Broad Range Research Articles

Predictive tools for the cooling rate-dependent microstructure evolution of AISI 316L stainless steel in additive manufacturing

This study presents a thorough evaluation of advanced predictive tools for the solidification microstructure of AISI 316L stainless steel, taking into account both chemical composition and cooling rate. In order to achieve a broad range of cooling rates comparable to prominent additive manufacturing processes, autogenous laser welds were carried out on the steel plate by means of powderless directed energy deposition (DED) and selective laser melting (SLM) machines. Computational analysis of cooling rates and metallographic investigations revealed that DED welds solidify at moderate rates, exhibiting a primary ferrite (FA) solidification mode. Conversely, SLM welds solidify at significantly higher rates, showing dual solidification modes: initial austenite (A) at the fusion boundary and subsequent ferrite (F) at the interior. The WRC-1992 constitution diagram predicts the FA mode in accordance with the findings in the DED welds. An implicated Schaeffler diagram, proposed for a comparable cooling rate, also predicts the same mode. An artificial neural network model, referred to as ORFN, consistently predicts the variation of ferrite content within the DED welds based on the cooling rate variation. The dual mode observed in the SLM welds aligns with established knowledge regarding the alteration of stainless steel solidification under extremely high cooling rates. While the occurrence of the A mode at the fusion boundary is anticipated according to implicated Schaeffler diagrams for extremely high cooling rates, the transition to the F mode is not addressed explicitly.

Review on the Discovery of New Benzimidazole Derivatives as Anticancer Agents: Synthesis and Structure-activity Relationship (2010-2022)

Background: Benzimidazole (Benz-fused bicyclic ring system) is the most versatile class of heterocyclic compounds due to its numerous applications in industrial and synthetic organic chemistry because of its many biological actions. Benzimidazole analogs have been used to discover various medical problems, such as cancer, bacterial infections, fungi infections, etc. Researchers are studying nitrogencontaining hybrid heterocyclic compounds because they provide a broad range of therapeutic potential and have minimal side effects. Objective: The current literature review emphasizes recent developments in the design of new benzimidazole derivatives as possible anticancer agents with their relationship between structure and activity, which will give insight into the future design of more active benzimidazole molecules. Results: The present review consists of synthetic protocols for the synthesis of benzimidazole derivatives along with their pharmacological potentials and structure-activity relationship in correlation with synthetic molecules to provide a depth view of the work done on benzimidazole. Conclusion: It would be significant for further research in developing better drug molecules representing a potent derivative of medicinal agents.

Advanced visual components inspired by animal eyes

Abstract Artificial vision systems pervade our daily lives as a foremost sensing apparatus in various digital technologies, from smartphones to autonomous cars and robotics. The broad range of applications for conventional vision systems requires facile adaptation under extreme and dynamic visual environments. However, these current needs have complicated individual visual components for high-quality image acquisition and processing, which indeed leads to a decline in efficiency in the overall system. Here, we review recent advancements in visual components for high-performance visual processing based on strategies of biological eyes that execute diverse imaging functionalities and sophisticated visual processes with simple and concise ocular structures. This review first covers the structures and functions of biological eyes (i.e., single-lens eyes and compound eyes), which contain micro-optic components and nanophotonic structures. After that, we focus on their inspirations in imaging optics/photonics, light-trapping and filtering components, and retinomorphic devices. We discuss the remaining challenges and notable biological structures waiting to be implemented.

Computational tools for quantifying actin filament numbers, lengths, and bundling.

The actin cytoskeleton is a dynamic filamentous network that assembles into specialized structures to enable cells to perform essential processes. Direct visualization of fluorescently-labeled cytoskeletal proteins has provided numerous insights into the dynamic processes that govern the assembly of actin-based structures. However, accurate analysis of these experiments is often complicated by the interdependent and kinetic natures of the reactions involved. It is often challenging to disentangle these processes to accurately track their evolution over time. Here, we describe two programs written in the MATLAB programming language that facilitate counting, length measurements, and quantification of bundling of actin filaments visualized in fluorescence micrographs. To demonstrate the usefulness of our programs, we describe their application to the analysis of two representative reactions: (1) a solution of pre-assembled filaments under equilibrium conditions, and (2) a reaction in which actin filaments are crosslinked together over time. We anticipate that these programs can be applied to extract equilibrium and kinetic information from a broad range of actin-based reactions, and that their usefulness can be expanded further to investigate the assembly of other biopolymers.

