Multiple Weight Research Articles

GOG-MBSHO: multi-strategy fusion binary sea-horse optimizer with Gaussian transfer function for feature selection of cancer gene expression data

Cancer gene expression data has the characteristics of high-dimensional, multi-text and multi-classification. The problem of cancer subtype diagnosis can be solved by selecting the most representative and predictive genes from a large number of gene expression data. Feature selection technology can effectively reduce the dimension of data, which helps analyze the information on cancer gene expression data. A multi-strategy fusion binary sea-horse optimizer based on Gaussian transfer function (GOG-MBSHO) is proposed to solve the feature selection problem of cancer gene expression data. Firstly, the multi-strategy includes golden sine strategy, hippo escape strategy and multiple inertia weight strategies. The sea-horse optimizer with the golden sine strategy does not disrupt the structure of the original algorithm. Embedding the golden sine strategy within the spiral motion of the sea-horse optimizer enhances the movement of the algorithm and improves its global exploration and local exploitation capabilities. The hippo escape strategy is introduced for random selection, which avoids the algorithm from falling into local optima, increases the search diversity, and improves the optimization accuracy of the algorithm. The advantage of multiple inertial weight strategies is that dynamic exploitation and exploration can be carried out to accelerate the convergence speed and improve the performance of the algorithm. Then, the effectiveness of multi-strategy fusion was demonstrated by 15 UCI datasets. The simulation results show that the proposed Gaussian transfer function is better than the commonly used S-type and V-type transfer functions, which can improve the classification accuracy, effectively reduce the number of features, and obtain better fitness value. Finally, comparing with other binary swarm intelligent optimization algorithms on 15 cancer gene expression datasets, it is proved that the proposed GOG1-MBSHO has great advantages in the feature selection of cancer gene expression data.

Improving cardiovascular risk stratification with ECG-predicted risk factors in primary prevention

Abstract Background Cardiovascular risk- and screening scores guide clinical practice. Built upon simple risk factors, they could benefit from information on actual, subclinical cardiac changes. The ECG provides proxies for various cardiac states, which can be detected using machine learning. We systematically assess the added value of ECG-based cardiovascular risk factor estimates for six outcomes in two population-based cohorts: Study of Health in Pomerania (SHIP-Start) and UK Biobank (UKB). Methods We employed convolutional neural networks to analyze ECGs from the Hamburg City Health Study (HCHS) for the assessment of multiple risk factors, including age, weight, diabetes, atrial fibrillation (AF) and heart failure. Cox models were fitted on UKB and SHIP to predict 5 and 15 year time-to-event. Endpoints were death, cardiovascular death, AF, stroke, heart failure and myocardial infarction. Covariates were sex, age, body mass index, diabetes, hypertension and smoking (Cox-RF), ECG-predicted risk factors (Cox-AI), and a combination (Cox-RF/AI). Stratified Monte Carlo cross-validation was used as validation and groups of C-indices were compared using Mann-Whitney U tests. Results The study included 9103 HCHS participants (mean age 62.0±8.4 years, 51% women), 3546 SHIP participants (age 49.7±16.4, 51% women), and 32828 UKB participants (age 64±7.6 years, 52% women). Cox-AI performed comparable or better than Cox-RF for AF (SHIP: 0.88 vs. 0.84, UKB: 0.71 vs. 0.72) and heart failure (SHIP: 0.8 vs. 0.79, UKB: 0.76 vs. 0.75), but was less strong for the other outcomes. The combination of information on cardiovascular risk factors and ECG information (Cox-RF/AI) significantly improved the predictive ability compared to risk factors alone (Cox-RF) for AF (SHIP: 0.89 vs. 0.84, UKB: 0.76 vs. 0.72), cardiovascular death (SHIP: 0.90 vs. 0.89, UKB: 0.75 vs. 0.74) and heart failure (SHIP: 0.81 vs. 0.79, UKB: 0.80 vs. 0.75). Discussion ECG-based risk factors complement risk stratification by incorporating nuanced information on cardiac function and structure, improving risk prediction and thus possibly clinical decision making and patient outcomes.

