The time-optimal path following (OPF) problem is to find a time evolution along a prescribed path in task space with shortest time duration. Numerical solution algorithms rely on an algorithm-specific (usually equidistant) sampling of the path parameter. This does not account for the dynamics in joint space, that is, the actual motion of the robot, however. Moreover, a well-known problem is that large joint velocities are obtained when approaching singularities, even for slow task space motions. This can be avoided by a sampling in joint space, where the path parameter is replaced by the arc length. Such discretization in task space leads to an adaptive refinement according to the nonlinear forward kinematics and guarantees bounded joint velocities. The adaptive refinement is also beneficial for the numerical solution of the problem. It is shown that this yields trajectories with improved continuity compared to an equidistant sampling. The OPF is reformulated as a second-order cone programming and solved numerically. The approach is demonstrated for a 6-DOF industrial robot following various paths in task space.

To obtain the optimal solution for the performance of the turbofan engine using infrared stealth technology, an engine mathematical model with a backward infrared radiation intensity calculation module was established. The effects of infrared suppression measures on the performance of turbofan engines were analysed. Based on the multi-objective particle swarm optimisation (MOPSO) algorithm, the optimal solution for the performance in the cruise state of the reference engine refitted with the infrared radiation suppression module was obtained; Further, through the multiple design points (MDPs) concept, the thermal cycle optimisation design of the turbofan engine was carried out. The results show that the integrated fully shielded guiding strut (IFSGS) with air film cooling had the ideal infrared suppression effect. Compared with the reference engine refitted with infrared radiation suppression module, the engine after cycle optimisation design could obtain better infrared stealth performance.

In the era of rapid product update and intense competition, aesthetic design has been increasingly important in various fields, as aesthetic feelings of customers largely influence their purchase preferences. However, the quantification of aesthetic feeling is still a very subjective process due to vague evaluations. The determination of form parameters according to aesthetics is difficult hitherto. Aesthetic measure recently arises as a prominent tool for this purpose using formulas derived from aesthetic theory. But as revealed by existing studies, it needs to be customized with deterministic and objective methods to be reliable in practice use. To facilitate this application, this paper proposes an evolutionary form design method, integrating aesthetic dimension selection and parameter optimization. After summarizing initial aesthetic dimensions, aesthetic dimension selection based on expert decision-making and particle swarm optimization (PSO) is carried out. With filtered aesthetic dimensions, design parameters are optimized with NSGA-II (non-dominated sorting genetic algorithm). The quality of pareto solutions obtained to be design schemes is assessed by three criteria to conduct sensitivity analysis of cross and mutation probability and population size. Our experiment using bicycle form design shows that the proposed evolutionary form design method can generate numerous and variant aesthetic design schemes rapidly. This is very useful for both product redesign and innovative new product development.

During long-duration spaceflight, astronauts are exposed to various risks including spaceflight-associated neuro-ocular syndrome, which serves as a risk to astronaut vision and a potential physiological barrier to future spaceflight. When considering exploration missions that may expose astronauts to longer periods of microgravity, radiation exposure, and natural aging processes during spaceflight, more severe changes to functional vision may occur. The macula plays a critical role in central vision and disruptions to this key area in the eye may compromise functional vision and mission performance. In this article, we describe the development of a countermeasure technique to digitally suppress monocular central visual distortion with head-mounted display technology. We report early validation studies with this noninvasive countermeasure in individuals with simulated metamorphopsia. When worn by these individuals, this emerging wearable countermeasure technology has demonstrated a suppression of monocular visual distortion. We describe the considerations and further directions of this head-mounted technology for both astronauts and aging individuals on Earth.

Two novel modified montmorillonite (Mnt) components were prepared using Mnt nanoparticles and two surfactants: docosyl-trimethylammonium chloride (BTAC) and sodium dodecyl sulfate (SDS). These modified Mnts were used to remove a carcinogenic and harmful dye, crystal violet (CV), from solution. Optimization and modelling studies of the adsorption of these two modified Mnts were performed using response surface methodology. Four influential variables (concentration of adsorbent, temperature, pH and CV concentration) were studied to obtain the optimum conditions for CV removal. The optimal values of these variables for the two modified Mnts yielded 100% dye-removal efficiency. The optimum conditions for CV adsorption on Mnt-BTAC and Mnt-BTAC-SDS, respectively, are temperatures of 25.00 and 33.29°C, pH values of 9 and 10.1, CV concentrations of 50.00 and 10.44 mg L–1 and adsorbent concentrations of 1.00 and 0.98 g L–1. In equilibrium studies of the two modified Mnts, the Temkin isotherm was selected as an appropriate model, and in kinetic studies of these Mnts, the fractal-like integrated kinetics Langmuir model was found to be the best model. The Mnt-BTAC-SDS component is an affordable adsorbent with high adsorption capacity for CV.

Multimodal digital data registered with wearable biosensors have emerged as highly complementary of clinical pencil-and-paper criteria, offering new insights in ways to detect and diagnose various aspects of Parkinson’s disease (PD). A pressing question is how to combine both the clinical knowledge of PD and the new technology to create interpretable digital biomarkers easily obtainable with off-the-shelf technology. Several challenges concerning disparity in biophysical units, anatomical differences across participants, sensor positioning, and sampling resolution are addressed in this work, along with identification of optimal parameters to automatically differentiate patients with PD from controls. We combine data from a multitude of biosensors registering signals from the central (electroencephalography) and peripheral (magnetometry, kinematics) nervous systems, inclusive of the autonomic nervous system (electrocardiogram), as the participants perform natural tasks requiring different levels of intentional planning and automatic control. We find that magnetometer data during walking, across a variety of amplitude and timing signals, provide optimal separation of PD from neurotypical controls. We conclude that using multimodal signals within the context of actions that bear different levels of intent, can be revealing of features of PD that would scape the naked eye. Further, we add that clinical criteria combined with such optimal digital parameter spaces offer a far more complete picture of PD than using either one of these pieces of data alone.

