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The purpose of this paper is to propose a new aerodynamic parameter estimation methodology based on neural network and output error method, while the output error method is improved based on particle swarm algorithm.

Firstly, the algorithm approximates the dynamic characteristics of aircraft based on feedforward neural network. Neural network is trained by extreme learning machine, and the trained network can predict the aircraft response at (k + 1)th instant given the measured flight data at kth instant. Secondly, particle swarm optimization is used to enhance the convergence of Levenberg–Marquardt (LM) algorithm, and the improved LM method is used to substitute for the Gauss Newton algorithm in output error method. Finally, the trained neural network is combined with the improved output error method to estimate aerodynamic derivatives.

Neither depending on the initial guess of the parameters to be estimated nor requiring numerical integration of the aircraft motion equation, the proposed algorithm can be used for unstable aircraft and is successfully applied to extract aerodynamic derivatives from both simulated and real flight data.

The proposed method requires iterative calculation and can only identify parameters offline.

The proposed method is successfully applied to estimate aircraft aerodynamic parameters and can also be used as a new algorithm for other optimization problems.

In this study, the output error method is improved to reduce the dependence on the initial value of parameters and expand its application scope. It is applied in aircraft aerodynamic parameter identification together with neural network.

This paper aims to fill a research gap by presenting design and 3D printing guidelines and considerations which apply to the development process of patient-specific osteotomy guides for orthopaedic surgery.

Analysis of specific constraints related to patient-specific surgical guides design and 3D printing, lessons learned during the development process of osteotomy guides for orthopaedic surgery, literature review of recent studies in the field and data gathered from questioning a group of surgeons for capturing their preferences in terms of surgical guides design corresponding to precise functionality (materializing cutting trajectories, ensuring unique positioning and stable fixation during surgery), were all used to extract design recommendations.

General design rules for patient-specific osteotomy guides were inferred from examining each step of the design process applied in several case studies in relation to how these guides should be designed to fulfill medical and manufacturing (fused deposition modelling process) constraints. Literature was also investigated for finding other information than the simple reference that the surgical guide is modelled as negative of the bone. It was noticed that literature is focussed more on presenting and discussing medical issues and on assessing surgical outcomes, but hardly at all on guides’ design and design for additive manufacturing aspects. Moreover, surgeons’ opinion was investigated to collect data on different design aspects, as well as interest and willingness to use such 3D-printed surgical guides in training and surgery.

The study contains useful rules and recommendations for engineers involved in designing and 3D printing patient-specific osteotomy guides.

A synergetic approach to identify general rules and recommendations for the patient-specific surgical guides design is presented. Specific constraints are identified and analysed using three case studies of wrist, femur and foot osteotomies. Recent literature is reviewed and surgeons’ opinion is investigated.

This paper aims to study the elasto-dynamic behavior of additively manufactured metallic lattice implants and compare them with human lower-body bone. This work is a step toward producing implants with high similarity of material properties to bone by developing a dynamic design approach.

A suitable topology was selected and admissible design space was established. Implants were fabricated by selective laser melting. Material dynamics, including elastic modulus, damping and natural frequency, were analyzed with experimental and finite element method methodology.

Generally, porosity improves dynamic properties up to an optimum point, which depends on printability, that is, ∼70%. Regarding elastic modulus and natural frequency, it is possible to achieve enough similarity with bone. But, considering damping, the similarity is <23% and <12% with dry and fresh bone, respectively. Damping and strain rate sensitivity increase with porosity. The natural frequency decreases with porosity. Bone ingrowth into lattice implants improves damping substantially while increasing elastic modulus.

Designers, dominantly had quasi-static approach, which considered only elastic modulus. But, the human body is a dynamic structure and experiences dynamic loads; meanwhile, bone, with its damping and natural frequency, regulates dynamic events like shock absorption and elastic wave filtering. Importantly, bone cells sense no load in quasi-static loading and must receive impact loads near their natural frequencies and special accelerations to conduct optimum mechanotransduction. So, it is necessary to develop a dynamic strategy which is comprehensive and describes bone duties.

This study compares the fatigue performance and biocompatibility of as-built and chemically etched Ti-6Al-4V alloys in TPMS-gyroid and stochastic structures fabricated via Powder Bed Fusion Laser Beam (PBF-LB). This study aims to understand how complex lattice structures and post-manufacturing treatment, particularly chemical etching, affect the mechanical properties, surface morphology, fatigue resistance and biocompatibility of these metamaterials for biomedical applications.

Selective Laser Melting (SLM) technology was used to fabricate TPMS-gyroid and Voronoi stochastic designs with three different relative densities (0.2, 0.3 and 0.4) in Ti-6Al-4V ELI alloy. The as-built samples underwent a chemical etching process to enhance surface quality. Mechanical characterization included static compression and dynamic fatigue testing, complemented by scanning electron microscopy (SEM) for surface and failure analysis. The biocompatibility of the samples was assessed through in-vitro cell viability assays using the Alamar Blue assay and cell proliferation studies.

