This paper deals with the optimization of a new redundant spherical parallel manipulator (New SPM). This manipulator consists of two spherical five-bar mechanisms connected by the end-effector, providing three degrees of freedom, and has an unlimited self-rotation capability. Three optimization procedures based on the genetic algorithm method were carried out to improve the dexterity of the New SPM. The first and the second optimizations were applied to a symmetric New SPM structure, while the third was applied to an asymmetric New SPM structure. In both cases, the optimization was performed using an objective function defined by the quadratic sum of link angles. In addition, certain criteria and constraints were implemented. The obtained results demonstrate significant improvements in the dexterity of the New SPM and its capability of an unlimited self-rotate in an extended workspace. A comparison of the self-rotation performances between the classical 3-RRR SPM (R for revolute joint) and the New SPM is also presented.

We devise schemes for producing, in the least possible time, p identical objects with n agents that work at differing speeds. This involves halting the process to transfer production across agent types. For the case of two types of agent, we construct schemes based on the Euclidean algorithm that seeks to minimize the number of pauses in production.

Risk measurement and econometrics are the two pillars of actuarial science. Unlike econometrics, risk measurement allows taking into account decision-makers’ risk aversion when analyzing the risks. We propose a hybrid model that captures decision-makers’ regression-based approach to study risks, focusing on explanatory variables while paying attention to risk severity. Our model considers different loss functions that quantify the severity of the losses that are provided by the risk manager or the actuary. We present an explicit formula for the regression estimators for the proposed risk-based regression problem and study the proposed results. Finally, we provide a numerical study of the results using data from the insurance industry.

In the field of laparoscopic surgery, research is currently focusing on the development of new robotic systems to assist practitioners in complex operations, improving the precision of their medical gestures. In this context, the performance of these robotic platforms can be conditioned by various factors, such as the robot’s accessibility and dexterity in the task workspace. In this paper, we present a new strategy for improving the kinematic and dynamic performance of a 7-degrees of freedom robot-assisted camera-holder system for laparoscopic surgery. This approach involves the simultaneous optimization of the robot base placement and the laparoscope mounting orientation. To do so, a general robot capability representation approach is implemented in an innovative multiobjective optimization algorithm. The obtained results are first evaluated in simulation and then validated experimentally by comparing the robot’s performances implementing both the existing and the optimized solution. The optimization result led to a 2% improvement in the accessibility index and a 14% enhancement in manipulability. Furthermore, the dynamic performance criteria resulted in a substantial 43% reduction in power consumption.

Finite Cartesian products of operators play a central role in monotone operator theory and its applications. Extending such products to arbitrary families of operators acting on different Hilbert spaces is an open problem, which we address by introducing the Hilbert direct integral of a family of monotone operators. The properties of this construct are studied, and conditions under which the direct integral inherits the properties of the factor operators are provided. The question of determining whether the Hilbert direct integral of a family of subdifferentials of convex functions is itself a subdifferential leads us to introducing the Hilbert direct integral of a family of functions. We establish explicit expressions for evaluating the Legendre conjugate, subdifferential, recession function, Moreau envelope, and proximity operator of such integrals. Next, we propose a duality framework for monotone inclusion problems involving integrals of linearly composed monotone operators and show its pertinence toward the development of numerical solution methods. Applications to inclusion and variational problems are discussed.

The optimization of mechanical behavior in safety systems during crash scenarios consistently poses challenges in vehicle development. Hence, a reinforcement learning-based approach for optimizing restraint systems in frontal impacts is proposed. The trained agent, which adjusts five parameters simultaneously, is capable of minimizing loads on a seen and unseen anthropomorphic test device on the co-driver position and is thus able of transferring knowledge. A hundred times higher rate of convergence to reach a similar optimum compared to a global optimization algorithm has been achieved.

The increase in Electrical and Electronic Equipment (EEE) usage in various sectors has given rise to repair and maintenance units. Disassembly of parts requires proper planning, which is done by the Disassembly Sequence Planning (DSP) process. Since the manual disassembly process has various time and labor restrictions, it requires proper planning. Effective disassembly planning methods can encourage the reuse and recycling sector, resulting in reduction of raw-materials mining. An efficient DSP can lower the time and cost consumption. To address the challenges in DSP, this research introduces an innovative framework based on Q-Learning (QL) within the domain of Reinforcement Learning (RL). Furthermore, an Enhanced Simulated Annealing (ESA) algorithm is introduced to improve the exploration and exploitation balance in the proposed RL framework. The proposed framework is extensively evaluated against state-of-the-art frameworks and benchmark algorithms using a diverse set of eight products as test cases. The findings reveal that the proposed framework outperforms benchmark algorithms and state-of-the-art frameworks in terms of time consumption, memory consumption, and solution optimality. Specifically, for complex large products, the proposed technique achieves a remarkable minimum reduction of 60% in time consumption and 30% in memory usage compared to other state-of-the-art techniques. Additionally, qualitative analysis demonstrates that the proposed approach generates sequences with high fitness values, indicating more stable and less time-consuming disassembles. The utilization of this framework allows for the realization of various real-world disassembly applications, thereby making a significant contribution to sustainable practices in EEE industries.