Chemotherapeutic Drug Delivery Nanoplatform Development: From Physicochemical to Preclinical Evaluation

Through this study, the synergistic behavior of small-molecular-weight, amphiphilic surfactant molecules and the triblock copolymer Pluronic 188 was extensively evaluated based on their ability to formulate nanocarriers with novel properties for the delivery of class II and IV (biopharmaceutical classification system) chemotherapeutic compounds. The combination of four different surfactants at multiple weight ratios and twelve initially formulated nanosystems resulted in four hybrid delivery platforms, which were further studied in terms of multiple physicochemical characteristics, as well as their stability in protein-rich media (fetal bovine serum/phosphate-buffer saline). Finally, we obtained a single final nanoformulation that exhibited a high loading capacity (%EE ≥ 75%) and a sustained drug release profile under physiological conditions (model drug methotrexate), without altering the original physicochemical characteristics of the carrier. With a mean hydrodynamic radius (Rh) of less than 70 nm, a polydispersity index of 0.219, and no protein complexation, the system is a suitable candidate for in vivo, intravenous, and/or intramuscular administration. The cytotoxicity and genotoxicity of both loaded and unloaded carriers were evaluated through the examination of the upregulation or downregulation of apoptosis-related pathways. Multiple conventional 2D and 3D spheroidal conformations were used for these assessments, including HEK293, HCT-116, and MCF-7 cell lines, the results of which stressed the safety and biocompatibility of the empty nanocarrier. Additionally, experiments on Caenorhabditis elegans were conducted to evaluate the system’s in vivo toxicity, focusing on developmental stages, egg-laying behavior, and locomotion. Nanosystems studied in terms of chemotherapeutic encapsulation have mostly focused on the physiochemical aspect of the development of such novel delivery platforms, with only few exceptions proceeding step-by-step from cellular 2D to 3D to in vivo experimentation. The present study offers a holistic view of the behavior of such a novel system, advancing our understanding of the capabilities of polymeric/surfactant-based nanodelivery platforms.

Transactive energy management for CIES considering tri-level hybrid game-theoretic approaches with multiple weight allocation factors

In city integrated energy systems, to reduce the costs of energy production and usage, it is extremely important for different entities to design proper energy management approaches since they have diverse behaviours and benefits. The hierarchy of the system is naturally divided into three layers, and hybrid game theories are employed on the basis of the vertical master–slave and horizontal equal cooperation characteristics among energy providers, aggregators, and users. First, to reflect the interactions between different levels' entities, Stackelberg game theory is adopted vertically, and the model complexity is lessened by the KKT conditions. Then, for energy providers, the asymmetric bargaining game is improved by combining multiple weight allocation factors, which can help reduce renewable energy fluctuations and encourage the consumption of clean energy to reduce the carbon emissions of the system. Also, the results show that the proposed method can enhance the utility of renewable energy generation units by approximately 64 % to 69 %. Finally, the optimality and effectiveness of the proposed game theoretic approaches are verified in case studies and in strict mathematical proofs. In addition, the impact mechanisms on the demand response from the perspectives of the user load and price sensitivity are analysed. To explore pricing methods that are more suitable for user aggregators, two pricing strategies are designed for the user aggregator and compared in terms of computation time and revenue. The results show that a unified pricing strategy can increase the aggregator's revenue by 1.24 % and reduce the computation time by 24.47 %.

A retrospective cohort analysis of factors influencing continuous antibiotic therapy with ampicillin

AimsThere are limited data on ampicillin/sulbactam, both for continuous infusion and for use in critically ill patients. We aimed to identify factors that help predict ampicillin plasma levels during continuous antibiotic therapy in intensive care patients. Main methodsWe retrospectively reviewed and retrieved a large dataset of patients who received continuous ampicillin infusion with therapeutic drug monitoring between 2015 and 2022. Patients initially received standard dosing (single shot of 2/1 g followed by continuous infusion of 6/3 g ampicillin/sulbactam per day), which was then adjusted based on the results of regular therapeutic drug monitoring and according to a target range of 30–60 mg/l (equivalent to four to eight times the minimum inhibitory concentration of ampicillin for Enterobacterales). Main results466 measurements from 225 patients (152 male, mean age 61 years) were analyzed. Initial measurements of ampicillin plasma levels were below the predefined optimal therapeutic range in 50 %, within the range in 30 % and above the range in 20 %. Target attainment increased to 70 % by the 4th measurement. There was a significant negative correlation between ampicillin plasma levels and estimated glomerular filtration rate (eGFR) (r = −0.74; p < 0.001) and, to a lesser extent, with height (r = −0.31; p < 0.001). Based on multiple linear regression, eGFR and body weight or height were the factors accounting for 63 % of the variability in the data. SignificanceTo optimise target achievement, ampicillin dosing in critically ill patients requires a personalized approach based on renal function. Importantly, patients with normal or augmented eGFR require higher standard doses of ampicillin/sulbactam.

