Combinatorial Functions Research Articles

Energy consumption forecasting for laser manufacturing of large artifacts based on fusionable transfer learning

This study presents an energy consumption (EC) forecasting method for laser melting manufacturing of metal artifacts based on fusionable transfer learning (FTL). To predict the EC of manufacturing products, particularly from scale-down to scale-up, a general paradigm was first developed by categorizing the overall process into three main sub-steps. The operating electrical power was further formulated as a combinatorial function, based on which an operator learning network was adopted to fit the nonlinear relations between the fabricating arguments and EC. Parallel-arranged networks were constructed to investigate the impacts of fabrication variables and devices on power. Considering the interconnections among these factors, the outputs of the neural networks were blended and fused to jointly predict the electrical power. Most innovatively, large artifacts can be decomposed into time-dependent laser-scanning trajectories, which can be further transformed into fusionable information via neural networks, inspired by large language model. Accordingly, transfer learning can deal with either scale-down or scale-up forecasting, namely, FTL with scalability within artifact structures. The effectiveness of the proposed FTL was verified through physical fabrication experiments via laser powder bed fusion. The relative error of the average and overall EC predictions based on FTL was maintained below 0.83%. The melting fusion quality was examined using metallographic diagrams. The proposed FTL framework can forecast the EC of scaled structures, which is particularly helpful in price estimation and quotation of large metal products towards carbon peaking and carbon neutrality.

Developmental cues are encoded by the combinatorial phosphorylation of Arabidopsis RETINOBLASTOMA-RELATED protein RBR1.

RETINOBLASTOMA-RELATED (RBR) proteins orchestrate cell division, differentiation, and survival in response to environmental and developmental cues through protein-protein interactions that are governed by multisite phosphorylation. Here we explore, using a large collection of transgenic RBR phosphovariants to complement protein function in Arabidopsis thaliana, whether differences in the number and position of RBR phosphorylation events cause a diversification of the protein's function. While the number of point mutations influence phenotypic strength, phosphosites contribute differentially to distinct phenotypes. RBR pocket domain mutations associate primarily with cell proliferation, while mutations in the C-region are linked to stem cell maintenance. Both phospho-mimetic and a phospho-defective variants promote cell death, suggesting that distinct mechanisms can lead to similar cell fates. We observed combinatorial effects between phosphorylated T406 and phosphosites in different protein domains, suggesting that specific, additive, and combinatorial phosphorylation events fine-tune RBR function. Suppression of dominant phospho-defective RBR phenotypes with a mutation that inhibits RBR interacting with LXCXE motifs, and an exhaustive protein-protein interaction assay, not only revealed the importance of DREAM complex members in phosphorylation-regulated RBR function but also pointed to phosphorylation-independent RBR roles in environmental responses. Thus, combinatorial phosphorylation defined and separated developmental, but not environmental, functions of RBR.

On submodular prophet inequalities and correlation gap

Prophet inequalities and secretary problems have been extensively studied in recent years due to their elegance, connections to online algorithms, stochastic optimization, and mechanism design problems in game theoretic settings. Rubinstein and Singla [31] developed a notion of combinatorial prophet inequalities in order to generalize the standard prophet inequality setting to combinatorial valuation functions such as submodular and subadditive functions. For non-negative submodular functions they demonstrated a constant factor prophet inequality for matroid constraints. Along the way they showed a variant of the correlation gap for non-negative submodular functions.In this paper we revisit their notion of correlation gap as well as the standard notion of correlation gap and prove much tighter and cleaner bounds. Via these bounds and other insights we obtain substantially improved constant factor combinatorial prophet inequalities for both monotone and non-monotone submodular functions over any constraint that admits an Online Contention Resolution Scheme. In addition to improved bounds we describe efficient polynomial-time algorithms that achieve these bounds.

FTMMR: Fusion Transformer for Integrating Multiple Molecular Representations.