Modeling Atmospheric Lines by the Exoplanet Community (MALBEC) Version 1.0: A CUISINES Radiative Transfer Intercomparison Project

Radiative transfer (RT) models are critical in the interpretation of exoplanetary spectra, in simulating exoplanet climates, and when designing the specifications of future flagship observatories. However, most models differ in methodologies and input data, which can lead to significantly different spectra. In this paper, we present the experimental protocol of the Modeling Atmospheric Lines By the Exoplanet Community (MALBEC) project. MALBEC is an exoplanet model intercomparison project that belongs to the Climates Using Interactive Suites of Intercomparisons Nested for Exoplanet Studies framework, which aims to provide the exoplanet community with a large and diverse set of comparison and validation of models. The proposed protocol tests include a large set of initial participating RT models, a broad range of atmospheres (from hot Jupiters to temperate terrestrials), and several observation geometries, which would allow us to quantify and compare the differences between different RT models used by the exoplanetary community. Two types of tests are proposed: transit spectroscopy and direct imaging modeling, with results from the proposed tests to be published in dedicated follow-up papers. To encourage the community to join this comparison effort and as an example, we present simulation results for one specific transit case (GJ-1214 b), in which we find notable differences in how the various codes handle the discretization of the atmospheres (e.g., sub-layering), the treatment of molecular opacities (e.g., correlated-k, line-by-line) and the default spectroscopic repositories generally used by each model (e.g., HITRAN, HITEMP, ExoMol).

Structural validation of the Manchester-Oxford Foot Questionnaire for use in foot and ankle surgery.

The Manchester-Oxford Foot Questionnaire (MOxFQ) is an anatomically specific patient-reported outcome measure (PROM) currently used to assess a wide variety of foot and ankle pathology. It consists of 16 items across three subscales measuring distinct but related traits: walking/standing ability, pain, and social interaction. It is the most used foot and ankle PROM in the UK. Initial MOxFQ validation involved analysis of 100 individuals undergoing hallux valgus surgery. This project aimed to establish whether an individual's response to the MOxFQ varies with anatomical region of disease (measurement invariance), and to explore structural validity of the factor structure (subscale items) of the MOxFQ. This was a single-centre, prospective cohort study involving 6,637 patients (mean age 52 years (SD 17.79)) presenting with a wide range of foot and ankle pathologies between January 2013 and December 2021. To assess whether the MOxFQ responses vary by anatomical region of foot and ankle disease, we performed multigroup confirmatory factor analysis. To assess the structural validity of the subscale items, exploratory and confirmatory factor analyses were performed. Measurement invariance by pathology was confirmed, suggesting the same model can be used across all foot and ankle anatomical regions. Exploratory factor analysis demonstrated a two- to three-factor model, and suggested that item 13 (inability to carry out work/everyday activities) and item 14 (inability to undertake social/recreational activities) loaded more positively onto the "walking/standing" subscale than their original "social interaction" subscale. This large cohort study supports the current widespread use of the MOxFQ across a broad range of foot and ankle pathologies. Our analyses found indications that could support alterations to the original factor structure (items 13 and 14 might be moved from the "social interaction" to the "walking/standing" subscale). However, this requires further work to confirm.

Turning community stories into community art1

A broad range of community art projects and programmes have been documented around the world since I first reported on this project in 2012. However, it still remains that lack of time and/or funding means that most art teachers are unable to engage in community arts or generate resources and opportunities to teach community arts. This means that unless community art projects directly engage schools and teachers in their projects and time is put aside for students and teachers to go on excursions, visit sites and engage with these communities, there are few opportunities for students to engage in community arts-based learning. This article reports on an innovative community art project that engaged narrative, and sculptural form, as a way of learning about community, place and identity. The project is explained from the perspective of an art educator, researcher and artist who was employed in the project both as community artist and as facilitator. This ‘insider’s perspective’ aims to afford some context to relevant theories through which such projects can be understood as potentially beneficial to art education – particularly in the way people have used narrative to communicate issues of place and the ways in which artists have translated community narratives into sculptural form. The author’s insider perspective offers insight into this project to share how a community art project could be designed and facilitated for students to engage in their local region and therefore a way forward for teachers and students to engage in something similar, to learn about how community stories can be translated into contemporary art, and the important role of place and identity in this work.

