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Explorative Binary Gray Wolf Optimizer with Quadratic Interpolation for Feature Selection. 探索性二元灰狼优化器与二次插值特征选择。
IF 3.4 3区 医学 Q1 ENGINEERING, MULTIDISCIPLINARY Pub Date : 2024-10-21 DOI: 10.3390/biomimetics9100648
Yijie Zhang, Yuhang Cai

The high dimensionality of large datasets can severely impact the data mining process. Therefore, feature selection becomes an essential preprocessing stage, aimed at reducing the dimensionality of the dataset by selecting the most informative features while improving classification accuracy. This paper proposes a novel binary Gray Wolf Optimization algorithm to address the feature selection problem in classification tasks. Firstly, the historical optimal position of the search agent helps explore more promising areas. Therefore, by linearly combining the best positions of the search agents, the algorithm's exploration capability is increased, thus enhancing its global development ability. Secondly, the novel quadratic interpolation technique, which integrates population diversity with local exploitation, helps improve both the diversity of the population and the convergence accuracy. Thirdly, chaotic perturbations (small random fluctuations) applied to the convergence factor during the exploration phase further help avoid premature convergence and promote exploration of the search space. Finally, a novel transfer function processes feature information differently at various stages, enabling the algorithm to search and optimize effectively in the binary space, thereby selecting the optimal feature subset. The proposed method employs a k-nearest neighbor classifier and evaluates performance through 10-fold cross-validation across 32 datasets. Experimental results, compared with other advanced algorithms, demonstrate the effectiveness of the proposed algorithm.

大型数据集的高维度会严重影响数据挖掘过程。因此,特征选择成为一个必不可少的预处理阶段,目的是通过选择信息量最大的特征来降低数据集的维度,同时提高分类准确率。本文提出了一种新颖的二元灰狼优化算法来解决分类任务中的特征选择问题。首先,搜索代理的历史最优位置有助于探索更有前景的领域。因此,通过线性组合搜索代理的最佳位置,可以提高算法的探索能力,从而增强其全局开发能力。其次,新颖的二次插值技术将种群多样性与局部开发相结合,有助于提高种群多样性和收敛精度。第三,在探索阶段对收敛因子施加混沌扰动(小的随机波动),有助于避免过早收敛,促进对搜索空间的探索。最后,新颖的转移函数在不同阶段对特征信息进行不同处理,使算法能够在二进制空间中有效搜索和优化,从而选择最佳特征子集。所提出的方法采用了 k 近邻分类器,并通过 32 个数据集的 10 倍交叉验证来评估性能。实验结果与其他先进算法相比,证明了所提算法的有效性。
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引用次数: 0
Path Planning of an Unmanned Aerial Vehicle Based on a Multi-Strategy Improved Pelican Optimization Algorithm. 基于多策略改进鹈鹕优化算法的无人飞行器路径规划
IF 3.4 3区 医学 Q1 ENGINEERING, MULTIDISCIPLINARY Pub Date : 2024-10-21 DOI: 10.3390/biomimetics9100647
Shaoming Qiu, Jikun Dai, Dongsheng Zhao

The UAV path planning algorithm has many applications in urban environments, where an effective algorithm can enhance the efficiency of UAV tasks. The main concept of UAV path planning is to find the optimal flight path while avoiding collisions. This paper transforms the path planning problem into a multi-constraint optimization problem by considering three costs: path length, turning angle, and collision avoidance. A multi-strategy improved POA algorithm (IPOA) is proposed to address this. Specifically, by incorporating the iterative chaotic mapping method with refracted reverse learning strategy, nonlinear inertia weight factors, the Levy flight mechanism, and adaptive t-distribution variation, the convergence accuracy and speed of the POA algorithm are enhanced. In the CEC2022 test functions, IPOA outperformed other algorithms in 69.4% of cases. In the real map simulation experiment, compared to POA, the path length, turning angle, distance to obstacles, and flight time improved by 8.44%, 5.82%, 4.07%, and 9.36%, respectively. Similarly, compared to MPOA, the improvements were 4.09%, 0.76%, 1.85%, and 4.21%, respectively.