This paper presents the design and validation of a wearable shoulder exoskeleton robot intended to serve as a platform for assistive controllers that can mitigate the risk of musculoskeletal disorders seen in workers. The design features a four-bar mechanism that moves the exoskeleton’s center of mass from the upper shoulders to the user’s torso, dual-purpose gravity compensation mechanism located inside the four-bar’s linkages that supports the full gravitational loading from the exoskeleton with partial user’s arm weight compensation, and a novel 6 degree-of-freedom (DoF) compliant misalignment compensation mechanism located between the end effector and the user’s arm to allow shoulder translation while maintaining control of the arm’s direction. Simulations show the four-bar design lowers the center of mass by $ 11 $ cm and the kinematic chain can follow the motion of common upper arm trajectories. Experimental tests show the gravity compensation mechanism compensates gravitational loading within $ \pm 0.5 $ Nm over the range of shoulder motion and the misalignment compensation mechanism has the desired 6 DoF stiffness characteristics and range of motion to adjust for shoulder center translation. Finally, a workspace admittance controller was implemented and evaluated showing the system is capable of accurately reproducing simulated impedance behavior with transparent low-impedance human operation.

As advances in technology have led to increased use of bentonites, more high-quality bentonite has been sought. The volume of high-quality bentonites available is shrinking and use of bentonite reserves containing impurities is inevitable. The aim of this study was to apply Box–Behnken experimental design and response surface methodology to model and optimize some operational parameters of a hydrocyclone to produce three groups of bentonite concentrates. The four significant operational parameters of hydrocyclones are feed solid ratio, inlet pressure, vortex diameter, and apex diameter, and these parameters were varied and the results evaluated using the Box–Behnken factorial design. In order to produce bentonite concentrates using a hydrocyclone, mathematical model equations were derived by computer simulation programming applying a least-squares method, using Minitab 15. Second-order response functions were produced for the swelling and to establish the quantity of smectite in the bentonite concentrates. Predicted values were found to be in good agreement with the experimental values (R2 values of between 0.829 and 0.999 for smectite and three different swelling groups for the bentonites). Although in natural states these bentonites are not suitable for industrial use, enhancements were obtained giving up to 81.45% smectite and by increasing swelling by 194% for the three bentonite groups. The swelling properties of the bentonites are improved by increasing the proportion of smectite content. The graphics were designed to relate swelling and smectite content according to the two-dimensional hydrocyclone factors, and each factor was evaluated in itself. The present study revealed that the Box–Behnken and response surface methodology can be applied efficiently to model the hydrocyclone for bentonite; the method is economical and provides the maximum amount of information in a short period of time and with the smallest numberof experiments.

The use of porous filters is indispensable in laboratory- and field-scale diffusion studies, where sample confinement is needed for mechanical reasons. Examples are diffusion studies with compacted swelling clays or brittle clay stones. Knowledge of the diffusion properties of these filters is important in cases where they contribute significantly to the overall diffusive resistance in the experimental setup. In the present study, measurements of effective diffusion coefficients (Db) in porous, stainless steel filter discs are reported for tritiated H2O (HTO), 22Na+, Cs+, and Sr2+ before and after use of the filters in diffusion experiments with different clay minerals. The Db values for used filters were found to be less than those of the as-received filters by ∼30–50%. The Db values measured for the diffusion of HTO, 22Na+, Cs+, and Sr2+ in unused and used stainless steel filter discs correlated fairly well with the respective molecular diffusion coefficients in bulk water. Although such correlations are inherently associated with some uncertainties, they allow reasonable estimates to be made for diffusants for which no Db values are available. For the first time, a procedure is outlined that allows an integrative assessment to be made for the impact of the uncertainties in the filter diffusion properties on the combined standard uncertainties of the diffusion parameters obtained from through-diffusion experiments. This procedure can be used in the design and optimization of through-diffusion experiments in which the diffusive resistance of the porous filters must not be ignored. Shown here, as a general rule of thumb, is that, if the effective diffusion coefficient in the porous filter is at least three times larger than that in the clay, the choice of geometrical boundary conditions is rather uncritical, as long as the thickness of the clay sample is greater than that of the porous filters.

