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Affinity labeling of mu opioid reeceptors in bovine striatal membranes, using (H)-14beta-(Bromoacetamido)-7,8-dihydromorphine (H-3) H2BAM, involves the covalent binding of (H-3) H2BAM to the mu opioid receptor at pH 8.5 and 37 degrees centigrade. It illustrates the pharmacological and molecular properties of the multiple opioid receptors, the magnitude and the specific nature of labeling.

In this study, an extensive numerical analysis is carried out to investigate the effects of different quantum-based operators on the performance of continuous quantum-inspired evolutionary algorithms (QEAs). In this context, different variants of quantum-inspired evolutionary operators are adopted for numerical simulations. Furthermore, some novel chaos-enhanced QEAs are proposed and their performances are evaluated through the numerical comparative study. Based on evaluating the accuracy, robustness, convergence, scalability and sensitivity to initialisation of the rival methods, it is indicated that the algorithmic structure of QEAs is prone to being combined with chaotic maps. The results demonstrate that chaotically implemented QEAs can effectively explore/exploit the solution spaces of different landscapes and dimensionality, and finally, converge to acceptable regions within the solution domain. [ABSTRACT FROM AUTHOR]

In this paper, a controller called dynamic matrix constrained variable structure controller (DM-CVSC) is proposed. The controller takes advantages of both dynamic matrix (DM) and constrained variable structure controllers. As a result, DM-CVSC is a robust trajectory tracking controller dealing with the constraints on control inputs and also makes decision based on the future behavior of the vehicle. The controller is applied to a linearized model of half-car suspension systems which are subject to different types of road disturbances and measurement noises. In this paper, it is shown that there is a simple formulation for calculating the range of sliding gains for single-input single-output (SISO) linear control systems. As for the multi-input multi-output (MIMO) linear control systems, the calculation of upper sliding gain profile for controller leads to a search problem. To show the efficiency of the proposed controller, it is applied to four different cases involving specific road disturbances and measurement noises. The performance of the proposed controller is compared to various control techniques. [ABSTRACT FROM AUTHOR]

The aim of the current study is to probe the potential of receding horizon sliding control (RHSC) technique for reducing the coldstart hydrocarbon ( HC) emissions of automotive spark-ignited (SI) engines. The RHSC approach incorporates the potentials of sliding control (SC) and nonlinear model predictive control (NMPC) to employ the future information of the considered engine to keep the system's trajectories close to a stable manifold. To calculate the control commands, the authors adopt an efficient optimization technique, known as the multivariate quadratic fit sectioning algorithm (MQFSA), and also, define three different objective functions, based on l1, l2, and l∞ norms. To demonstrate the efficacy of RHSC controller, its performance is compared with two other well-known controllers extracted from the literature, namely NMPC and Pontryagin's minimum principle (PMP)-based controllers. Through numerical simulations for three distinctive operating conditions, it is demonstrated that the RHSC controller is very effective for reducing the total tailpipe HC emissions over the coldstart period of the considered engine system. Moreover, by conducting a hardware-in-the-loop (HIL) test using an echo state network high-fidelity model, it is indicated that the computational speed of calculating control commands is fast enough to enable RHSC to be used for real-time implementations in practice. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid quantum-inspired evolutionary algorithm (QIEA) is proposed to automatically design regularised ensemble extreme learning machines (EELMs). Quantum evolutionary computing is a relatively recent spot-lighted concept which takes advantage from both the evolutionary and quantum computing laws. In general, QIEAs have been proven to be really powerful for optimising complex engineering tasks. The fascinating trait of observation operator in QIEA enables us to transform the quantum bits to both the binary and continuous spaces. Here, the authors present a mix continuous/binary version of QIEA, to find out whether it is suited for designing regularised EELMs. Indeed, the design process of EELM is conducted at two different levels, i.e. hyper and low levels. At the low level, some novel criteria are presented in the form of penalty functions to enable the optimiser searching for parsimonious, compact and accurate regularised extreme learning machines, as individual components of the ensemble. At the hyper-level, the non-negative least square error optimisation technique is utilised to deterministically find the most eligible components for designing the ensemble. Through extensive numerical experiments, the authors demonstrate that the proposed method is really efficient for the automated design of EELM identifiers. [ABSTRACT FROM AUTHOR]