无人机路径规划算法在城市环境中有许多应用,有效的算法可以提高无人机执行任务的效率。无人机路径规划的主要概念是在避免碰撞的同时找到最优飞行路径。本文通过考虑路径长度、转弯角度和避免碰撞三个代价,将路径规划问题转化为多约束优化问题。为此,本文提出了一种多策略改进 POA 算法(IPOA)。具体来说,通过结合具有折射反向学习策略的迭代混沌映射法、非线性惯性权重因子、利维飞行机制和自适应 t 分布变化,提高了 POA 算法的收敛精度和速度。在 CEC2022 测试函数中,IPOA 在 69.4% 的情况下优于其他算法。在真实地图仿真实验中,与 POA 相比,路径长度、转弯角度、障碍物距离和飞行时间分别提高了 8.44%、5.82%、4.07% 和 9.36%。同样,与 MPOA 相比,分别提高了 4.09%、0.76%、1.85% 和 4.21%。
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引用次数: 0
Brain-Inspired Architecture for Spiking Neural Networks. 尖峰神经网络的脑启发架构
IF 3.4 3区 医学 Q1 ENGINEERING, MULTIDISCIPLINARY Pub Date : 2024-10-21 DOI: 10.3390/biomimetics9100646
Fengzhen Tang, Junhuai Zhang, Chi Zhang, Lianqing Liu

Spiking neural networks (SNNs), using action potentials (spikes) to represent and transmit information, are more biologically plausible than traditional artificial neural networks. However, most of the existing SNNs require a separate preprocessing step to convert the real-valued input into spikes that are then input to the network for processing. The dissected spike-coding process may result in information loss, leading to degenerated performance. However, the biological neuron system does not perform a separate preprocessing step. Moreover, the nervous system may not have a single pathway with which to respond and process external stimuli but allows multiple circuits to perceive the same stimulus. Inspired by these advantageous aspects of the biological neural system, we propose a self-adaptive encoding spike neural network with parallel architecture. The proposed network integrates the input-encoding process into the spiking neural network architecture via convolutional operations such that the network can accept the real-valued input and automatically transform it into spikes for further processing. Meanwhile, the proposed network contains two identical parallel branches, inspired by the biological nervous system that processes information in both serial and parallel. The experimental results on multiple image classification tasks reveal that the proposed network can obtain competitive performance, suggesting the effectiveness of the proposed architecture.

尖峰神经网络(SNN)使用动作电位(尖峰)来表示和传输信息,与传统的人工神经网络相比,在生物学上更加可信。然而,现有的大多数尖峰神经网络都需要一个单独的预处理步骤,将实值输入转换为尖峰,然后输入网络进行处理。分割的尖峰编码过程可能会造成信息丢失,导致性能下降。然而,生物神经元系统并不执行单独的预处理步骤。此外,神经系统可能没有单一的途径来响应和处理外部刺激,而是允许多个回路感知同一刺激。受生物神经系统这些优势方面的启发,我们提出了一种具有并行结构的自适应编码尖峰神经网络。该网络通过卷积运算将输入编码过程整合到尖峰神经网络架构中,从而使网络能够接受实值输入,并自动将其转换为尖峰信号进行进一步处理。同时,受生物神经系统以串行和并行方式处理信息的启发,所提出的网络包含两个相同的并行分支。在多个图像分类任务上的实验结果表明,所提出的网络可以获得具有竞争力的性能,这表明所提出的架构是有效的。
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引用次数: 0
Performance Comparison of Bio-Inspired Algorithms for Optimizing an ANN-Based MPPT Forecast for PV Systems. 生物启发算法的性能比较,用于优化光伏系统基于 ANN 的 MPPT 预测。
IF 3.4 3区 医学 Q1 ENGINEERING, MULTIDISCIPLINARY Pub Date : 2024-10-21 DOI: 10.3390/biomimetics9100649
Rafael Rojas-Galván, José R García-Martínez, Edson E Cruz-Miguel, José M Álvarez-Alvarado, Juvenal Rodríguez-Resendiz