NuSC: An Effective Local Search Algorithm for Solving the Set Covering Problem.

The set covering problem (SCP) is a fundamental NP-hard problem in computer science and has a broad range of important real-world applications. In practice, SCP instances transformed from real-world applications would be of large scale, so it is of significant importance to design effective heuristic algorithms, especially local search ones. However, there exist only few research works on developing local search algorithms for solving SCP. In this article, we propose a new local search algorithm for solving SCP, dubbed NuSC. In particular, NuSC introduces a new combined scoring function for subset selection, which combines different subset properties in an effective way and helps NuSC find more optimized solutions. Besides, NuSC incorporates a dynamic weighting scheme for elements, a tabu search strategy, and a novelty selection mechanism to further enhance its practical performance. In order to study the effectiveness and robustness of our proposed NuSC algorithm, we conduct extensive experiments to compare NuSC against many state-of-the-art competitors on various types of SCP instances. Our experimental results demonstrate that NuSC significantly outperforms its competitors on the majority of instances, indicating the superiority of NuSC. Also, our empirical evaluations confirm the effectiveness of each algorithmic technique underlying NuSC.

FrFT-based estimation of linear and nonlinear impairments using Vision Transformer

To comprehensively assess the conditions of an optical fiber communication system, it is essential to implement joint estimation of the following four critical impairments: nonlinear signal-to-noise ratio (SNRNL), optical signal-to-noise ratio (OSNR), chromatic dispersion (CD), and differential group delay (DGD). However, current studies only achieve identifying a limited number of impairments within a narrow range, due to a lack of high-performance computing algorithms and a unified representation of impairments. To address these challenges, we adopt time-frequency signal processing based on the fractional Fourier transform (FrFT) to achieve the unified representation of impairments, while employing a Transformer-based neural network (NN) to break through network performance limitations. To verify the effectiveness of the proposed estimation method, numerical simulations were conducted on a five-channel polarization-division-multiplexed quadrature phase shift keying (PDM-QPSK) long haul optical transmission system with the symbol rate of 50 GBaud per channel. The mean absolute error (MAE) for SNRNL, OSNR, CD, and DGD estimation is 0.091 dB, 0.058 dB, 117 ps/nm, and 0.38 ps, and the monitoring window ranges from 0−20dB, 10−30dB, 1700−51,000ps/nm, and 0−100ps, respectively. Our proposed method achieves accurate estimation of linear and nonlinear impairments over a broad range, representing a significant advancement in the field of optical performance monitoring (OPM).

Deep neural networks optimization for resource-constrained environments: techniques and models

This paper aims to present a comprehensive review of advanced techniques and models with a specific focus on deep neural network (DNN) for resource-constrained environments (RCE). The paper contributes by highlighting the RCE devices, analyzing challenges, reviewing a broad range of optimization techniques and DNN models, and offering a comparative assessment. The findings provide potential optimization techniques and recommend a baseline model for future development. It encompasses a broad range of DNN optimization techniques, including network pruning, weight quantization, knowledge distillation, depthwise separable convolution, residual connections, factorization, dense connections, and compound scaling. Moreover, the review analyzes the established optimization models which utilizes the above optimization techniques. A comprehensive analysis is conducted for each technique and model, considering its specific attributes, usability, strengths, and limitations in the context of effective deployment in RCEs. The review also presents a comparative assessment of advanced DNN models’ deployment for image classification, employing key evaluation metrics such as accuracy and efficiency factors like memory and inference time. The article concludes with the finding that combining depthwise separable convolution, weight quantization, and pruning represents potential optimization techniques, while also recommending EfficientNetB1 as a baseline model for the future development of optimization models in RCE image classification.<p> </p>

Poly-Si/a-Si/4H-SiC p-n heterojunction broadband photodetector prepared by magnetron sputtering

With the increasing complexity of scenarios, there is a growing need for broadband photodetectors (PDs). In this work, we report a polycrystalline-Si (poly-Si)/amorphous-Si (a-Si)/4H-SiC p-n heterojunction PD with efficient response in a broad spectral range of ultraviolet–visible–near-infrared. The poly-Si/a-Si/4H-SiC heterojunction was achieved by magnetron sputtering and annealing. The fabricated heterojunction device has a low dark current of 1 pA at −40 V and a fast response time of 3 ns due to the outstanding rectification characteristics of the heterojunction combined with narrow bandgap and wide bandgap material. In addition, the carrier behavior of the heterojunction exposed to broadband light is analyzed in detail by constructing the energy band diagram.

