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PCR testing is increasingly important for microbial control in SPF facilities. However, most current PCR methods are timeconsuming and require compromise between high sensitivity and high multiplexing. We developed a one-tube multiplex nested PCR strategy (MN-PCR) for simultaneous direct (that is, without culturing) detection of multiple pathogens. We first aligned sequences for the 16S rDNA genes of selected target bacteria and a panel of closely related organisms. From these data, we designed a pair of universal primers and multiple sets of species-specific PCR primers to amplify the target sequences; the universal primers were modified to include various degenerate bases and locked nucleic acids. In a single tube, 16S rDNA sequences were amplified by using the nested PCR primers under high temperature (that is, above 65°C) during the first stage of the MN-PCR procedure, when the target-species–specific PCR primers do not support amplification due to their short length. In addition, the concentration of the nested PCR primers during the first stage was adjusted to ensure that they were consumed and did not yield visible bands themselves. During the second stage, the enriched 16S rDNA sequences then served as templates for amplification of the species-specific fragments by using the multiple PCR primers at low annealing temperatures (that is, below 60°C). The results showed that our MN-PCR method detected as little as 1 fg of target bacterial DNA in a 20-μL reaction volume, whereas conventional multiplex PCR detected a minimum of 1 pg only. Compared with traditional multiplex PCR assays, our MN-PCR system is an effective and efficient culture-free process.

The proposed work focuses on the path planning for Unmanned Surface Vehicles (USVs) in the ocean enviroment, taking into account various spatiotemporal factors such as ocean currents and other energy consumption factors. The paper proposes the use of Gaussian Process Motion Planning (GPMP2), a Bayesian optimization method that has shown promising results in continuous and nonlinear path planning algorithms. The proposed work improves GPMP2 by incorporating a new spatiotemporal factor for tracking and predicting ocean currents using a spatiotemporal Bayesian inference. The algorithm is applied to the USV path planning and is shown to optimize for smoothness, obstacle avoidance, and ocean currents in a challenging environment. The work is relevant for practical applications in ocean scenarios where an optimal path planning for USVs is essential for minimizing costs and optimizing performance., 9 pages and 7 figures, submitted for IEEE Transactions on Man, systems ,and Cybernetics

In situations involving teams of diverse robots, assigning appropriate roles to each robot and evaluating their performance is crucial. These roles define the specific characteristics of a robot within a given context. The stream actions exhibited by a robot based on its assigned role are referred to as the process role. Our research addresses the depiction of process roles using a multivariate probabilistic function. The main aim of this study is to develop a role engine for collaborative multi-robot systems and optimize the behavior of the robots. The role engine is designed to assign suitable roles to each robot, generate approximately optimal process roles, update them on time, and identify instances of robot malfunction or trigger replanning when necessary. The environment considered is dynamic, involving obstacles and other agents. The role engine operates hybrid, with central initiation and decentralized action, and assigns unlabeled roles to agents. We employ the Gaussian Process (GP) inference method to optimize process roles based on local constraints and constraints related to other agents. Furthermore, we propose an innovative approach that utilizes the environment's skeleton to address initialization and feasibility evaluation challenges. We successfully demonstrated the proposed approach's feasibility, and efficiency through simulation studies and real-world experiments involving diverse mobile robots., Comment: 10 pages, 18 figures, summited in IEEE Transactions on Systems, Man and Cybernetics(T-SMC)

This paper focuses on the problematic position tracking of a quadcopter under external disturbances by developing a super-twisting sliding mode controller (STSMC) based on a higher-order sliding mode observer (HOSMO). A generalized quadcopter model is presented with position and attitude dynamics. This observer-based controller design is applied adaptable for future hardware implementation on a COEX Clover 4.2. The Clover drone has measurable position signal using vision-based navigation and ArUco marker localization, where the HOSMO can estimate the unknown state being linear velocity. HOSMO helps in estimating the unknown external disturbances on the quadcopter for possible compensations. The proposed controller has been applied to the position dynamics with extensive simulations. If desired, it could be applied to the attitude dynamics as well. Lyapunov- based method is used to investigate the closed-loop stability and convergence while a smoothing function was used to attenuate potential chattering problems. Part of the Proceedings of the Canadian Society for Mechanical Engineering International Congress 2022.