This study compares bio-inspired optimization algorithms for enhancing an ANN-based Maximum Power Point Tracking (MPPT) forecast system under partial shading conditions in photovoltaic systems. Four algorithms-grey wolf optimizer (GWO), particle swarm optimization (PSO), squirrel search algorithm (SSA), and cuckoo search (CS)-were evaluated, with the dataset augmented by perturbations to simulate shading. The standard ANN performed poorly, with 64 neurons in Layer 1 and 32 in Layer 2 (MSE of 159.9437, MAE of 8.0781). Among the optimized approaches, GWO, with 66 neurons in Layer 1 and 100 in Layer 2, achieved the best prediction accuracy (MSE of 11.9487, MAE of 2.4552) and was computationally efficient (execution time of 1198.99 s). PSO, using 98 neurons in Layer 1 and 100 in Layer 2, minimized MAE (2.1679) but had a slightly longer execution time (1417.80 s). SSA, with the same neuron count as GWO, also performed well (MSE 12.1500, MAE 2.7003) and was the fastest (987.45 s). CS, with 84 neurons in Layer 1 and 74 in Layer 2, was less reliable (MSE 33.7767, MAE 3.8547) and slower (1904.01 s). GWO proved to be the best overall, balancing accuracy and speed. Future real-world applications of this methodology include improving energy efficiency in solar farms under variable weather conditions and optimizing the performance of residential solar panels to reduce energy costs. Further optimization developments could address more complex and larger-scale datasets in real-time, such as integrating renewable energy sources into smart grid systems for better energy distribution.

本研究比较了生物启发优化算法,以增强光伏系统部分遮阳条件下基于 ANN 的最大功率点跟踪(MPPT)预测系统。对四种算法--灰狼优化算法(GWO)、粒子群优化算法(PSO)、松鼠搜索算法(SSA)和布谷鸟搜索算法(CS)--进行了评估,并通过扰动增加数据集以模拟遮光。标准 ANN 的表现不佳,第 1 层有 64 个神经元,第 2 层有 32 个神经元(MSE 为 159.9437,MAE 为 8.0781)。在优化方法中,GWO(第 1 层有 66 个神经元,第 2 层有 100 个神经元)的预测准确率最高(MSE 为 11.9487,MAE 为 2.4552),而且计算效率高(执行时间为 1198.99 秒)。PSO 在第 1 层使用 98 个神经元,在第 2 层使用 100 个神经元,MAE(2.1679)最小,但执行时间稍长(1417.80 秒)。采用与 GWO 相同神经元数的 SSA 也表现出色(MSE 12.1500,MAE 2.7003),而且速度最快(987.45 秒)。CS 第一层有 84 个神经元,第二层有 74 个神经元,其可靠性较低(MSE 为 33.7767,MAE 为 3.8547),速度也较慢(1904.01 秒)。事实证明,GWO 是兼顾准确性和速度的最佳方法。该方法未来在现实世界中的应用包括在多变天气条件下提高太阳能发电场的能源效率,以及优化住宅太阳能电池板的性能以降低能源成本。进一步的优化开发可以实时处理更复杂、更大规模的数据集,例如将可再生能源整合到智能电网系统中,以实现更好的能源分配。
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引用次数: 0
Clinical Applications of Micro/Nanobubble Technology in Neurological Diseases. 微/纳米气泡技术在神经系统疾病中的临床应用。
IF 3.4 3区 医学 Q1 ENGINEERING, MULTIDISCIPLINARY Pub Date : 2024-10-20 DOI: 10.3390/biomimetics9100645
Parth B Patel, Sun Latt, Karan Ravi, Mehdi Razavi

Nanomedicine, leveraging the unique properties of nanoparticles, has revolutionized the diagnosis and treatment of neurological diseases. Among various nanotechnological advancements, ultrasound-mediated drug delivery using micro- and nanobubbles offers promising solutions to overcome the blood-brain barrier (BBB), enhancing the precision and efficacy of therapeutic interventions. This review explores the principles, current clinical applications, challenges, and future directions of ultrasound-mediated drug delivery systems in treating stroke, brain tumors, neurodegenerative diseases, and neuroinflammatory disorders. Additionally, ongoing clinical trials and potential advancements in this field are discussed, providing a comprehensive overview of the impact of nanomedicine on neurological diseases.

