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1. The pharmacokinetics and disposition of delafloxacin was investigated following a single intravenous (300 mg, 100 µCi) dose to healthy male subjects. 2. Mean Cmax, AUC0-∞, Tmax and t1/2 values for delafloxacin were 8.98 µg/mL, 21.31 µg h/mL, 1 h and 2.35 h, respectively, after intravenous dosing. 3. Radioactivity was predominantly excreted via the kidney with 66% of the radioactive dose recovered in the urine. Approximately 29% of the radioactivity was recovered in the faeces, giving an overall mean recovery of 94% administered radioactivity. 4. The predominant circulating components were identified as delafloxacin and a direct glucuronide conjugate of delafloxacin.

The use of conventional actuators in robotic systems (electric motors in particular), while often offering advantages in terms of flexibility and controllability, suffer from primary actuator failure, due to unexpected complexities in their environment, which can lead to loss of controllability. Conventional actuators can impose disadvantages on mechanical complexity, weight, and cost. Here, the Mixed conventional/braking Actuation Mobile Robot (MAMR), a new mobile robot platform, is proposed to tackle such drawbacks in actuation and explore the use and control of braking actuation. This platform substitutes the drive motors used in Ackermann steering with brakes that have only two states, ON and OFF. Additionally, the conventional drive wheels are replaced by a single, omni-directional wheel that only supports a driving force in the robots longitudinal direction. The ability of braking actuators in providing controllability under actuator failure is one of the primary motivations of this work. To validate the reliability and accuracy of MAMR approach, this paper studies the design of such robotic systems, the design and synthesis of fuzzy logic controllers along with the experimental assessments of these controllers in real-time. The experimental tests point out the controller performance enhancement using fuzzy logic controllers and MAMR.

Abstract-The pose detection of three-dimensional (3-D) objects from two-dimensional (2-D) images is an important issue in computer vision and robotics applications. Specific examples include automated assembly, automated part inspection, robotic welding, and human robot interaction, as well as a host of others. Eigendecomposition is a common technique for dealing with this issue and has been applied to sets of correlated images for this purpose. Unfortunately, for the pose detection of 3-D objects, a very large number of correlated images must be captured from many different orientations. As a result, the eigendecomposition of this large set of images is very computationally expensive. In this work, we present a method for capturing images of objects from many locations by sampling S 2 appropriately. Using this spherical sampling pattern, the computational burden of computing the eigendecomposition can be reduced by using the Spherical Harmonic Transform to "condense" information due to the correlation in S 2 . We propose a computationally efficient algorithm for approximating the eigendecomposition based on the spherical harmonic transform analysis. Experimental results are presented to compare and contrast the algorithm against the true eigendecomposition, as well as quantify the computational savings.