In this paper, a nonlinear model predictive controller (NMPC) with a multiagent heuristic optimizer, which is called dynamic particle swarm optimization (DPSO), is proposed to reduce the cold-start hydrocarbon (HC) emission of an automotive spark-ignited engine. In general, the cold-start HC emission reduction has been proven to be a very challenging control problem that has attracted increasing attention from the automotive research community. The main contribution of this paper lies in the implementation of a model-based predictive control scheme that uses a discrete nonlinear control-oriented model for calculating the control commands. In the first part of the experiments, the developed control-oriented model is validated with some data obtained through experiments. Then, by applying the controller to different cold-starting conditions, the values of exhaust gas temperature T\mathrm{exh} and engine-out HC emissions are controlled to reduce the cumulative tailpipe emissions \textHC\mathrm{cum}. To ascertain the veracity of the proposed control scheme, the same problem is solved using Pontryagin's minimum principle. The conducted simulations indicate the applicability of DPSO as an effective solver at the heart of the NMPC. Moreover, the results of model-in-the-loop simulation and hardware-in-the-loop testing demonstrate the acceptable performance of the proposed control scheme for real-time applications, which is based on the NMPC method for the considered problem. [ABSTRACT FROM PUBLISHER]

The urgent need to meet increasingly tight environmental regulations and new fuel economy requirements has motivated system science researchers and automotive engineers to take advantage of emerging computational techniques to further advance hybrid electric vehicle and plug-in hybrid electric vehicle (PHEV) designs. In particular, research has focused on vehicle powertrain system design optimization, to reduce the fuel consumption and total energy cost while improving the vehicle's driving performance. In this work, two different natural optimization machines, namely the synchronous self-learning Pareto strategy and the elitism non-dominated sorting genetic algorithm, are implemented for component sizing of a specific power-split PHEV platform with a Toyota plug-in Prius as the baseline vehicle. To do this, a high-fidelity model of the Toyota plug-in Prius is employed for the numerical experiments using the Autonomie simulation software. Based on the simulation results, it is demonstrated that Pareto-based algorithms can successfully optimize the design parameters of the vehicle powertrain. [ABSTRACT FROM AUTHOR]

In this research, a high-performance predictive controller is developed for automotive coldstart emission reductions. The proposed control scheme combines a hybrid switching predictive controller (HSPC) with proportional integral derivative (PID) gains to simultaneously minimize the cumulative hydrocarbon emissions ( HC cum ) and the control input variations for a given engine during the coldstart operation. It is essential to use a sufficiently accurate surrogate meta-representation of the real engine within this model-based controller to predict the states of the plant and impart proper control commands to the system. The existing studies in the research literature have clearly demonstrated that automotive engines have a highly transient nonlinear behavior during coldstart periods and different disturbances can affect their operations. To cope with the mentioned difficulties, several coldstart experiments are performed to capture a comprehensive database for the considered engine. Thereafter, a powerful knowledge-based black-box meta-modeling tool, known as group method data handling (GMDH), is adopted to have a neural representation of the engine׳s coldstart behavior. As a real-time controller, the proposed PID-based HSPC requires a fast and robust solver to calculate the gains of PID in a computationally efficient manner. Here, a multivariate quadratic fit-sectioning algorithm (MQFSA) is proposed to deterministically determine the control commands. Other than the considered online optimizer, a powerful chaos-enhanced evolutionary algorithm (CEA) is used to heuristically optimize the prediction horizon ( H P ) and control commands horizon ( H U ) to achieve the best results. It is demonstrated that using such an optimizer, instead of trial-and-errors, to heuristically set the control and plant prediction horizon lengths is an effective strategy. Finally, several comparative studies are conducted to further indicate the efficacy of the proposed PID-based HSPC for the automotive coldstart control problem. [ABSTRACT FROM AUTHOR]