This article considers the link removal problem in a strongly connected directed network with the goal of minimizing the dominant eigenvalue of the network’s adjacency matrix while maintaining its strong connectivity. Due to the complexity of the problem, this article focuses on computing a suboptimal solution. Furthermore, it is assumed that the knowledge of the overall network topology is not available. This calls for distributed algorithms which rely solely on the local information available to each individual node and information exchange between each node and its neighbors. Two different strategies based on matrix perturbation analysis are presented, namely simultaneous and iterative link removal strategies. Key ingredients in implementing both strategies include novel distributed algorithms for estimating the dominant eigenvectors of an adjacency matrix and for verifying strong connectivity of a directed network under link removal. It is shown via numerical simulations on different type of networks that in general the iterative link removal strategy yields a better suboptimal solution. However, it comes at a price of higher communication cost in comparison to the simultaneous link removal strategy.

Earth-contact mechanism (ECM), a type of mechanism to keep the system in contact with the earth and to move with the terrain changes. This paper uses the virtual equivalent parallel mechanism (VEPM) to convert the terrain data into the kinematical variables of the moving platform in the VEPM, and further analyzes the performance of the VEPM at each terrain point. Then, the comprehensive performance of the VEPM is chosen as the optimization goal, and a task-oriented dimensional optimization approach combined with the particle swarm algorithm and the neural network algorithm is proposed. This paper conducted a comparative experiment to verify the superiority of the new approach in optimizing the ECM’s comprehensive performance, whose performance analysis also can be applied into the layout design of the ECM. This paper proposed an analysis method to construct the ECM’s performance map based on the digital terrain map, which helps the control system and operator to make the optimal control decision.

We consider the simultaneous propagation of two contagions over a social network. We assume a threshold model for the propagation of the two contagions and use the formal framework of discrete dynamical systems. In particular, we study an optimization problem where the goal is to minimize the total number of new infections subject to a budget constraint on the total number of available vaccinations for the contagions. While this problem has been considered in the literature for a single contagion, our work considers the simultaneous propagation of two contagions. This optimization problem is NP-hard. We present two main solution approaches for the problem, namely an integer linear programming (ILP) formulation to obtain optimal solutions and a heuristic based on a generalization of the set cover problem. We carry out a comprehensive experimental evaluation of our solution approaches using many real-world networks. The experimental results show that our heuristic algorithm produces solutions that are close to the optimal solution and runs several orders of magnitude faster than the ILP-based approach for obtaining optimal solutions. We also carry out sensitivity studies of our heuristic algorithm.

Maintenance of eukaryotic microalgae strains for the long term is generally carried out using serial subculture techniques which require labour, time and cost. Cryopreservation techniques provide long-term storage of up to years for numerous microorganism strains and cell cultures. Ssu930ijn vbvbhnn8;l,n is related to a successfully designed mass and heat transfer balance throughout the cell. In this study, optimization of the cryopreservation process was carried out for two commercially used microalgal strains. The parameters to be optimized were DMSO percentage (0–25%), incubation time (1–15 min) and cryopreservation term (7–180 days) using a central composite design (CCD). Long-term storage up to 123.17 and 111.44 days corresponding to high cell viabilities was achieved for Chlorella vulgaris and Neochloris texensis, respectively. Generated models were found to be in good agreement with experimental results. The study also revealed holistic results for storage of microalgal strains in a stable state for industrial applications.

Body posture determination methods have many applications, including product design, ergonomic workplace design, human body simulation, virtual reality, and animation industry. Initiated in robotics, inverse kinematic (IK) method has been widely applied to proactive human body posture estimation. The analytic inverse kinematic (AIK) method is a convenient and time-saving type of IK methods. It is also indicated that, based on AIK methods, a specific body posture can be determined by the optimization of an arbitrary objective function. The objective of this paper is to predict the postures of human arms during reaching tasks. In this research, a human body model is established in MATLAB, where the middle rotation axis analytic kinematic method is accomplished, based on this model. The joint displacement function and joint discomfort function are selected to be initially applied in this AIK method. Results show that neither the joint displacement function nor the joint discomfort function predicts postures that are close enough to natural upper limb postures of human being, during reaching tasks. Therefore, a bi-criterion objective function is proposed by integrating the joint displacement function and joint discomfort function. The accuracy of the arm postures, predicted by the proposed objective function, is the most satisfactory, while the optimal value of the coefficient, in the proposed objective function, is determined by golden section search.

For an underactuated anthropomorphic hand, apart from replicating the geometry, biomimicry of human counterpart requires design based on functional biomimesis. Optimization based on grasping capabilities to generate a stable grasp for underactuated anthropomorphic hands has been a recent focus. In this article, we optimize the actuation parameters of the underactuated mechanism based on grasp quality measures. Optimization of the hand design parameters like pulley radii, spring stiffnesses, and pre-load angles was undertaken based upon the grasp robustness metric. A quasi-static model based on a soft synergistic compliant grasp of the underactuated hand is formalized. Numerical simulations based on evolutionary strategies are applied to the grasp model to optimize the underactuated parameters. Finally, validation of the results based on the grasp wrench space is presented. The results show that an anthropomorphic hand with an optimized underactuated mechanism performs better grasps.