Calculation of loss scenarios is a fundamental requirement of simulation-based capital models and these are commonly approximated. Within a life insurance setting, a loss scenario may involve an asset-liability optimization. When cashflows and asset values are dependent on only a small number of risk factor components, low-dimensional approximations may be used as inputs into the optimization and resulting in loss approximation. By considering these loss approximations as perturbations of linear optimization problems, approximation errors in loss scenarios can be bounded to first order and attributed to specific proxies. This attribution creates a mechanism for approximation improvements and for the eventual elimination of approximation errors in capital estimates through targeted exact computation. The results are demonstrated through a stylized worked example and corresponding numerical study. Advances in error analysis of proxy models enhance confidence in capital estimates. Beyond error analysis, the presented methods can be applied to general sensitivity analysis and the calculation of risk.

Inflatable wings for UAVs are useful where storage space is a severe constraint. Literature in the field of inflatable wings often assumes an inflated aerofoil shape for various analyses. However, the flexible inflatable aerofoil fabric might deform to another equilibrium shape upon inflation. Hence accurate shape prediction of the inflated aerofoil is vital. Further, no standardised nomenclature or a process to convert a smooth aerofoil into its corresponding inflatable aerofoil counterpart is available. This paper analytically predicts the equilibrium shape of any inflatable aerofoil and validates the analytical prediction using non-linear finite element methods. Further, a scheme for the generation of two types of inflatable aerofoils is presented. Parameters such as the number and position of compartments and aerofoil length ratio (ALR) are identified as necessary to define the aerofoil’s shape fully. A process to minimise the deviation of the inflatable aerofoil from its original smooth aerofoil using particle swarm optimisation (PSO) is discussed. Research presented in this paper can help in performing various analyses on the actual equilibrium shape of the aerofoil.

This paper investigates the effect of the optimised morphing leading edge (MLE) and the morphing trailing edge (MTE) on dynamic stall vortices (DSV) for a pitching aerofoil through numerical simulations. In the first stage of the methodology, the optimisation of the UAS-S45 aerofoil was performed using a morphing optimisation framework. The mathematical model used Bezier-Parsec parametrisation, and the particle swarm optimisation algorithm was coupled with a pattern search with the aim of designing an aerodynamically efficient UAS-45 aerofoil. The $\gamma - R{e_\theta }$ transition turbulence model was firstly applied to predict the laminar to turbulent flow transition. The morphing aerofoil increased the overall aerodynamic performances while delaying boundary layer separation. Secondly, the unsteady analysis of the UAS-S45 aerofoil and its morphing configurations was carried out and the unsteady flow field and aerodynamic forces were analysed at the Reynolds number of 2.4 × 106 and five different reduced frequencies of k = 0.05, 0.08, 1.2, 1.6 and 2.0. The lift (${C_L})$, drag (${C_D})$ and moment (${C_M})\;$coefficients variations with the angle-of-attack of the reference and morphing aerofoils were compared. It was found that a higher reduced frequencies of 1.2 to 2 stabilised the leading-edge vortex that provided its lift variation in the dynamic stall phase. The maximum lift $\left( {{C_{L,max}}} \right)$ and drag $\left( {{C_{D,max}}} \right)\;$coefficients and the stall angles of attack are evaluated for all studied reduced frequencies. The numerical results have shown that the new radius of curvature of the MLE aerofoil can minimise the streamwise adverse pressure gradient and prevent significant flow separation and suppress the formation of the DSV. Furthermore, it was shown that the morphing aerofoil delayed the stall angle-of-attack by 14.26% with respect to the reference aerofoil, and that the ${C_{L,max}}\;$of the aerofoil increased from 2.49 to 3.04. However, while the MTE aerofoil was found to increase the overall lift coefficient and the ${C_{L,max}}$, it did not control the dynamic stall. Vorticity behaviour during DSV generation and detachment has shown that the MTE can change the vortices’ evolution and increase vorticity flux from the leading-edge shear layer, thus increasing DSV circulation. The conclusion that can be drawn from this study is that the fixed drooped morphing leading edge aerofoils have the potential to control the dynamic stall. These findings contribute to a better understanding of the flow analysis of morphing aerofoils in an unsteady flow.

With the increasing demand for smarter antenna design in advanced technology applications, well-designed antennas have been an important factor in enhancing system performance. Most traditional antenna design requires multiple iterations and extensive testing to produce a final product. Machine learning (ML) algorithms have been used as an alternative to predict the optimal design parameters, but the outcome depends highly on the ML model efficiency. With recent development in machine learning algorithms and the availability of data for antenna design, we investigated different machine learning algorithms for optimizing the output strength of three basic antennae by analyzing the signal strength of the antenna for various antenna parameters. Different regression-based ML models were used to learn the behaviors and efficiency of three different antennas and to predict the output strength (S11) for different ranges of frequencies. The experiment compared and analyzed these ML regression algorithms for three different antennas: shot antenna, patch antenna, and bowtie antenna. In addition, the paper also provides comparison of ensemble ML models for performance analysis using the best three ML algorithms from the preliminary study. This study optimizes antenna parameters and quicker and smarter antenna design procedure using ML algorithms as compared to traditional design methods.