Molecular property prediction has gained substantial attention due to its potential for various bio-chemical applications. Numerous attempts have been made to enhance the performance by combining multiple molecular representations (1D, 2D, and 3D). However, most prior works only merged a limited number of representations or tried to embed multiple representations through a single network without using representation-specific networks. Furthermore, the heterogeneous characteristics of each representation made the fusion more challenging. Addressing these challenges, we introduce the Fusion Transformer for Multiple Molecular Representations (FTMMR) framework. Our strategy employs three distinct representation-specific networks and integrates information from each network using a fusion transformer architecture to generate fused representations. Additionally, we use self-supervised learning methods to align heterogeneous representations and to effectively utilize the limited chemical data available. In particular, we adopt a combinatorial loss function to leverage the contrastive loss for all three representations. We evaluate the performance of FTMMR using seven benchmark datasets, demonstrating that our framework outperforms existing fusion and self-supervised methods.

Engineered CRISPR-Cas12a for higher-order combinatorial chromatin perturbations.

Multiplexed genetic perturbations are critical for testing functional interactions among coding or non-coding genetic elements. Compared to double-stranded DNA cutting, repressive chromatin formation using CRISPR interference (CRISPRi) avoids genotoxicity and is more effective for perturbing non-coding regulatory elements in pooled assays. However, current CRISPRi pooled screening approaches are limited to targeting one to three genomic sites per cell. We engineer an Acidaminococcus Cas12a (AsCas12a) variant, multiplexed transcriptional interference AsCas12a (multiAsCas12a), that incorporates R1226A, a mutation that stabilizes the ribonucleoprotein-DNA complex via DNA nicking. The multiAsCas12a-KRAB fusion improves CRISPRi activity over DNase-dead AsCas12a-KRAB fusions, often rescuing the activities of lentivirally delivered CRISPR RNAs (crRNA) that are inactive when used with the latter. multiAsCas12a-KRAB supports CRISPRi using 6-plex crRNA arrays in high-throughput pooled screens. Using multiAsCas12a-KRAB, we discover enhancer elements and dissect the combinatorial function of cis-regulatory elements in human cells. These results instantiate a group testing framework for efficiently surveying numerous combinations of chromatin perturbations for biological discovery and engineering.

Proposed Fault Detection Algorithm with Optimized Hybrid Speed Control

The Brushless DC (BLDC) motor is a common choice for industrial applications, particularly in the automotive sector, owing to its high efficiency and robust capabilities. To detect the position of the motor rotor, hall-effect sensors can be used, but these sensors may prevent the system from operating if they fail. Consequently, fault-tolerant control (FTC) has been proposed in several studies to ensure continuity of operation in the event of sensor failure. This paper proposes an innovative method of fault detection in the hall effect sensor for a BLDC motor using combinatorial functions. This paper proposes an innovative method of hall-effect sensor fault detection for a BLDC motor using combinatorial functions. For the speed control of the BLDC under study, a hybrid adaptive neuro-fuzzy inference control (ANFIS) is implemented. In addition, the FTC signal reconstruction technique adopted has been improved to achieve motor start-up despite a fault in one of the sensors, thanks to well-defined fault detection algorithms. Simulation results are presented for each sensor failure case to test the effectiveness of the method used.

Machine learning enabled design features of antimicrobial peptides selectively targeting peri-implant disease progression

