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Disruption of quorum sensing (QS) system, which is a central regulator for pathogenesis of Pseudomonas aeruginosa, is referring to as quorum quenching (QQ). This study was undertaken to evaluate and enhance the anti-quorum sensing (AQS) potential of probiotic strain Lactobacillus rhamnosus GG. The cell-free supernatant (CFS) of this probiotic strain showed anti-quorum sensing activity against Pseudomonas aeruginosa, which was determined using well-diffusion agar-plate assay. Anti-quorum sensing potential of L. rhamnosus GG was enhanced by optimization of various cultural conditions using classical and statistical optimization approaches. Six variables were optimized using one-variable-at-a-time (OVAT) method. Four significant variables, viz., temperature, pH, incubation time, metal ion, and its concentration, were chosen for further optimization by response surface methodology (RSM) using central composite design (CCD). Analysis of variance (ANOVA) demonstrated that the regression model is highly significant, as indicated by F test with a low probability value (p < 0.0002) and high value of coefficient of determination (0.8738) and also had significant influence on the generation of anti-quorum sensing effector molecules. Maximum production of anti-quorum sensing activity, in terms of zones of inhibition, was achieved under the following optimized conditions such as 37 °C temperature, pH 6.5, incubation time 24 h, and 2.5 mM concentration of zinc sulfate (ZnSO4). The quadratic model predicted 1.3-fold increase anti-quorum sensing activity production over un-optimized cultural conditions. The present research is the first report representing the enhancement of anti-quorum sensing potential of L. rhamnosus GG. [ABSTRACT FROM AUTHOR]

Disruption of quorum sensing (QS) system, which is a central regulator for pathogenesis of Pseudomonas aeruginosa, is referring to as quorum quenching (QQ). This study was undertaken to evaluate and enhance the anti-quorum sensing (AQS) potential of probiotic strain Lactobacillus rhamnosus GG. The cell-free supernatant (CFS) of this probiotic strain showed anti-quorum sensing activity against Pseudomonas aeruginosa, which was determined using well-diffusion agar-plate assay. Anti-quorum sensing potential of L. rhamnosus GG was enhanced by optimization of various cultural conditions using classical and statistical optimization approaches. Six variables were optimized using one-variable-at-a-time (OVAT) method. Four significant variables, viz., temperature, pH, incubation time, metal ion, and its concentration, were chosen for further optimization by response surface methodology (RSM) using central composite design (CCD). Analysis of variance (ANOVA) demonstrated that the regression model is highly significant, as indicated by F test with a low probability value (p < 0.0002) and high value of coefficient of determination (0.8738) and also had significant influence on the generation of anti-quorum sensing effector molecules. Maximum production of anti-quorum sensing activity, in terms of zones of inhibition, was achieved under the following optimized conditions such as 37 °C temperature, pH 6.5, incubation time 24 h, and 2.5 mM concentration of zinc sulfate (ZnSO4). The quadratic model predicted 1.3-fold increase anti-quorum sensing activity production over un-optimized cultural conditions. The present research is the first report representing the enhancement of anti-quorum sensing potential of L. rhamnosus GG. [ABSTRACT FROM AUTHOR]

We present a comparison study of state-of-the-art classical optimization methods to a D-Wave 2000Q quantum annealer for the scheduling of agile Earth observation satellites. The problem is to acquire high-value images while obeying the attitude maneuvering constraint of the satellite. In order to investigate close to real-world problems, we created benchmark problems by simulating realistic scenarios. Our results show that a tuned quantum annealing approach can run faster when used to find the optimal solution than a classical exact solver for some of the problem instances. Moreover, we find that the solution quality of the quantum annealer is comparable to the heuristic method used operationally for small problem instances, but degrades rapidly due to the limited precision of the quantum annealer. [ABSTRACT FROM AUTHOR]