Topology Identification for Networked Piecewise-smooth Systems With Multiple Weight Couplings Based a Novel Fixed-time Synchronization Approach

Topology Identification for Networked Piecewise-smooth Systems With Multiple Weight Couplings Based a Novel Fixed-time Synchronization Approach

Combining the aggregated forecasts: An efficient method for improving accuracy by stacking multiple weighting models.

Combining the aggregated forecasts: An efficient method for improving accuracy by stacking multiple weighting models.

Static Scheduling of Weight Programming for DNN Acceleration with Resource Constrained PIM

Most existing architectural studies on ReRAM-based processing-in-memory (PIM) DNN accelerators assume that all weights of the DNN can be mapped to the crossbar at once. However, these studies are over-idealized. ReRAM crossbar resources for calculation are limited because of technological limitations, so multiple weight mapping procedures are required during the inference process. In this article, we propose a static scheduling framework which generates the mapping between DNN weights and ReRAM cells with minimum runtime weight programming cost. We first build a ReRAM crossbar programming latency model by simultaneously considering the DNN weight patterns, ReRAM programming operations, and PIM architecture characteristics. Then, the model is used in the searching process to obtain an optimized weight-to-OU mapping table with minimum online programming latency. Finally, an OU scheduler is used to coordinate the activation sequences of OUs in the crossbars to perform the inference computation correctly. Evaluation results show the proposed framework significantly reduces the weight programming overhead and the overall inference latency for various DNN models with different input datasets.

Students' approaches to developing scientific communication skills.

Science communication is a core skill for undergraduate science students to acquire in preparation for their future careers, but studies show that this skill is underdeveloped in science graduates. The aim of this study was to discover the resources and approaches undergraduate students use to effectively develop their science communication skills and how the use of these methods relates to academic performance on a communication task. Undergraduate students undertaking a second-year biomedical science course (n = 490) were asked which approaches and resources they used to aid the development of their science communication skills, and the frequency of their responses was correlated against their laboratory report mark, using multiple regression and relative weights analysis. Students' (n = 453) use of Communication Learning in Practice for Scientists (CLIPS; an open-access interactive website on science communication), resources provided by the university, interactions with university teaching staff, and engagement with the scientific literature significantly predicted the laboratory report mark. Students enrolled in a blended format or in remote online learning only, and in different programs, performed comparably in the written report and used similar approaches and resources, other than remote students reporting more use of other online resources and students in blended learning engaging more with university resources. Together, these findings provide insight into which strategies are most helpful for undergraduate students to engage with to improve their scientific communication skills. The findings highlight that the provision of well-designed interactive communication resources, guided assessment resources, and opportunities to engage with teaching staff can assist in the development of science communication skills.NEW & NOTEWORTHY This study identifies the approaches and resources that undergraduate science students use to develop their science communication skills. It reveals which of these approaches and resources predict improved academic performance in a written science communication assessment task. The findings point to the importance of explicit guidance, and engagement with teaching staff, in advancing the development of science communication skills.

Abdominal MR Multitasking for radiotherapy treatment planning: A motion-resolved and multicontrast 3D imaging approach.

Radiotherapy treatment planning (RTP) using MR has been used increasingly for the abdominal site. Multiple contrast weightings and motion-resolved imaging are desired for accurate delineation of the target and various organs-at-risk and patient-tailored planning. Current MR protocols achieve these through multiple scans with distinct contrast and variable respiratory motion management strategies and acquisition parameters, leading to a complex and inaccurate planning process. This study presents a standalone MR Multitasking (MT)-based technique to produce volumetric, motion-resolved, multicontrast images for abdominal radiotherapy treatment planning. The MT technique resolves motion and provides a wide range of contrast weightings by repeating a magnetization-prepared (saturation recovery and T2 preparations) spoiled gradient-echo readout series and adopting the MT image reconstruction framework. The performance of the technique was assessed through digital phantom simulations and in vivo studies of both healthy volunteers and patients with liver tumors. In the digital phantom study, the MT technique presented structural details and motion in excellent agreement with the digital ground truth. The in vivo studies showed that the motion range was highly correlated (R2 = 0.82) between MT and 2D cine imaging. MT allowed for a flexible contrast-weighting selection for better visualization. Initial clinical testing with interobserver analysis demonstrated acceptable target delineation quality (Dice coefficient = 0.85 ± 0.05, Hausdorff distance = 3.3 ± 0.72 mm). The developed MT-based, abdomen-dedicated technique is capable of providing motion-resolved, multicontrast volumetric images in a single scan, which may facilitate abdominal radiotherapy treatment planning.