Peri-implantitis is a complex infectious disease that manifests as progressive loss of alveolar bone around the dental implants and hyper-inflammation associated with microbial dysbiosis. Using antibiotics in treating peri-implantitis is controversial because of antibiotic resistance threats, the non-selective suppression of pathogens and commensals within the microbial community, and potentially serious systemic sequelae. Therefore, conventional treatment for peri-implantitis comprises mechanical debridement by nonsurgical or surgical approaches with adjunct local microbicidal agents. Consequently, current treatment options may not prevent relapses, as the pathogens either remain unaffected or quickly re-emerge after treatment. Successful mitigation of disease progression in peri-implantitis requires a specific mode of treatment capable of targeting keystone pathogens and restoring bacterial community balance toward commensal species. Antimicrobial peptides (AMPs) hold promise as alternative therapeutics through their bacterial specificity and targeted inhibitory activity. However, peptide sequence space exhibits complex relationships such as sparse vector encoding of sequences, including combinatorial and discrete functions describing peptide antimicrobial activity. In this paper, we generated a transparent Machine Learning (ML) model that identifies sequence-function relationships based on rough set theory using simple summaries of the hydropathic features of AMPs. Comparing the hydropathic features of peptides according to their differential activity for different classes of bacteria empowered predictability of antimicrobial targeting. Enriching the sequence diversity by a genetic algorithm, we generated numerous candidate AMPs designed for selectively targeting pathogens and predicted their activity using classifying rough sets. Empirical growth inhibition data is iteratively fed back into our ML training to generate new peptides, resulting in increasingly more rigorous rules for which peptides match targeted inhibition levels for specific bacterial strains. The subsequent top scoring candidates were empirically tested for their inhibition against keystone and accessory peri-implantitis pathogens as well as an oral commensal bacterium. A novel peptide, VL-13, was confirmed to be selectively active against a keystone pathogen. Considering the continually increasing number of oral implants placed each year and the complexity of the disease progression, prevalence of peri-implant diseases continues to rise. Our approach offers transparent ML-enabled paths towards developing antimicrobial peptide-based therapies targeting the changes in the microbial communities that can beneficially impact disease progression.

On the multiplicity order of spinnable star-like transformation semigroup Tw<sup>*</sup><sub>n</sub>

The application of graph theory has gained significant traction within the realm of the algebraic theory of semigroups. This study delves into exploring the geometric properties of the star-like transformation semigroup \alpha\omega_n^*, a distinctive category of transformation, and delineates a tropical graph (curve) by elucidating its algebraic and tropical structure. Through this investigation, various tropical properties are established, offering insights into the graph theory aspects of star-like spinnable T\omega_n^* transformation semigroups. Consequently, the objective of this research is to delineate and characterize several tropical and combinatorial functions applicable to T\omega_n^*.

Application of zeroed neural networks to stability analysis of continuous dynamic systems

Abstract Modern production processes frequently require steady-state analysis of continuous dynamic systems. Traditional numerical approaches, however, fall short in efficiency when tasked with addressing large-scale or dynamic problems. To tackle the inverse problem inherent in stability analysis, this study presents an innovative approach by integrating a combined excitation function into the foundational zeroing neural network (ZNN) model. This integration constrains the ZNN model, evolving it into an enhanced EZNN model specifically designed for solving the inverse of dynamic complex matrices. Additionally, this paper conducts a rigorous theoretical analysis of the robust performance of the EZNN model when excited by the combined function, both in the presence and absence of noise interference. The model solution process is promoted by using a class of high-dimensional continuous dynamic systems as an example, and numerical simulation experiments are used for validation. Considering the dynamic system satisfying { A ( t ) = ( 4 + sin ( 2 t ) 4 - cos ( 2 t ) 5 + sin ( 2 t ) ) C ( t ) = ( cos ( t ) sin ( t ) - cos ( t ) - sin ( t ) cos ( t ) sin ( t ) ) b ( t ) = 4 + cos ( 4 t ) , d ( t ) = ( cos ( 2 t ) , cos ( 2 t ) ) T \left\{ \matrix{ A(t) = \left( {4 + \sin (2t)\quad 4 - \cos (2t)\quad 5 + \sin (2t)} \right) \hfill \cr C(t) = \left( {\matrix{{\cos (t)} & {\sin (t)} & { - \cos (t)} \cr { - \sin (t)} & {\cos (t)} & {\sin (t)} \cr } } \right) \hfill \cr b(t) = 4 + \cos (4t),d(t) = {(\cos (2t),\cos (2t))^T} \hfill \cr} \right. , the error E 1(x(t),t) obtained by the EZNN model with combinatorial function excitation always remains negative or tends rapidly to 0. The x (t) obtained by the model converges rapidly to an exact solution of the system. Through the discussion of parametric conditions, it is also found that increasing the value of parameter γ increases the rate of convergence of the ZNN model.