Chemical etching significantly improves the surface morphology, mechanical properties and fatigue resistance of both TPMS-gyroid and stochastic structures. Gyroid structures demonstrated superior mechanical performance and fatigue resistance compared to stochastic structures, with etching providing more pronounced benefits in these aspects. In-vitro biocompatibility tests showed high cytocompatibility for both as-built and etched samples, with etched samples exhibiting notably improved cell viability. The study also highlights the importance of design and post-processing in optimizing the performance of Ti64 components for biomedical applications.

The comparative analysis between as-built and etched conditions, alongside considering different lattice designs, provides valuable information for developing advanced biomedical implants. The demonstration of enhanced fatigue resistance and biocompatibility through etching adds significant value to the field of additive manufacturing, suggesting new avenues for designing and post-processing implantable devices.

The purpose of this paper is to propose a pre-defined performance robust control method for pre-assembly configuration establishment of in-space assembly missions, and collision avoidance is considered during the configuration establishment process.

First, six-degrees-of-freedom error kinematic and dynamic models of relative translational and rotational motion between transportation systems are developed. Second, the prescribed transient-state performance bounds of tracking errors are designed. In addition, based on the backstepping, combining the pre-defined performance control method with a robust control method, a pre-defined performance robust controller is designed.

By designing prescribed transient-state performance bounds of tracking errors to guarantee that there is no overshoot, collision-avoidance can be achieved. Combining the pre-defined performance control method with a robust control method, robustness to disturbance is guaranteed.

This paper proposed a pre-defined performance robust control method. Simulation results demonstrate that the proposed controller can achieve a pre-assembly configuration establishment with collision avoidance in the existence of external disturbances.

This bibliography is offered as a practical guide to published papers, conference proceedings papers and theses/dissertations on the finite element (FE) and boundary element (BE) applications in different fields of biomechanics between 1976 and 1991. The aim of this paper is to help the users of FE and BE techniques to get better value from a large collection of papers on the subjects. Categories in biomechanics included in this survey are: orthopaedic mechanics, dental mechanics, cardiovascular mechanics, soft tissue mechanics, biological flow, impact injury, and other fields of applications. More than 900 references are listed.

Most existing methods for concrete crack detection are based on deep learning techniques such as convolutional neural networks. However, these models, due to their large memory footprint, high power consumption and insufficient feature extraction capabilities, face challenges in mobile applications. To address these issues, this paper proposes a lightweight spiking neural network detection model.

This model achieves fast and accurate crack detection. Firstly, the Gabor-Spiking (GS) module preprocesses input images, extracting texture features and edge features of crack images through Gabor filter convolution modules and spiking convolution modules, respectively. Next, the multiscale residual (MR) module is designed, composed of convolutional layers and residual modules of various scales, to process the fused features and perform crack detection.

Experimental results demonstrate that the model’s size can be reduced to 4.6 MB, achieving accuracy improvements to 87.3 and 96.4% on the SDNET and OCD datasets, respectively.

This paper proposes a lightweight spiking neural network detection model based on the GS module for edge texture feature fusion and the MR module for crack detection.

This paper explores how the existence of a second-hand market can affect remanufacturing decisions for durable goods in the presence of patent protection.

The authors construct a dynamic decision model between a durable goods original manufacturer and a durable goods remanufacturer considering the characteristics of the multi-cycle uses of new durable goods and remanufactured durable goods.

The results show that (1) the second-hand market compresses the cost space of a durable goods original manufacturer and a remanufacturer; (2) when the second-hand market exists, the optimal pricing of new durable goods is reduced, the optimal pricing of remanufactured durable goods is increased and the patent cost of each unit of durable goods increases and (3) the presence of the second-hand market will increase the original manufacturer's and remanufacturer's profits.

The research conclusion has certain reference value for the production strategy selection of each enterprise in the process of patented product remanufacturing and the government's fiscal policy formulation at each stage of the remanufacturing industry's development.

The purpose of this study is to explore how the development of digital trade can provide new development prospects to China's foreign trade under the background of the gradual expansion of China's digital economy and the further release of policy dividends.

Using the methods of literature collection and induction, combined with traditional trade theory, this paper analyzes the characteristics and challenges of digital trade under the background of the digital economy.

The findings reveal that China's digital trade development still faces some risks, such as the containment of China's core technology, digital security and unbalanced development among regions. Considering these risks, China should break through core technical problem, participate in the formulation of international rules to ensure data security, give priority to the development of service trade and improve the unbalanced development of digital trade.

By analyzing the development status and characteristics of the digital economy and digital trade, this paper summarizes the challenges and comparative advantages faced by China's digital trade, and puts forward corresponding suggestions. These suggestions will allow China to take advantage of its rapid digital economy development and occupy a leading position in global digital trade.

This paper creatively expounds on the new development direction of digital trade from the perspective of comparative advantage and risks, and provides some suggestions to expedite China's digital trade development.