pattern Parametrisation and Assessing Behavioural Intention to Adopt It

Pattern parametrisation has the potential to address the inefficiencies and environmental concerns associated with traditional pattern construction practices dominating both industry and academia. However, its adoption is still limited due to a lack of realisation despite its presence in CAD technical applications for decades. This has impeded its ability to shift the pattern industry and academia to a more efficient and sustainable paradigm. Using mixed methods and a three-phased quasi-experimental design, this study aims to enhance the realisation of pattern parametrisation across various industry and academic settings and assess participants’ behavioural intentions to adopt it. Findings from a convenience sample of 40 UK-based participants, analysed using Kirkpatrick’s (1994) model, indicate increased recognition and intention to adopt pattern parametrisation, as evidenced by participants identifying its diverse applications and aspiring to integrate it into their future goals. This study contributes to theory and has practical applications for both academia and industry.

De novo assembly of Idesia polycarpa transcriptome and unsaturated fatty acid biosynthesis candidate genes Mining and functional Identification

Unsaturated fatty acids (UFA) in lipids are the key to nutraceutical oil applications, with various potential applications in nutraceutical functional foods and pharmaceutical industries. In Idesia polycarpa (Salicaceae), more than 80 % of UFA have been found in the fruits; yet, the underlying genetic mechanism remains poorly understood. Due to the lack of theoretical research on the genes related to lipid biosynthesis and the complete genetic transformation system of I. polycarpa fruit, the selection and breeding of I. polycarpa, an excellent oil tree, has been severely restricted. In-depth understanding of the molecular mechanism and gene function of lipid biosynthesis of I. polycarpa fruit is therefore of great significance for the development of I. polycarpa resources. This is not only conducive to the genetic improvement of I. polycarpa by molecular breeding technologies but can also provide a reference for the study of the gene functions of other oil plants. In this study, the FA accumulation patterns of I. polycarpa fruits during 8 growth periods were analysed. Fruit from two developmental periods with different UFA levels were analysed for RNA sequencing by an Illumina NovaSeq 6000 HiSeq platform. De novo transcriptome assembly presented 115,350 unigenes and 4382 differentially expressed genes (DEGs). Functional annotation in the KEGG pathway and combined with DEG data revealed candidate genes potentially involved in UFA biosynthesis. Expression analysis of q-PCR of IpDGAT2, IpGPAT, IpKASII, IpSAD, IpFAD2, IpFAD3 and IpFAD8 suggested that these genes are highly involved in UFA biosynthesis. Full-length candidate genes were cloned and analysed by bioinformatic tools, and function analysis of IpSAD and IpFAD3 showed that these genes regulated the products of linoleic acid metabolism. This study provides a foundation for UFA biosynthesis in Idesia polycarpa, facilitating its genetic breeding in the future.

Open source container orchestration for Industry 4.0 – requirements and systematic feature analysis

Container-based virtualization is a popular technique, e.g., to realize microservice architectures. Recently, containers became popular in Industry 4.0 / IIoT systems, which typically consist of hundreds of (edge) devices and machines. In such setups, efficient management of containers is essential as offered by container orchestrators like Kubernetes. However, currently no specific overviews discussing orchestrator capabilities for Industry 4.0 are available. In this paper, we analyze nine open source container orchestrators for their application in Industry 4.0 or IIoT settings as a basis for future research and development. We contribute a systematic literature review to identify 23 basic orchestration requirements. We complement this by insights from an intensive requirements collection in a research project on intelligent industrial production, as well as selected features from a published generic orchestrator analysis. From these 66 requirements, we derive a requirements/feature taxonomy, which we use to analyze the nine open source orchestrators including Kubernetes. We show that there is, e.g., still a lack of support regarding edge devices, IIoT protocols, security mechanisms, and specialized resources for artificial intelligence.

Capillary container array structures for efficient, energy-saving, and sustainable evaporative cooling and humidification

Evaporative cooling is a widely employed method for air conditioning and heat dissipation, utilizing the phase transition of water to achieve simultaneous cooling and humidification. It relies on high-surface-area solid materials to enhance air-liquid contact upon wetting. However, these water-absorbing materials have drawbacks such as increased air resistance, degradation of water quality, and limited water retention capabilities. This study introduces a novel structure called the capillary container array (CCA) for effective humidification and cooling. The CCA structure overcomes the limitations of traditional methods by incorporating interconnected capillary containers with adjustable spacing and utilizing capillary forces to capture liquid droplets while minimizing the solid component. Compared to commercial paper-based wet curtains, the CCA structure offers significant advantages including strong water retention capabilities, high humidification performance, low air resistance and high pollution resistance. It increases water capacity by 6 times, humidification capacity by 8 %, and temperature drop by 2.2 °C. Moreover, it exhibits a 5-fold longer in water retention duration, a 30 % reduction in air pressure drop and a 3-fold increase in the performance coefficient, and maintains excellent water quality (<1 NTU) over prolonged operation. These advancements make the CCA structure highly promising for various applications in agriculture, industry and environmental conservation.

A quasi-3D SinZZ model-driven multi-field Chebyshev FEM for nonlinear vibration control in multilayer multiferroic composite plates

This paper presents an advanced numerical method for modeling and mitigating nonlinear vibrations in thin multilayered fiber-reinforced multiferroic composite plates, addressing complex multi-physical interactions. The proposed methodology leverages a multi-physical coupling Chebyshev finite element formulation, utilizing high-order shape functions derived from Chebyshev polynomials. By integrating the strengths of spectral element methods and Legendre spectral finite element methods, this approach effectively overcomes challenges such as shear locking and spurious zero energy modes while ensuring high convergence rates in multi-physical problems. A quasi-3D refined model, incorporating the Murakami zig-zag model and sinusoidal shear deformation theory, is employed to accurately capture the nonlinear Von Kármán strain–displacement relationship and the magneto-electro-elastic coupling in multilayer structures with thickness-dependent material properties. To suppress nonlinear vibrations, the study utilizes a closed-loop multiphysical Chebyshev finite element model for time-domain analysis of viscoelastically damped systems, employing the Golla–Hughes–McTavish model. The results underscore the significant influence of multiferroic properties and the strategic distribution of ferroelectric fibers within the substrate on the dynamic behavior of the plate. The numerical validation, supported by rigorous verification, demonstrates the robustness of the proposed method in effectively simulating and controlling multilayer fiber-reinforced multiferroic composite plates. Additionally, this research highlights the potential for significant vibration reduction through semi-active damping mechanisms, offering valuable insights for practical applications in industries where precision and stability are critical.