The effect of winds on atmospheric layers of red supergiants II. Modelling VLTI/GRAVITY and MATISSE observations of AH Sco, KW Sgr, V602 Car, CK Car, and V460 Car

Context. Mass loss plays a crucial role in the lives of massive stars, especially as the star leaves the main sequence and evolves to the red supergiant (RSG) phase. Despite its importance, the physical processes that trigger mass-loss events in RSGs are still not well understood. Recently, we showed that adding a semi-empirical wind to atmosphere models can accurately reproduce observed extensions in the atmospheres of RSGs, where the mass-loss events are taking place, particularly in the CO and water lines. Aims. By adding a static wind to a MARCS atmospheric model, we computed synthetic observables that match new interferometric data of the RSGs AH Sco, KW Sgr, V602 Car, CK Car, and V460 Car obtained with the VLTI/MATISSE and VLTI/GRAVITY instruments between August 2022 and February 2023. We also used archival VLTI/AMBER data of KW Sgr and VLTI/GRAVITY data of AH Sco. The MATISSE wavelength range includes the SiO molecule at 4.0 μm with a spectral resolution of R ~ 500. Methods. The model intensities with respect to the line-of-sight angle (μ) as well as the spectra and visibilities were computed using the stellar radiative transfer code TURBOSPECTRUM. We found the best-fit model, mass-loss rate, and best-fit angular Rosseland diameter for the observations. We simultaneously matched our model to the data, covering a wavelength range of 1.8–5.0 μm, which corresponds to the K, L, and M bands. Results. Our models reproduce the spectro-interferometric data over this wide wavelength range, including extended atmospheric layers of CO, H2O, and SiO. We obtain a range of Rosseland angular diameters between 3.0 < θRoss < 5.5 mas and a range of mass-loss rates of −6.5 < log Ṁ/M⊙ yr−1 < −4 for our five targets. In our best-fit models, the partial pressure of SiO relative to the gas pressure, PSiO/Pg, and the SiO 4.0 μm line intensity increase between 2 and 3 stellar radii. The relative intensity depends on the luminosity used for our models, since the more luminous models have a higher mass-loss rate. Conclusions. This work further demonstrates that our MARCS+wind model can reproduce the observed physical extension of RSG atmospheres for several spectral diagnostics spanning a broad wavelength range. We reproduce both spectra and visibilities of newly obtained data as well as provide temperature and density stratifications that are consistent with the observations. With the MATISSE data, we newly include the extension of SiO layers as a precursor of silicate dust.

Continuous adiabatic frequency conversion for FMCW-LiDAR

Continuous tuning of the frequency of laser light serves as the fundamental basis for a myriad of applications spanning basic scientific research to industrial settings. These applications encompass endeavors such as the detection of gravitational waves, the development of precise optical clocks, environmental monitoring for health and ecological purposes, as well as distance measurement techniques. However, achieving a broad tuning range exceeding 100 GHz along with sub-microsecond tuning times, inherent linearity in tuning, and coherence lengths beyond 10 m presents significant challenges. Here, we demonstrate that electro-optically driven adiabatic frequency converters utilizing high-Q microresonators fabricated from lithium niobate possess the capability to convert arbitrary voltage signals into frequency chirps with temporal resolutions below 1 µs. The temporal evolution of the frequency correlates accurately with the applied voltage signal. We have achieved to generate 200-ns-long frequency chirps with deviations of less than 1 % from perfect linearity without requiring supplementary measures. The coefficient of determination is R2>0.999\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R^2>0.999$$\\end{document}. Moreover, the coherence length of the emitted light exceeds 20 m. To validate these findings, we employ the linear frequency sweeps for Frequency-Modulated Continuous Wave (FMCW) LiDAR covering distances ranging from 0.5 to 10 m. Leveraging the demonstrated nanosecond-level tuning capabilities, coupled with the potential to tune the eigenfrequency of lithium-niobate-based resonators by several hundred GHz, our results show that electro-optically driven adiabatic frequency converters can be used in applications that require ultrafast and flexible continuous frequency tuning characterized by inherent linearity and substantial coherence length.