纳米医学利用纳米粒子的独特特性,彻底改变了神经疾病的诊断和治疗。在各种纳米技术进展中,使用微气泡和纳米气泡的超声介导给药技术为克服血脑屏障(BBB)、提高治疗干预的精确性和有效性提供了前景广阔的解决方案。本综述探讨了超声介导给药系统治疗中风、脑肿瘤、神经退行性疾病和神经炎症性疾病的原理、当前临床应用、挑战和未来发展方向。此外,还讨论了该领域正在进行的临床试验和潜在的进展,全面概述了纳米医学对神经系统疾病的影响。
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引用次数: 0
Dual-Coated Antireflective Film for Flexible and Robust Multi-Environmental Optoelectronic Applications. 用于灵活、坚固的多环境光电应用的双涂层抗反射薄膜。
IF 3.4 3区 医学 Q1 ENGINEERING, MULTIDISCIPLINARY Pub Date : 2024-10-20 DOI: 10.3390/biomimetics9100644
Hyuk Jae Jang, Jaemin Jeon, Joo Ho Yun, Iqbal Shudha Tasnim, Soyeon Han, Heeyoung Lee, Sungguk An, Seungbeom Kang, Dongyeon Kim, Young Min Song

Artificial antireflective nanostructured surfaces, inspired by moth eyes, effectively reduce optical losses at interfaces, offering significant advantages in enhancing optical performance in various optoelectronic applications, including solar cells, light-emitting diodes, and cameras. However, their limited flexibility and low surface hardness constrain their broader use. In this study, we introduce a universal antireflective film by integrating nanostructures on both sides of a thin polycarbonate film. One side was thinly coated with Al2O3 for its high hardness, enhancing surface durability while maintaining flexibility. The opposite side was coated with SiO2 to optimize antireflective properties, making the film suitable for diverse environments (i.e., air, water, and adhesives). This dual-coating strategy resulted in a mechanically robust and flexible antireflective film with superior optical properties in various conditions. We demonstrated the universal capabilities of our antireflective film via optical simulations and experiments with the fabricated film in different environments.

受飞蛾眼睛的启发,人造抗反射纳米结构表面可有效减少界面上的光学损耗,在提高太阳能电池、发光二极管和照相机等各种光电应用的光学性能方面具有显著优势。然而,它们有限的柔韧性和较低的表面硬度限制了它们的广泛应用。在本研究中,我们通过在聚碳酸酯薄膜的两面集成纳米结构,推出了一种通用型抗反射薄膜。其中一面薄薄地镀上了 Al2O3,因为 Al2O3 具有高硬度,可在保持柔韧性的同时提高表面耐久性。另一面则涂有二氧化硅,以优化抗反射特性,使薄膜适用于各种环境(如空气、水和粘合剂)。这种双涂层策略使抗反射薄膜具有机械坚固性和柔韧性,并在各种条件下具有优异的光学性能。我们通过光学模拟和在不同环境下对所制备薄膜的实验,证明了我们的抗反射薄膜的通用能力。
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引用次数: 0
Peak Identification in Evolutionary Multimodal Optimization: Model, Algorithms, and Metrics. 进化多模态优化中的峰值识别:模型、算法和度量。
IF 3.4 3区 医学 Q1 ENGINEERING, MULTIDISCIPLINARY Pub Date : 2024-10-19 DOI: 10.3390/biomimetics9100643
Yu-Hui Zhang, Zi-Jia Wang

In this paper, we present a two-phase multimodal optimization model designed to efficiently and accurately identify multiple optima. The first phase employs a population-based search algorithm to locate potential optima, while the second phase introduces a novel peak identification (PI) procedure to filter out non-optimal solutions, ensuring that each identified solution represents a distinct optimum. This approach not only enhances the effectiveness of multimodal optimization but also addresses the issue of redundant solutions prevalent in existing algorithms. We propose two PI algorithms: HVPI, which uses a hill-valley approach to distinguish between optima, without requiring prior knowledge of niche radii; and HVPIC, which integrates HVPI with bisecting K-means clustering to reduce the number of fitness evaluations (FEs). The performance of these algorithms was evaluated using the F-measure, a comprehensive metric that accounts for both the accuracy and redundancy in the solution set. Extensive experiments on a suite of benchmark functions and engineering problems demonstrated that our proposed algorithms achieved a high precision and recall, significantly outperforming traditional methods.