Ligand-directed biased agonism at human histamine H3 receptor isoforms across Gαi/o- and β-arrestin2-mediated pathways

The histamine H3 receptor (H3R) is a neurotransmitter receptor that is primarily found in the brain, where it controls the release and synthesis of histamine, as well as the release of other neurotransmitters (e.g. dopamine, serotonin). Notably, 20 H3R isoforms are differentially expressed in the human brain as a consequence of alternative gene splicing. The hH3R-445, -415, -365 and -329 isoforms contain the prototypical GPCR (7TM) structure, yet exhibit deletions in the third intracellular loop, a structural domain that is pivotal for G protein-coupling, signaling and regulation. To date, the physiological relevance underlying the individual and combinatorial function of hH3R isoforms remains poorly understood. Nevertheless, given their significant implication in physiological processes (e.g. cognition, homeostasis) and neurological disorders (e.g. Alzheimer’s and Parkinson’s disease, schizophrenia), widespread targeting of hH3R isoforms by drugs may lead to on-target side effects in brain regions that are unaffected by disease. To this end, isoform- and/or pathway-selective targeting of hH3R isoforms by biased agonists could be of therapeutic relevance for the development of region- and disease-specific drugs. Hence, we have evaluated ligand biased signaling at the hH3R-445, -415, -365 and -329 isoforms across various Gαi/o-mediated (i.e. [35S]GTPγS accumulation, cAMP inhibition, pERK1/2 activation, pAKT T308/S473 activation) and non Gαi/o-mediated (i.e. β-arrestin2 recruitment) endpoints that are relevant to neurological diseases. Our findings indicate that H3R agonists display significantly altered patterns in their degree of ligand bias, in a pathway- and isoform-dependent manner, underlining the significance to investigate GPCRs with multiple isoforms to improve development of selective drugs. Subject categoryNeuropharmacology.

Role of Genetic Variation in Transcriptional Regulatory Elements in Heart Rhythm.

Genetic predisposition to cardiac arrhythmias has been a field of intense investigation. Research initially focused on rare hereditary arrhythmias, but over the last two decades, the role of genetic variation (single nucleotide polymorphisms) in heart rate, rhythm, and arrhythmias has been taken into consideration as well. In particular, genome-wide association studies have identified hundreds of genomic loci associated with quantitative electrocardiographic traits, atrial fibrillation, and less common arrhythmias such as Brugada syndrome. A significant number of associated variants have been found to systematically localize in non-coding regulatory elements that control the tissue-specific and temporal transcription of genes encoding transcription factors, ion channels, and other proteins. However, the identification of causal variants and the mechanism underlying their impact on phenotype has proven difficult due to the complex tissue-specific, time-resolved, condition-dependent, and combinatorial function of regulatory elements, as well as their modest conservation across different model species. In this review, we discuss research efforts aimed at identifying and characterizing-trait-associated variant regulatory elements and the molecular mechanisms underlying their impact on heart rate or rhythm.

Optimizing additive manufacturing path pattern for Ti-6Al-4V thin rods using a combinatorial radial basis function surrogate-assisted genetic algorithm

Path pattern is one of the most significant parameters in the additive manufacturing (AM) process because it influences the specimen's final shape and residual stress distribution. Generally, the optimal path pattern is a computationally expensive, high-dimensional, and black-box permutation optimization problem. In this paper, we propose a combinatorial radial basis function surrogate-assisted genetic algorithm (CRBF-GA) to effectively generate the optimal path pattern by integrating the combinatorial radial basis function surrogate model (CRBF) with the genetic algorithm (GA). To demonstrate the effectiveness of the proposed CRBF-GA, a Ti-6Al-4V thin rod, a component of lattice, is chosen as the research object. Through numerical simulation, experimental verification, and error comparison analysis, the RBF-GA pattern is demonstrated to be the best path pattern among the random forest-assisted evolutionary algorithm (RF-EA), GA, spiral, and zigzag patterns, and it excels in achieving a more precise rod shape compared to the other patterns examined.