Highly Sensitive Broadband Polarized Photodetector Based on the As0.6P0.4/WSe2 Heterostructure toward Imaging and Optical Communication Application.

Polarization-sensitive photodetectors based on two-dimensional anisotropic materials still encounter the issues of narrow spectral coverage and low polarization sensitivity. To address these obstacles, anisotropic As0.6P0.4 with a narrow band gap has been integrated with WSe2 to construct a type-II heterostructure, realizing a high-performance polarization-sensitive photodetector with broad spectral range from 405 to 2200 nm. By operating in photovoltaic mode at zero bias, the device shows a very low dark current of ∼0.02 picoampere, high responsivity of 492 m A/W, and high photoswitching ratio of 6 × 104, yielding a high specific detectivity of 1.4 × 1012 Jones. The strong in-plane anisotropy of As0.6P0.4 endows the device with a capability of polarization-sensitive detection with a high polarization ratio of 6.85 under a bias voltage. As an image sensor and signal receiver, the device shows great potential in imaging and optical communication applications. This work develops an anisotropic vdW heterojunction to realize polarization-sensitive photodetectors with wide spectral coverage, fast response, and high sensitivity, providing a new candidate for potential applications of polarization-resolved electronics and photonics.

Mind-wandering increases in frequency over time during task performance: An individual-participant meta-analytic review.

Attention has a seemingly inevitable tendency to turn inward toward our thoughts. Mind-wandering refers to moments when this inward focus diverts attention away from the current task-at-hand. Mind-wandering is thought to be ubiquitous, having been estimated to occur between 30% and 50% of our waking moments. Yet, it is unclear whether this frequency is similar within-task performance contexts and unknown whether mind-wandering systematically increases with time-on-task for a broad range of tasks. We conducted a systematic literature search and individual participant data meta-analysis of rates of occurrence of mind-wandering during task performance. Our search located 68 research reports providing almost a half-million total responses to experience sampling mind-wandering probes from more than 10,000 unique individuals. Latent growth curve models estimated the initial occurrence of mind-wandering and linear change in mind-wandering over sequential probes for each study sample, and effects were summarized using multivariate meta-analysis. Our results confirm that mind-wandering increases in frequency over time during task performance, implicating mind-wandering in characteristic within-task psychological changes, such as increasing boredom and patterns of worsening behavioral performance with time-on-task. The systematic search and meta-analysis provide the most comprehensive assessment of normative rates of mind-wandering during task performance reported to date. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Peptide nucleic acid-assisted generation of targeted double-stranded DNA breaks with T7 endonuclease I.

Gene-editing technologies have revolutionized biotechnology, but current gene editors suffer from several limitations. Here, we harnessed the power of gamma-modified peptide nucleic acids (γPNAs) to facilitate targeted, specific DNA invasion and used T7 endonuclease I (T7EI) to recognize and cleave the γPNA-invaded DNA. Our data show that T7EI can specifically target PNA-invaded linear and circular DNA to introduce double-strand breaks (DSBs). Our PNA-Guided T7EI (PG-T7EI) technology demonstrates that T7EI can be used as a programmable nuclease capable of generating single or multiple specific DSBs in vitro under a broad range of conditions and could be potentially applied for large-scale genomic manipulation. With no protospacer adjacent motif (PAM) constraints and featuring a compact protein size, our PG-T7EI system will facilitate and expand DNA manipulations both in vitro and in vivo, including cloning, large-fragment DNA assembly, and gene editing, with exciting applications in biotechnology, medicine, agriculture, and synthetic biology.