在本文中,我们提出了一个两阶段多模式优化模型,旨在高效、准确地识别多个最优解。第一阶段采用基于种群的搜索算法来定位潜在的最优方案,第二阶段则引入新颖的峰值识别(PI)程序来过滤非最优方案,确保每个识别出的方案都代表一个独特的最优方案。这种方法不仅提高了多模式优化的效率,还解决了现有算法中普遍存在的冗余解问题。我们提出了两种 PI 算法:HVPI 和 HVPIC,前者使用山谷法来区分最优解,而无需事先了解利基半径;后者将 HVPI 与 K-means 分叉聚类整合在一起,以减少适配性评估(FE)的数量。这些算法的性能是通过 F-measure 进行评估的,F-measure 是一个综合指标,同时考虑了解决方案集的准确性和冗余性。在一系列基准函数和工程问题上进行的广泛实验表明,我们提出的算法实现了较高的精确度和召回率,明显优于传统方法。
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引用次数: 0
A New Single-Parameter Bees Algorithm. 新的单参数蜜蜂算法
IF 3.4 3区 医学 Q1 ENGINEERING, MULTIDISCIPLINARY Pub Date : 2024-10-18 DOI: 10.3390/biomimetics9100634
Hamid Furkan Suluova, Duc Truong Pham

Based on bee foraging behaviour, the Bees Algorithm (BA) is an optimisation metaheuristic algorithm which has found many applications in both the continuous and combinatorial domains. The original version of the Bees Algorithm has six user-selected parameters: the number of scout bees, the number of high-performing bees, the number of top-performing or "elite" bees, the number of forager bees following the elite bees, the number of forager bees recruited by the other high-performing bees, and the neighbourhood size. These parameters must be chosen with due care, as their values can impact the algorithm's performance, particularly when the problem is complex. However, determining the optimum values for those parameters can be time-consuming for users who are not familiar with the algorithm. This paper presents BA1, a Bees Algorithm with just one parameter. BA1 eliminates the need to specify the numbers of high-performing and elite bees and other associated parameters. Instead, it uses incremental k-means clustering to divide the scout bees into groups. By reducing the required number of parameters, BA1 simplifies the tuning process and increases efficiency. BA1 has been evaluated on 23 benchmark functions in the continuous domain, followed by 12 problems from the TSPLIB in the combinatorial domain. The results show good performance against popular nature-inspired optimisation algorithms on the problems tested.

蜜蜂算法(BA)是一种基于蜜蜂觅食行为的优化元启发式算法,在连续领域和组合领域都有很多应用。蜜蜂算法的原始版本有六个用户选择参数:侦察蜜蜂的数量、高绩效蜜蜂的数量、最高绩效或 "精英 "蜜蜂的数量、跟随精英蜜蜂的觅食蜜蜂的数量、其他高绩效蜜蜂招募的觅食蜜蜂的数量以及邻域大小。这些参数的选择必须慎重,因为它们的值会影响算法的性能,尤其是在问题复杂的情况下。然而,对于不熟悉算法的用户来说,确定这些参数的最佳值可能会很耗时。本文介绍的 BA1 是一种只有一个参数的蜜蜂算法。BA1 无需指定高绩效蜜蜂和精英蜜蜂的数量以及其他相关参数。取而代之的是,它使用增量 K 均值聚类将侦察蜜蜂分成若干组。通过减少所需的参数数量,BA1 简化了调整过程并提高了效率。BA1 在连续域的 23 个基准函数上进行了评估,随后在组合域的 TSPLIB 中对 12 个问题进行了评估。结果表明,在所测试的问题上,与流行的自然启发优化算法相比,BA1 性能良好。
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引用次数: 0
Lightweight Optic Disc and Optic Cup Segmentation Based on MobileNetv3 Convolutional Neural Network. 基于 MobileNetv3 卷积神经网络的轻量级视盘和视杯分割。
IF 3.4 3区 医学 Q1 ENGINEERING, MULTIDISCIPLINARY Pub Date : 2024-10-18 DOI: 10.3390/biomimetics9100637
Yuanqiong Chen, Zhijie Liu, Yujia Meng, Jianfeng Li