The Selection of Basic Functions for a Time-Varying Model of Unmodeled Errors in Medium and Long GNSS Baselines

Unmodeled errors play a critical role in improving the positioning accuracy of Global Navigation Satellite Systems. Few studies have addressed unmodeled errors in medium and long baselines using their time correlation, which is highly beneficial for achieving a precise and real-time solution. However, before tackling unmodeled errors, it is first necessary to determine reasonable basic functions to fit such unmodeled errors. Therefore, we study the selection of basic functions for time-varying unmodeled errors in two positioning modes: estimating atmospheric delays and using an IF combination. We choose three basic functions: polynomials, sinusoidal functions, and combinatorial functions. Fitting experiments and positioning experiments are conducted using the unmodeled error data provided by four baselines ranging from 30 to 220 km. The Root Mean Square Errors fitted by the second order are approximately 2 mm. The corresponding residuals generally converge to 3 mm in about 30 s. After correcting the observations using the fitted unmodeled errors of the second-order polynomial, the positioning results show improvements of about 40% to 80% in all directions. We conclude that the second-order polynomial is the optimal basic function in all two positioning modes.

Quasi-isometry invariance of relative filling functions (with an appendix by Ashot Minasyan)

For a finitely generated group G and collection of subgroups \mathcal{P} , we prove that the relative Dehn function of a pair (G,\mathcal{P}) is invariant under quasi-isometry of pairs. Along the way, we show quasi-isometries of pairs preserve almost malnormality of the collection and fineness of the associated coned-off Cayley graphs. We also prove that for a cocompact simply connected combinatorial G - 2 -complex X with finite edge stabilisers, the combinatorial Dehn function is well defined if and only if the 1 -skeleton of X is fine. We also show that if H is a hyperbolically embedded subgroup of a finitely presented group G , then the relative Dehn function of the pair (G, H) is well defined. In the appendix, it is shown that the Baumslag–Solitar group \mathrm{BS}(k,l) has a well-defined Dehn function with respect to the cyclic subgroup generated by the stable letter if and only if neither k divides l nor l divides k .

Evolutionary Piecewise Developable Approximations

We propose a novel method to compute high-quality piecewise developable approximations for triangular meshes. Central to our approach is an evolutionary genetic algorithm for optimizing the combinatorial and discontinuous fitness function, including the approximation error, the number of patches, the patch boundary length, and the penalty for small patches and narrow regions within patches. The genetic algorithm's operations (i.e., initialization, selection, mutation, and crossover) are explicitly designed to minimize the fitness function. The main challenge is evaluating the fitness function's approximation error as it requires developable patches, which are difficult or time-consuming to obtain. Resolving the challenge is based on a critical observation: the approximation error and the mapping distortion between an input surface and its developable approximation are positively correlated empirically. To efficiently measure distortion without explicitly generating developable shapes, we creatively use conformal mapping techniques. Then, we control the mapping distortion at a relatively low level to achieve high shape similarity in the genetic algorithm. The feasibility and effectiveness of our method are demonstrated over 240 complex examples. Compared with the state-of-the-art methods, our results have much smaller approximation errors, fewer patches, shorter patch boundaries, and fewer small patches and narrow regions.