Entropy Generation and Thermal Radiation Impact on Magneto-Convective Flow of Heat-Generating Hybrid Nano-Liquid in a Non-Darcy Porous Medium with Non-Uniform Heat Flux

The principal objective of the study is to examine the impact of thermal radiation and entropy generation on the magnetohydrodynamic hybrid nano-fluid, Al2O3/H2O, flow in a Darcy–Forchheimer porous medium with variable heat flux when subjected to an electric field. Investigating the impact of thermal radiation and non-uniform heat flux on the hybrid nano-liquid magnetohydrodynamic flow in a non-Darcy porous environment produces novel and insightful findings. Thus, the goal of the current study is to investigate this. The non-linear governing equation can be viewed as a set of ordinary differential equations by applying the proper transformations. The resultant dimensionless model is numerically solved in Matlab using the bvp4c command. We obtain numerical results for the temperature and velocity distributions, skin friction, and local Nusselt number across a broad range of controlling parameters. We found a significant degree of agreement with other research that has been compared with the literature. The results show that an increase in the Reynolds and Brinckmann numbers corresponds to an increase in entropy production. Furthermore, a high electric field accelerates fluid velocity, whereas the unsteadiness parameter and the presence of a magnetic field slow it down. This study is beneficial to other researchers as well as technical applications in thermal science because it discusses the factors that lead to the working hybrid nano-liquid thermal enhancement.

Pharmacotherapy for Disease Modification in Early Parkinson's Disease: How Early Should We Be?

Slowing or halting progression continues to be a major unmet medical need in Parkinson's disease (PD). Numerous trials over the past decades have tested a broad range of interventions without ultimate success. There are many potential reasons for this failure and much debate has focused on the need to test 'disease-modifying' candidate drugs in the earliest stages of disease. While generally accepted as a rational approach, it is also associated with significant challenges around the selection of trial populations as well as trial outcomes and durations. From a health care perspective, intervening even earlier and before at-risk subjects have gone on to develop overt clinical disease is at the heart of preventive medicine. Recent attempts to develop a framework for a biological definition of PD are aiming to enable 'preclinical' and subtype-specific diagnostic approaches. The present review addresses past efforts towards disease-modification, including drug targets and reasons for failure, as well as novel targets that are currently being explored in disease-modification trials in early established PD. The new biological definitions of PD may offer new opportunities to intervene even earlier. We critically discuss the potential and challenges around planning 'disease-prevention' trials in subjects with biologically defined 'preclinical' or prodromal PD.

Analysis on metaphor of sky based on corpus

This study explores the different metaphorical usage of sky using the BNC corpus, one of the largest English corpus in the world. It aims to uncover the diverse conceptualizations and linguistic expressions associated with this universal and culturally significant element. By employing corpus linguistics methods, the study views a broad range of written and spoken texts to identify and analyze metaphors of sky in various contexts and genres. The research seeks to elucidate how the metaphor of sky is employed to convey abstract concepts, such as emotions, aspirations, spirituality, and how it is intertwined with cultural, environmental, and imaginative dimensions. Through the systematic analysis of linguistic data extracted from the BNC corpus, this study contributes to a deeper understanding of the metaphorical diversity of the sky. The findings provide insights into the cognitive and communicative significance of sky-based metaphors, offering valuable implications for fields such as linguistics, cognitive science, cultural studies, and literary analysis.

A proposed reconstruction method of a 3D animation scene based on a fuzzy long and short-term memory algorithm.

With the development of computer technology leading to a broad range of virtual technology implementations, the construction of virtual tasks has become highly demanded and has increased rapidly, especially in animation scenes. Constructing three-dimensional (3D) animation characters utilizing properties of actual characters could provide users with immersive experiences. However, a 3D face reconstruction (3DFR) utilizing a single image is a very demanding operation in computer graphics and vision. In addition, limited 3D face data sets reduce the performance improvement of the proposed approaches, causing a lack of robustness. When datasets are large, face recognition, transformation, and animation implementations are relatively practical. However, some reconstruction methods only consider the one-to-one processes without considering the correlations or differences in the input images, resulting in models lacking information related to face identity or being overly sensitive to face pose. A face model composed of a convolutional neural network (CNN) regresses 3D deformable model coefficients for 3DFR and alignment tasks. The manuscript proposes a reconstruction method for 3D animation scenes employing fuzzy LSMT-CNN (FLSMT-CNN). Multiple collected images are employed to reconstruct 3D animation characters. First, the serialized images are processed by the proposed method to extract the features of face parameters and then improve the conventional deformable face modeling (3DFDM). Afterward, the 3DFDM is utilized to reconstruct animation characters, and finally, high-precision reconstructions of 3D faces are achieved. The FLSMT-CNN has enhanced both the precision and strength of the reconstructed 3D animation characters, which provides more opportunities to be applied to other animation scenes.