In the current study, a novel intelligent control algorithm is proposed for the efficient reduction of coldstart hydrocarbon ( HC ) emissions of an automotive engine. The proposed intelligent controller inherits the computational advantages of two advanced techniques, namely hybrid switching predictive controller and extreme learning machine (ELM). The proposed model-based predictive controller has a switching behavior which enables it to be used for a deliberate control of the considered engine's behavior during the coldstart period. As the hybrid controller requires a hybrid model for processing, a bi-level ELM is used which is composed of two independent ELMs for the calculation of exhaust gas temperature ( T exh ) and engine-out HCs . A novel online trajectory builder is also introduced to make sure that the control commands are adjusted according to the actual behavior of the engine. Through simulations, the computational potentials of ELM for designing hybrid switching controllers are demonstrated. Furthermore, it is indicated that the proposed hybrid switching controller can afford qualified results for the considered case study. To verify the authenticity of the obtained results, two well-known controllers, namely nonlinear model predictive controller (NMPC) and an optimal controller based on the Pontryagin's minimum principle (PMP), are taken into account. The numerical experiments also host an additional step which evaluates the impact of prediction horizon ( H P ) on the performance of the controller. Based on all of the mentioned simulations, the efficacy of hybrid switching predictive controller with bi-level ELM (HSPC-BELM) is verified. [ABSTRACT FROM AUTHOR]

In this study, a comprehensive empirical test is conducted to analyse the effects of two well-known chaotic maps, namely sinusoidal and logistic maps, on the efficacy of double Pareto crossover, Laplace crossover and simulated binary crossover operators for the global optimization of continuous problems. To do so, 13 well-known numerical benchmark problems in three distinctive dimensions, namely 50D, 100D and 200D, are considered and the genetic algorithm (GA) with simple version and chaos-enhanced versions of the mentioned crossover operators are utilized for optimizing these functions. Furthermore, a time complexity analysis is conducted to find out the impact of hybridizing the chaos and the evolutionary operators on the computational complexity of GA. The results of the experimental analysis provide us with fruitful information regarding the scalability, computational complexity and exploration/exploitation capability of the considered rival optimization algorithms, as well as, demonstrate the efficacy of chaos-evolutionary computing for numerical continuous optimizations. [ABSTRACT FROM AUTHOR]

This papers deals with the design of an efficient intelligent tool for automotive engine coldstart monitoring applications. The real-time identification and control of engine coldstart operations have been proven to be very formidable tasks. This refers to the highly nonlinear and transient behavior of the engine system over coldstart operations. As the catalyst temperature is not sufficiently high, the amount of tailpipe hydrocarbon emissions is remarkable over this period. The researchers of systems sciences have investigated the development of soft sensors which are needed to monitor the catalyst temperature for enabling effective coldstart controllers to reduce the emissions. However, most of the conducted researches have focused on using complicated statistical models as well as gradient-based neural networks for the considered problem. This raises several problems regarding the generalization and computational efficiency of the proposed models. In this paper, the authors propose a novel computationally efficient method based on the integration of accelerated kernels and Tikhonov regularized extreme learning machine for the online monitoring of the catalyst temperature over the coldstart period for a given engine. Based on the results of comparative simulations, the authors demonstrate that the proposed soft sensor can be very effective for automotive coldstart applications. [ABSTRACT FROM AUTHOR]

In this investigation, the authors intend to find out whether chaos-embedded heuristic walks with nature-based controllers are capable of increasing the performance of the standard random walks of evolutionary algorithms. Indeed, the research motivation emanates in the pursuit of addressing an increasing interest in using chaos sequences for modifying the exploitation capability of evolutionary optimisers. Here, the authors propose a nature-based controller to dynamically balance the exploration/exploitation capability of the evolutionary optimiser. To test the validity of the proposed method, a number of well-known Gaussian distribution-based evolutionary operators are considered, and their random parameters are changed with discrete-time chaotic sequences. Besides, to obtain authentic and reliable results regarding the accuracy and robustness of the rival techniques, the authors went through the existing literature and extracted 23 scalable, multimodal, constraint and unconstraint numerical benchmark problems and one engineering problem, i.e. optimal control of shape memory alloy actuators. The convergence rate and computational time of the resulting self-controlled chaos-based evolutionary approaches are analysed to find out whether they are capable of stabilising the chromosomes within a logical time. The results of the numerical experiments indicate that the self-controlled chaos-enhanced heuristic walks significantly increase the global searching capability of the evolutionary optimisers and have an aptitude to show a robust performance. [ABSTRACT FROM PUBLISHER]