Glaucoma represents a significant global contributor to blindness. Accurately segmenting the optic disc (OD) and optic cup (OC) to obtain precise CDR is essential for effective screening. However, existing convolutional neural network (CNN)-based segmentation techniques are often limited by high computational demands and long inference times. This paper proposes an efficient end-to-end method for OD and OC segmentation, utilizing the lightweight MobileNetv3 network as the core feature-extraction module. Our approach combines boundary branches with adversarial learning, to achieve multi-label segmentation of the OD and OC. We validated our proposed approach across three public available datasets: Drishti-GS, RIM-ONE-r3, and REFUGE. The outcomes reveal that the Dice coefficients for the segmentation of OD and OC within these datasets are 0.974/0.900, 0.966/0.875, and 0.962/0.880, respectively. Additionally, our method substantially lowers computational complexity and inference time, thereby enabling efficient and precise segmentation of the optic disc and optic cup.

青光眼是导致全球失明的重要原因。准确分割视盘(OD)和视杯(OC)以获得精确的 CDR 对有效筛查至关重要。然而,现有的基于卷积神经网络(CNN)的分割技术往往受到计算要求高和推理时间长的限制。本文利用轻量级 MobileNetv3 网络作为核心特征提取模块,提出了一种高效的端到端 OD 和 OC 分割方法。我们的方法将边界分支与对抗学习相结合,实现了 OD 和 OC 的多标签分割。我们在三个公开数据集上验证了我们提出的方法:Drishti-GS、RIM-ONE-r3 和 REFUGE。结果显示,在这些数据集中,OD 和 OC 分割的 Dice 系数分别为 0.974/0.900、0.966/0.875 和 0.962/0.880。此外,我们的方法大大降低了计算复杂度和推理时间,从而实现了对视盘和视杯的高效、精确分割。
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引用次数: 0
Improved Biocompatibility in Laser-Polished Implants. 提高激光抛光植入物的生物相容性。
IF 3.4 3区 医学 Q1 ENGINEERING, MULTIDISCIPLINARY Pub Date : 2024-10-18 DOI: 10.3390/biomimetics9100642
Mattew A Olawumi, Francis T Omigbodun, Bankole I Oladapo

This research aims to enhance the surface quality, mechanical properties, and biocompatibility of PEEK (polyether-ether-ketone) biomimetic dental implants through laser polishing. The objective is to improve osseointegration and implant durability by reducing surface roughness, increasing hydrophilicity, and enhancing mechanical strength. The methodology involved fabricating PEEK implants via FDM and applying laser polishing. The significant findings showed a 66.7% reduction in surface roughness, Ra reduced from 2.4 µm to 0.8 µm, and a 25.3% improvement in hydrophilicity, water contact angle decreased from 87° to 65°. Mechanical tests revealed a 6.3% increase in tensile strength (96 MPa to 102 MPa) and a 50% improvement in fatigue resistance (100,000 to 150,000 cycles). The strength analysis result showed a 10% increase in stiffness storage modulus from 1400 MPa to 1500 MPa. Error analysis showed a standard deviation of ±3% across all tests. In conclusion, laser polishing significantly improves the surface, mechanical, and biological performance of PEEK implants, making it a promising approach for advancing biomimetic dental implant technology.

这项研究旨在通过激光抛光提高 PEEK(聚醚醚酮)仿生牙科植入体的表面质量、机械性能和生物相容性。目的是通过降低表面粗糙度、增加亲水性和提高机械强度来改善骨结合和植入物的耐久性。研究方法包括通过 FDM 制造 PEEK 种植体并进行激光抛光。研究结果表明,表面粗糙度降低了 66.7%,Ra 从 2.4 微米降至 0.8 微米;亲水性提高了 25.3%,水接触角从 87°降至 65°。机械测试显示,拉伸强度提高了 6.3%(从 96 兆帕提高到 102 兆帕),抗疲劳性提高了 50%(从 100,000 次循环提高到 150,000 次循环)。强度分析结果显示,刚度存储模量从 1400 兆帕提高到 1500 兆帕,提高了 10%。误差分析表明,所有测试的标准偏差为 ±3%。总之,激光抛光能明显改善 PEEK 种植体的表面、机械和生物性能,使其成为推动生物仿生牙科种植技术发展的一种有前途的方法。
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引用次数: 0
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