Issue with Stone-II three phase permeability model, and a novel robust fundamentals-based alternative to it

The objective of this paper is to present a fundamentals-based, consistent with observation, three-phase flow model that avoids the pitfalls of conventional models such as Stone-II or Baker's three-phase permeability models.While investigating the myth of residual oil saturation in SAGD with comparing model generated results against field data, Gupta et al. (2020) highlighted the difficulty in matching observed residual oil saturation in steamed reservoir with Stone-II and Baker's linear models. Though the use of Stone-II model is very popular for three-phase flow across the industry, one issue in the context of gravity drainage is how it appears to counter-intuitively limit the flow of oil when water is present near its irreducible saturation.The current work begins with describing the problem with existing combinatorial methods such as Stone-II, which in turn combine the water-oil, and gas-oil relative permeability curves to yield the oil relative permeability curve in presence of water and gas. Then starting with the fundamentals of laminar flow in capillaries and with successive analogical formulations, it develops expressions that directly yield the relative permeabilities for all three phases. In this it assumes a pore size distribution approximated by functions used earlier in the literature for deriving two-phase relative permeability curves. The outlined approach by-passes the need for having combinatorial functions such as prescribed by Stone or Baker.The model so developed is simple to use, and it avoids the unnatural phenomenon or discrepancy due to a mathematical artefact described in the context of Stone-II above. The model also explains why in the past some researchers have found relative permeability to be a function of temperature. The new model is also amenable to be determined experimentally, instead of being based on an assumed pore-size distribution. In that context it serves as a set of skeletal functions of known dependencies on various saturations, leaving constants to be determined experimentally.The novelty of the work is in development of a three-phase relative permeability model that is based on fundamentals of flow in fine channels and which explains the observed results in the context of flow in porous media better. The significance of the work includes, aside from predicting results more in line with expectations and an explanation of temperature dependent relative permeabilities of oil, a more reliable time dependent residual oleic-phase saturation in the context of gravity-based oil recovery methods.

Boosted local dimensional mutation and all-dimensional neighborhood slime mould algorithm for feature selection

The slime mould algorithm (SMA) is a population-based optimization algorithm that mimics the foraging behavior of slime moulds with a simple structure and few hyperparameters. However, SMA has some limitations, such as getting trapped in local optima when dealing with multimodal or combinatorial functions. To overcome these limitations and improve the algorithm's exploration and exploitation abilities, a local dimensional mutation strategy and an all-dimensional neighborhood search strategy for SMA, known as LASMA, were introduced. To evaluate the performance of LASMA, experiments were conducted on 30 benchmark functions from the CEC2014 competition, and the results were compared with up to 27 peers. The experimental results were then synthesized, and the Wilcoxon signed-rank test was used to evaluate the performance of LASMA. The results showed that LASMA outperformed other algorithms in terms of solution accuracy, stability, and convergence speed, with at least a 53.3% improvement in optimization performance on the 30 tested functions. Moreover, to demonstrate the applicability of LASMA to feature selection problems, a binary version of LASMA called bLASMA was developed and compared with eight binary classification algorithms on 18 datasets from the UCI repository. The experimental results showed that bLASMA not only had faster convergence speed and higher convergence accuracy in handling optimization problems but also performed well in feature selection applications. Thus, LASMA is a promising optimization tool for handling global and binary optimization problems, and its binary version, bLASMA, can be used for feature selection tasks. By addressing the limitations of SMA and improving the algorithm's exploration and exploitation abilities, LASMA provides a robust and effective solution for various optimization problems.

Dual Functions of labial Resolve the Hox Logic of Chelicerate Head Segments.