The paper deals with devising two different fuzzy inference systems to predict the hardness of copper/carbon nanotube nanocomposite. These composites are outstanding candidates for thermal management applications in electronic packaging due to the high conductivity of copper. Knowing the extraordinary properties of carbon nanotubes, it seems that copper-based composites reinforced with small amount of carbon nanotubes, resulted in improved mechanical properties. Hence, carbon nanotube reinforced copper matrix nanocomposites are fabricated by hot-press sintering of high energy ball milled copper/carbon nanotube powders. Different milling factors are investigated. Finally the Vickers hardness of sintered nanocomposites is reported. To simulate a predictive framework for current case study, two different machine learning algorithms are engaged. The first learning algorithm is the classic least square optimization method, which provides the requirements for fast adaption of the consequent parts of fuzzy inference system. The second method learning algorithm uses the rudiments of neural computing through layering the fuzzy inference system and using back-propagation optimization algorithm. Based on the experiments, the authors realize that the adopted fuzzy systems can effectively extract the knowledge required for predicting the hardness of copper/carbon nanotube nanocomposite. [ABSTRACT FROM AUTHOR]

In this paper, the authors intend to attest the applicability of computational intelligence for tackling a demanding real-life engineering problem, that is analysing the amounts of exhaust gas temperature () and the engine-out hydrocarbon emission () during the coldstart operation of an automotive engine with respect to the variation of engine speed (), spark timing (Δ) and air/fuel ratio. It has been proven that the coldstart phenomenon is highly transient, nonlinear and uncertain and therefore has absorbed an increasing attention of the researchers of automotive society. In this paper, we prove that a proper integration of intelligent methods can result in a very efficient tool suited for identifying the characteristics of coldstart phenomenon. To do so, a recent spotlighted natural-inspired optimiser called mutable smart bee algorithm is utilised to evolve a Gaussian generalised regression neural network such that the resulted framework can conduct a fast, robust and accurate identification. The outcomes of the conducted experiments endorse on the applicability of intelligent techniques for engine coldstart identification, and also, pave the way for future investigations on intelligent-based analysis of demanding automotive problems. [ABSTRACT FROM AUTHOR]

In this investigation, an intelligent technique is used to analyze the deposition efficiency (DE) of micro-particles in a realistic human upper airway model. To do so, firstly a numerical framework including computerized tomography (CT) imaging, geometry production software and mesh generation tool is utilized to provide a bed for simulating the human airway. Thereafter, the simulated airway is exposed to computational fluid-particle dynamics (CFPD) system to study the complete upper tracheabronchial airway from trachea (G0) to second generation of bifurcations (G2). At the numerical phase, low Reynolds number (LRN) k-ωturbulence model is considered to simulate the laminar to turbulent occurred airflow. At the final stage, the obtained knowledge is used to provide a continuous model suited for analyzing the DE value in different sections of human upper airway. Here, the authors utilize a fast yet accurate intelligent interpolator called extreme learning machine (ELM) neural network to capture the knowledge of the obtained database. Bio-inspired metaheuristics are also used to evolve the architecture of ELM neural network such that it can predict the DE values with a high degree of accuracy and robustness. The results of the conducted experiments indicate that the proposed bio-inspired ELM interpolator is capable of providing a fast and accurate model which can yield proper information in a very short period of time as compared to the existing numerical techniques such as CFD. On the other hand, by using different types of bio-inspired metaheuristics, i.e. mutable smart bee algorithm (MSBA), particle swarm optimization (PSO), firefly algorithm (FA) and scale-factor local search differential evolution (SFLSDE), it is observed that the structure of ELM neural network can be easily trained using metaheuristics. In general, the experimental results demonstrate the applicability and efficacy of soft computing techniques for predicting the DE values in human upper airway. [ABSTRACT FROM AUTHOR]