Revisiting Winnow: A modified online feature selection algorithm for efficient binary classification

AbstractWinnow is an efficient binary classification algorithm that effectively learns from data even in the presence of a large number of irrelevant attributes. It is specifically designed for online learning scenarios. Unlike the Perceptron algorithm, Winnow employs a multiplicative weight update function, which leads to fewer mistakes and faster convergence. However, the original Winnow algorithm has several limitations. They include, it only works on binary data, and the weight updates are constant and do not depend on the input features. In this article, we propose a modified version of the Winnow algorithm that addresses these limitations. The proposed algorithm is capable of handling real‐valued data, updates the learning function based on the input feature vector. To evaluate the performance of our proposed algorithm, we compare it with seven existing variants of the Winnow algorithm on datasets of varying sizes. We employ various evaluation metrics and parameters to assess and compare the performance of the algorithms. The experimental results demonstrate that our proposed algorithm outperforms all the other algorithms used for comparison, highlighting its effectiveness in classification tasks.

Experimental analysis of enhanced finite set model predictive control and direct torque control in SRM drives for torque ripple reduction

The magnet-less switched reluctance motor (SRM) speed-torque characteristics are ideally suited for traction motor drive characteristics and its advantage to minimize the overall cost of on-road EVs. The main drawbacks are torque and flux ripple, which have produced high in low-speed operation. However, the emerging direct torque control (DTC) operated magnitude flux and torque estimation with voltage vectors (VVs) gives high torque ripples due to the selection of effective switching states and sector partition accuracy. On the other hand, the existing model predictive control (MPC) with multiple objective and optimization weighting factors produces high torque ripples due to the system dynamics and constraints. Therefore, existing DTC and MPC can result in high torque ripples. This paper proposed a finite set (FS)-MPC with a single cost function objective without weighting factor: the predicted torque considered to evaluate VVs to minimize the ripples further. The selected optimal VV minimizes the SRM drive torque and flux ripples in steady and dynamic state behaviour. The classical DTC and proposed model were developed, and simulation results were verified using MATLAB/Simulink. The proposed model operated in SRM drives experimental results to prove the effective minimization of torque and flux ripples compared to the existing DTC.

Rotation Error Prediction of CNC Spindle Based on Short-Time Fourier Transform of Vibration Sensor Signals and Improved Weighted Residual Network.

The spindle rotation error of computer numerical control (CNC) equipment directly reflects the machining quality of the workpiece and is a key indicator reflecting the performance and reliability of CNC equipment. Existing rotation error prediction methods do not consider the importance of different sensor data. This study developed an adaptive weighted deep residual network (ResNet) for predicting spindle rotation errors, thereby establishing accurate mapping between easily obtainable vibration information and difficult-to-obtain rotation errors. Firstly, multi-sensor data are collected by a vibration sensor, and Short-time Fourier Transform (STFT) is adopted to extract the feature information in the original data. Then, an adaptive feature recalibration unit with residual connection is constructed based on the attention weighting operation. By stacking multiple residual blocks and attention weighting units, the data of different channels are adaptively weighted to highlight important information and suppress redundancy information. The weight visualization results indicate that the adaptive weighted ResNet (AWResNet) can learn a set of weights for channel recalibration. The comparison results indicate that AWResNet has higher prediction accuracy than other deep learning models and can be used for spindle rotation error prediction.

The Impact of Weight Cycling on Health and Obesity.