Although there are multiple methodologies to carry out collaborative practices of inventory management, none are set up for impulse purchase products. This is a disadvantage because with the opening of new markets and the proliferation of consumer culture, the economic importance of buying products on impulse always remains relevant. In this paper, a Vendor Managed Inventory model was designed based on the direct participation of a vendor and a buyer (two-level supply chain), in order to agree on the procurement operations of a portfolio of impulse purchase products. For this proposal, a mathematical model based on classical optimization was designed to minimize inventory costs. Subsequently, a case study was conducted comparing the economic impact of the model with respect to a traditional supply agreement in a non-cooperative scenario. The results reflected positive economic effects in the implementation of the model related to the economies of scale to exploit fixed costs present in the agreement. Additionally, the conditions under which the implementation of this model grants individual and global benefits to the participating companies were validated. [ABSTRACT FROM AUTHOR]

In the fields which require finding the most appropriate value, optimization became a vital approach to employ effective solutions. With the use of optimization techniques, many different fields in the modern life have found solutions to their real-world based problems. In this context, classical optimization techniques have had an important popularity. But after a while, more advanced optimization problems required the use of more effective techniques. At this point, Computer Science took an important role on providing software related techniques to improve the associated literature. Today, intelligent optimization techniques based on Artificial Intelligence are widely used for optimization problems. The objective of this paper is to provide a comparative study on the employment of classical optimization solutions and Artificial Intelligence solutions for enabling readers to have idea about the potential of intelligent optimization techniques. At this point, two recently developed intelligent optimization algorithms, Vortex Optimization Algorithm (VOA) and Cognitive Development Optimization Algorithm (CoDOA), have been used to solve some multidisciplinary optimization problems provided in the source book Thomas Calculus 11th Edition and the obtained results have compared with classical optimization solutions. [ABSTRACT FROM AUTHOR]

B-splines have today become the industry standard for CAD data representation. Freeform shape synthesis from point cloud data is an emerging technique. This predominantly involves B-spline curve / surface fitting to the point cloud data to obtain the CAD definitions. Accurate curve and surface fit-ting from point clouds needs estimation of order, i.e. number of knots and a good parameterization model, i.e. the determination of parameter values of the digitized points in order to perform least squares (LSQ) fitting. Numerous work have been done on selection of such parameters. Nevertheless, the problem of LSQ with optimal knots has not been addressed in totality. Simultaneous optimization of number of knots and parameter values leads to multiple contradictory objectives and traditional optimization is prone to fail. The present work proposes a hybrid approach based on genetic algorithms, for optimal number of knots and optimal parameter allocation, simultaneously, for curve and surface fitting. A novel population initial-ization scheme involving analytical and neural network estimation is also proposed here, ensuring that the optimization procedure is both global in nature and computationally less expensive. Further classical opti-mization of parameters alone based on error is carried if required. The present study of parameterization is for Non Uniform B-spline fitting. [ABSTRACT FROM AUTHOR]

• Metaheuristics and classical methods are compared for the reconfiguration problem. • New metrics are proposed to compare methods to solve the reconfiguration problem. • Metrics allow a fair comparison among different computational implementations. • Metaheuristics have superior computational efficiency to solve the reconfiguration. • Classical methods achieve the optimum solution for low and medium-size systems. Reconfiguration is a complex combinatorial problem in which the topology of distribution systems is modified by the opening/closing of interconnection switches aiming techno-economic benefits (e.g., minimization of losses). Numerous optimization methods have been developed to solve the reconfiguration problem, although a comparative analysis of their performances is still a challenging task due to the nature of the methods, differences in their implementation, and used computational equipment. To fulfill that gap, this paper assesses classical models along with metaheuristics already applied in the specialized literature considering the reported losses and computational effort. To eliminate differences due to implementation and equipment, two proposed metrics are assessed using a reference specialized power flow: 'equivalent time' and 'equivalent number of power flows'. The quality of the solutions was compared for standard test systems (33, 136, and 417 buses) and a ranking of the methods was produced. It was concluded that linear and conic programming models find the optimal solution for low and medium-size systems; moreover, the linear model requires lower computational effort than the conic and the nonlinear programming formulations. On the other hand, it was verified that metaheuristics need lower computational effort and provide better solutions for large-size systems compared to classical optimization. [ABSTRACT FROM AUTHOR]