In this investigation, a comparative experimental study is conducted to obtain an efficient hybrid intelligent framework for analysing and optimising the operating parameters of the laser solid freeform fabrication (LSFF) process. Here, the experimental studies are conducted in two different stages. In the first stage, different concepts of machine learning systems are taken into account to find a simple yet accurate intelligent model for identifying the LSFF process. To do so, multi-layered neural network with different types of analytical, gradient-based and heuristic learning strategies, i.e. extreme learning machine, back-propagation and steepest descend gradient-based learning and Nelder–Mead simplex heuristic, respectively, are adopted and applied to the LSFF process. In the second stage, different types of swarm- and evolutionary-based metaheuristics, i.e. differential evolutionary algorithm, particle swarm optimisation, the great salmon run, firefly algorithm, bee algorithm, are used to simultaneously find the optimal values of melt-pool depth and clad height during the LSFF process. The statistical results of the simulation indicate that the conducted experiments can result in a fast, robust and accurate hybrid intelligent system which can easily cope with the nonlinearities and uncertainties of the resulting optimisation problem. [ABSTRACT FROM AUTHOR]

Fabrication of scaffolds that closely mimic the biomechanical properties of the surrounding bone is one of the main issues in designing an orthopedic implant. This study is aimed to develop an optimal predefined three-dimensional polymethylmethacrylate scaffold as a potential bone substitute via laser drilling technique, which is a novel method of fabricating bone scaffold. To achieve this goal, using the obtained experimental data, the authors have tuned an adaptive neuro fuzzy inference system, which predicts the hole diameter and its depth as two process outputs for any given values of laser power and laser irradiation time. In addition to this adaptive neuro fuzzy inference system model, some experimental models were also proposed to estimate the scaffold porosity and the mechanical strength regarding the drilled hole geometry. To design an optimal scaffold with the highest amount of strength and porosity, a Pareto-based self-learning evolutionary algorithm called SOPEA was proposed. To enhance its ability in finding the optimal Pareto front, it was equipped with the adaptive self-organizing map. Based on the obtained Pareto front, a trade-off has been made in order to find a solution that yields deliberate operational properties. The scaffold morphology was observed by scanning electron microscopy images and its mechanical property was determined using a compressive test. The versatility provided by this technique allows the fabrication of scaffolds with controllable amount of porosity, pore size, and mechanical properties while providing fully interconnected channel network. The results also indicate that the proposed model predicts the process outputs with acceptable accuracy and the proposed optimization algorithm is able to find the optimal solution effectively. [ABSTRACT FROM PUBLISHER]

In recent decades, analyzing and optimizing thermal systems have become of great interest to researchers. Recently, the engineers concentrated on variant concepts of artificial intelligence such as machine learning, simulation, fuzzy logic, game theory, and evolutionary computing to deal with complicated barriers and obstacles. Artificial intelligence and expert system techniques play an important role for surveying and controlling mechanical systems such as power plants and reservoirs. This is because of their interdisciplinary applications and versatile servicing potential in mathematical modeling of industrial systems. In this article, a new method called synchronous parallel shuffling self-organized Pareto strategy algorithm is presented which synthesizes different artificial techniques, nominally evolutionary computing, swarm intelligence techniques, and time adaptive self-organizing map that apply simultaneously incorporating with a stochastic data sharing behavior. Thereafter, it is applied to verify the optimum operating parameter of Damavand power plant as the biggest constructed power plant in Middle East with the potential of producing about 2300 MW electricity sited in Tehran, capital of Iran, as a multi-objective, multi-modal complex problem. It is also proved that implementing the governing equations of power plant leads to a multi-objective problem where some of these objectives are non-linear, non-convex, and multi-modal with different type of real-life engineering constraints. The results confirm the acceptable performance of proposed technique in optimizing the operating parameters of Damavand power plant. [ABSTRACT FROM AUTHOR]