Obesity is a systemic and chronic inflammation, which seriously endangers people's health. People tend to diet to control weight, and the short-term effect of dieting in losing weight is significant, but the prognosis is limited. With weight loss and recovery occurring frequently, people focus on weight cycling. The effect of weight cycling on a certain tissue of the body also has different conclusions. Therefore, this article systematically reviews the effects of body weight cycling on the body and finds that multiple weight cycling (1) increased fat deposition in central areas, lean mass decreased in weight loss period, and fat mass increased in weight recovery period, which harms body composition and skeletal muscle mass; (2) enhanced the inflammatory response of adipose tissue, macrophages infiltrated into adipose tissue, and increased the production of pro-inflammatory mediators in adipocytes; (3) blood glucose concentration mutation and hyperinsulinemia caused the increase or decrease in pancreatic β-cell population, which makes β-cell fatigue and leads to β-cell failure; (4) resulted in additional burden on the cardiovascular system because of cardiovascular rick escalation. Physical activity combined with calorie restriction can effectively reduce metabolic disease and chronic inflammation, alleviating the adverse effects of weight cycling on the body.

Maternal pre-pregnancy BMI influences the associations between bisphenol and phthalate exposures and maternal weight changes and fat accumulation

BackgroundBisphenols and phthalates are two classes of endocrine-disrupting chemicals (EDCs) thought to influence weight and adiposity. Limited research has investigated their influence on maternal weight changes, and no prior work has examined maternal fat mass. We examined the associations between exposure to these chemicals during pregnancy and multiple maternal weight and fat mass outcomes. MethodsThis study included a sample of 318 women enrolled in a Canadian prospective pregnancy cohort. Second trimester urinary concentrations of 2 bisphenols and 12 phthalate metabolites were quantified. Self-reported and measured maternal weights and measured skinfold thicknesses were used to calculate gestational weight gain, 3-months and 3- to 5-years postpartum weight retention, late pregnancy fat mass gain, total postpartum fat mass loss, and late postpartum fat mass retention. Adjusted robust regressions examined associations between chemicals and outcomes in the entire study population and sub-groups stratified by pre-pregnancy body mass index (BMI). Bayesian kernel machine regression examined chemical mixture effects. ResultsAmong women with underweight or normal pre-pregnancy BMIs, MBzP was negatively associated with weight retention at 3- to 5-years postpartum (B = −0.04, 95%CI: −0.07, −0.01). Among women with overweight or obese pre-pregnancy BMIs, MEHP and MMP were positively associated with weight retention at 3-months and 3- to 5-years postpartum, respectively (B's = 0.12 to 0.63, 95%CIs: 0.02, 1.07). DEHP metabolites and MCNP were positively associated with late pregnancy fat mass gain and late postpartum fat mass retention (B's = 0.04 to 0.18, 95%CIs: 0.001, 0.32). Further, the mixture of EDCs was positively associated with late pregnancy fat mass gain. ConclusionIn this cohort, pre-pregnancy BMI was a key determinant of the associations between second trimester exposure to bisphenols and phthalates and maternal weight changes and fat accumulation. Investigations of underlying physiological mechanisms, windows of susceptibility, and impacts on maternal and infant health are needed.

Enhancing Broiler Weight Estimation through Gaussian Kernel Density Estimation Modeling

The management of individual weights in broiler farming is not only crucial for increasing farm income but also directly linked to the revenue growth of integrated broiler companies, necessitating prompt resolution. This paper proposes a model to estimate daily average broiler weights using time and weight data collected through scales. In the proposed model, a method of self-adjusting weights in the bandwidth calculation formula is employed, and the daily average weight representative value is estimated using KDE. The focus of this study is to contribute to the individual weight management of broilers by intensively researching daily fluctuations in average broiler weight. To address this, weight and time data are collected and preprocessed through scales. The Gaussian kernel density estimation model proposed in this paper aims to estimate the representative value of the daily average weight of a single broiler using statistical estimation methods, allowing for self-adjustment of bandwidth values. When applied to the dataset collected through scales, the proposed Gaussian kernel density estimation model with self-adjustable bandwidth values confirmed that the estimated daily weight did not deviate beyond the error range of ±50 g compared with the actual measured values. The next step of this study is to systematically understand the impact of the broiler environment on weight for sustainable management strategies for broiler demand, derive optimal rearing conditions for each farm by combining location and weight data, and develop a model for predicting daily average weight values. The ultimate goal is to develop an artificial intelligence model suitable for weight management systems by utilizing the estimated daily average weight of a single broiler even in the presence of error data collected from multiple weight measurements, enabling more efficient automatic measurement of broiler weight and supporting both farms and broiler demand.

Diabetes and Stroke: Impact of Novel Therapies for the Treatment of Type 2 Diabetes Mellitus.