Despite an abundance of gene expression surveys, comparatively little is known about Hox gene function in Chelicerata. Previous investigations of paralogs of labial (lab) and Deformed (Dfd) in a spider have shown that these play a role in tissue maintenance of the pedipalp segment (lab-1) and in patterning the first walking leg identity (Dfd-1), respectively. However, extrapolations of these data across chelicerates are hindered by the existence of duplicated Hox genes in arachnopulmonates (e.g., spiders and scorpions), which have resulted from an ancient whole genome duplication (WGD) event. Here, we investigated the function of the single-copy ortholog of lab in the harvestman Phalangium opilio, an exemplar of a lineage that was not subject to this WGD. Embryonic RNA interference against lab resulted in two classes of phenotypes: homeotic transformations of pedipalps to chelicerae, as well as reduction and fusion of the pedipalp and leg 1 segments. To test for combinatorial function, we performed a double knockdown of lab and Dfd, which resulted in a homeotic transformation of both pedipalps and the first walking legs into cheliceral identity, whereas the second walking leg is transformed into a pedipalpal identity. Taken together, these results elucidate a model for the Hox logic of head segments in Chelicerata. To substantiate the validity of this model, we performed expression surveys for lab and Dfd paralogs in scorpions and horseshoe crabs. We show that repetition of morphologically similar appendages is correlated with uniform expression levels of the Hox genes lab and Dfd, irrespective of the number of gene copies.

Endoderm-derived islet1-expressing cells differentiate into endothelial cells to function as the vascular HSPC niche in zebrafish.

Endothelial cells (ECs) line blood vessels and serve as a niche for hematopoietic stem and progenitor cells (HSPCs). Recent data point to tissue-specific EC specialization as well as heterogeneity; however, it remains unclear how ECs acquire these properties. Here, by combining live-imaging-based lineage-tracing and single-cell transcriptomics in zebrafish embryos, we identify an unexpected origin for part of the vascular HSPC niche. We find that islet1 (isl1)-expressing cells are the progenitors of the venous ECs that constitute the majority of the HSPC niche. These isl1-expressing cells surprisingly originate from the endoderm and differentiate into ECs in a process dependent on Bmp-Smad signaling and subsequently requiring npas4l (cloche) function. Single-cell RNA sequencing analyses show that isl1-derived ECs express a set of genes that reflect their distinct origin. This study demonstrates that endothelial specialization in the HSPC niche is determined at least in part by the origin of the ECs.

Rapid Morphological Measurement Method of Aortic Dissection Stent Based on Spatial Observation Point Set.

Post-operative stent morphology of aortic dissection patients is important for performing clinical diagnosis and prognostic assessment. However, stent morphologies still need to be manually measured, which is a process prone to errors, high time consumption and difficulty in exploiting inter-data associations. Herein, we propose a method based on the stepwise combination of basic, non-divisible data sets to quickly obtain morphological parameters with high accuracy. We performed the 3D reconstruction of 109 post-operative follow-up CT image data from 26 patients using mimics software. By extracting the spatial locations of the basic morphological observation points on the stent, we defined a basic and non-reducible set of observation points. Further, we implemented a fully automatic stent segmentation and an observation point extraction algorithm. We analyzed the stability and accuracy of the algorithms on a test set containing 8 cases and 408 points. Based on this dataset, we calculated three morphological parameters of different complexity for the different spatial structural features exhibited by the stent. Finally, we compared the two measurement schemes in four aspects: data variability, data stability, statistical process complexity and algorithmic error. The statistical results of the two methods on two low-complexity morphological parameters (spatial position of stent end and vascular stent end-slip volume) show good agreement (n = 26, P1, P2 < 0.001, r1 = 0.992, r2 = 0.988). The statistics of the proposed method for the morphological parameters of medium complexity (proximal support ring feature diameter and distal support ring feature diameter) avoid the errors caused by manual extraction, and the magnitude of this correction to the traditional method does not exceed 4 mm with an average correction of 1.38 mm. Meanwhile, our proposed automatic observation point extraction method has only 2.2% error rate on the test set, and the average spatial distance from the manually marked observation points is 0.73 mm. Thus, the proposed method is able to rapidly and accurately measure the stent circumferential deflection angle, which is highly complex and cannot be measured using traditional methods. The proposed method can significantly reduce the statistical observation time and information processing cost compared to the traditional morphological observation methods. Moreover, when new morphological parameters are required, one can quickly and accurately obtain the target parameters by new "combinatorial functions." Iterative modification of the data set itself is avoided.