Type 2 diabetes mellitus (T2DM) is a significant risk factor for stroke. Nevertheless, the evidence supporting stringent glycemic control to reduce macrovascular complications, particularly stroke, is not as clear as for microvascular complications. Presently, risk reduction strategies are based on controlling multiple risk factors, including hypertension, dyslipidemia, glycemia, smoking, and weight. Since 2008, new pharmacological therapies for treating T2DM have been required to undergo trials to ensure their cardiovascular safety. Remarkably, several novel therapies have exhibited protective effects against the combined endpoint of major cardiovascular events. Evidence from these trials, with stroke as a secondary endpoint, along with real-world data, suggests potential benefits in stroke prevention, particularly with glucagon-like peptide 1 receptor agonists. Conversely, the data on sodium-glucose cotransporter type 2 inhibitors remains more controversial. Dipeptidyl peptidase 4 inhibitors appear neutral in stroke prevention. More recent pharmacological therapies still lack significant data on this particular outcome. This article provides a comprehensive review of the evidence on the most recent T2DM therapies for stroke prevention and their impact on clinical practice.

Alternative weighting schemes for fine‐tuned extended similarity indices

AbstractExtended similarity indices (i.e., generalization of pairwise similarity) have recently gained importance because of their simplicity, fast computation, and superiority in tasks like diversity picking. However, they operate with several meta parameters that should be optimized. Earlier, we extended the binary similarity indices to “discrete non‐binary” and “continuous” data; now we continue with introducing and comparing multiple weighting functions. As a case study, the similarity of CYP enzyme inhibitors (4016 molecules after curation) was characterized by their extended similarities, based on 2D descriptors, MACCS and Morgan fingerprints. A statistical workflow based on sum of ranking differences (SRD) and analysis of variance (ANOVA) was used for finding the optimal weight function(s). Overall, the best weighting function is the fraction (“frac”), which corresponds to the principle of parsimony. Optimal extended similarity indices were also found, and their differences are revealed across different data sets. We intend this work to be a guideline for users of extended similarity indices regarding the various weighting options available. Source code for the calculations is available at https://github.com/mqcomplab/MultipleComparisons.

Well-posedness of stochastic multi-weighted complex networks with regime-switching diffusions

In this paper, a stochastic multi-weighted complex network with regime-switching diffusions (SMCNRSD) is considered. Here, the jump and the diffusion processes are interdependent, which generalizes the most existing network model. In the study of stochastic multi-weighted complex networks, stability analysis is an important topic. But so far, some work on stability requires stochastic multi-weighted complex networks to satisfy the global linear growth condition, which cannot be applied to some high-order or nonlinear networks. Therefore, one of the research contents of this study is to obtain the global existence and uniqueness of SMCNRSD under local conditions. Besides, the SMCNRSD usually does not stabilize spontaneously for reasons such as stochastic disturbance, multiple weights and nonlinear coupling. To stabilize SMCNRSD, periodic intermittent control is introduced to obtain the stability criterion. In application, stochastic multi-weighted Chua’s circuits with regime-switching diffusions are taken into account. The corresponding existence and uniqueness as well as stability criteria are given under local conditions. To further illustrate the validity of theoretical results, numerical simulations are given.

Strengthening artificial muscle of oil-water composite network by nanotechnology

Artificial muscle, as a new technology, has a broad application prospect and market in the future, which is more effectively applied to the medical repair of human muscle damage, enhance the strength performance of human muscle, and enrich the function of artificial robots. Nanotechnology is also a high-precision technology that can be added to artificial muscles to make expansion and contraction more reliable. Currently, nanotechnology is constantly innovating in artificial muscles. There have been examples of using artificial muscles to add touch systems for real-time interaction and temperature changes, as well as combining sacrificial coordination keys with mechanical training processes to allow artificial muscles to bear more than their own multiple weights. Carbon catheters using nanotechnology have been shown to contract by as much as 20%. However, while focusing on enhancing the performance of artificial muscles, the bionic ability of the material itself has been neglected, and the elasticity and stretch of the muscle is a major difficulty. Therefore, there are still some shortcomings and areas that can be improved in this field. In this paper, we discuss how to use carbon conduit nanotechnology to strengthen artificial muscles, which provides effective reference materials for human beings to better understand the application of artificial muscle nanotechnology.