This paper presents a methodology for computation of artificial vector fields that allows a robot to converge to and circulate around generic curves specified in n-dimensional spaces. These vector fields may be directly applied to solve several robotnavigation problems such as border monitoring, surveillance, target tracking, and multirobot pattern generation, with special application to fixed-wing aerial robots, which must keep a positive forward velocity and cannot converge to a single point. Unlike previous solutions found in the literature, the approach is based on fully continuous vector fields and is generalized to time-varying curves defined in n-dimensional spaces. We provide mathematical proofs and present simulation and experimental results that illustrate the applicability of the proposed approach. We also present a methodology for construction of the target curve based on a given set of its samples.

This paper presents a solution for the problem of minimum time coverage of ground areas using a group of unmanned air vehicles (UAVs) equipped with image sensors. The solution is divided into two parts: (i) the task modeling as a graph whose vertices are geographic coordinates determined in such a way that a single UAV would cover the area in minimum time; and (ii) the solution of a mixed integer linear programming problem, formulated according to the graph variables defined in the first part, to route the team of UAVs over the area. The main contribution of the proposed methodology, when compared with the traditional vehicle routing problem’s (VRP) solutions, is the fact that our method solves some practical problems only encountered during the execution of the task with actual UAVs. In this line, one of the main contributions of the paper is that the number of UAVs used to cover the area is automatically selected by solving the optimization problem. The number of UAVs is influenced by the vehicles’ maximum flight time and by the setup time, which is the time needed to prepare and launch a UAV. To illustrate the methodology, the paper presents experimental results obtained with two hand-launched, fixed-wing UAVs.

Vargas, VZ, Baptista, AF, Pereira, GOC, Pochini, AC, Ejnisman, B, Santos, MB, João, SMA, and Hazime, FA. Modulation of isometric quadriceps strength in soccer players with transcranial direct current stimulation: a crossover study. J Strength Cond Res 32(5): 1336-1341, 2018-The aim of this study was to evaluate the effect of transcranial direct current stimulation (tDCS) on the maximum isometric muscle contraction (MVIC) of the knee extensors in soccer players at the preprofessional level. Twenty female soccer players aged 15-17 years (mean = 16.1; SD = 0.9) with 5.2 ± 2.6 years of training were randomly divided into 2 groups to receive either active or sham tDCS in a single session (2 mA; 0.057 mA·cm). The MVIC of the knee extensors was evaluated in both lower limbs by manual dynamometry in 5 sets of contractions divided into 4 blocks: (a) prestimulation, (b) during tDCS, (c) 30 minutes after tDCS, and (d) 60 minutes after tDCS. After an interval of 7 days, the groups were evaluated again, and the type of initial stimulation was inverted between participants. The MVIC of the knee extensors increased significantly during active tDCS (dominant limb (DL) = 0.4; IC = 0.1-0.8 N·Kg), 30 minutes after active tDCS (DL = 0.9; IC 0.4-1.4 N·Kg), and 60 minutes after active tDCS (DL = 1.0; IC 0.3-1.6 N·Kg) but not for sham tDCS. Our conclusion was that tDCS temporarily increases isometric quadriceps strength in adolescent female soccer players, which may be useful for both strength training and rehabilitation.

It may be that resistance exercise can be used to prevent the degenerative processes and inflammation associated with ageing. Thus, the aim of the present study was to evaluate the effects of resistance training on cytokines, leptin, resistin, and muscle strength in post-menopausal women. Thirty-five sedentary women (mean age 63.18 years, s = 4.8; height 1.64 m, s = 0.07; body mass 57.84 kg, s = 7.70) were recruited. The 16 weeks of periodized resistance training consisted of two weekly sessions of three sets of 6-14 repetition maximum. Maximal strength was tested in bench press, 45 degrees leg press, and arm curl. Plasma tumour necrosis factor-alpha, interleukin-6, interleukin-15, leptin, and resistin were determined by enzyme-linked immunosorbent assay. Maximal strength on all measures was increased after 16 weeks. There were minor or no modifications in tumour necrosis factor-alpha and interleukin-15. Interleukin-6 was decreased 48 h after compared with baseline and declined after 16 weeks. Leptin decreased 24 h after compared with baseline and was reduced at baseline and 48 h after compared with pre-training. There was a decrease in resistin after 24 and 48 h compared with baseline and a decline in baseline and immediately after levels compared with pre-training. A possible explanation of the results of the present study is a lower production of pro-inflammatory cytokines by the innate immune system. Periodized resistance training seems to be an important intervention to reduce systemic inflammation in this population.

We consider search and rescue applications in which heterogeneous groups of agents (humans, robots, static and mobile sensors) enter an unknown building and disperse while following gradients in temperature and concentration of toxins, and looking for immobile humans. The agents deploy the static sensors and maintain line of sight visibility and communication connectivity whenever possible. Since different agents have different sensors and therefore different pieces of information, communication is necessary for tasking the network, sharing information, and for control.

The aim of the present study was to investigate the effects of resistance training on skeletal muscle lipid content, liver lipid content, heart lipid content, fat depots, and lipid profile in ovariectomized rats. Wistar adult female rats were divided into 4 groups (n = 10 per group): sedentary (Sed-Intact), sedentary ovariectomized (Sed-Ovx), strength trained (ChronicEx-intact), and strength trained ovariectomized (ChronicEx-Ovx). A 12-week strength-training period was used, during which the animals climbed a 1.1-m vertical ladder with weights attached to their tails. The sessions were performed once every 3 days, with 4-9 climbs and 8-12 dynamic movements per climb. Ovariectomy increased liver lipid content and fat depots, and heart and muscle lipid content. There was an increase in the atherogenic index and a negative change in lipid profile because of the ovariectomy. Resistance training decreased lipid content in the liver, soleus, and tibialis anterior, decreased fat depots (mesenteric and retroperitoneal), and changed the lipid profile, independently of ovarian hormone status. These results indicate the potential benefits of resistance training as an alternative strategy to control the effects of ovariectomy on fat depot, lipid profile, and tissue lipid content.

The Gleason score is the most important prognostic marker for prostate cancer patients, but it suffers from significant observer variability. Artificial intelligence (AI) systems based on deep learning can achieve pathologist-level performance at Gleason grading. However, the performance of such systems can degrade in the presence of artifacts, foreign tissue, or other anomalies. Pathologists integrating their expertise with feedback from an AI system could result in a synergy that outperforms both the individual pathologist and the system. Despite the hype around AI assistance, existing literature on this topic within the pathology domain is limited. We investigated the value of AI assistance for grading prostate biopsies. A panel of 14 observers graded 160 biopsies with and without AI assistance. Using AI, the agreement of the panel with an expert reference standard increased significantly (quadratically weighted Cohen's kappa, 0.799 vs. 0.872; p = 0.019). On an external validation set of 87 cases, the panel showed a significant increase in agreement with a panel of international experts in prostate pathology (quadratically weighted Cohen's kappa, 0.733 vs. 0.786; p = 0.003). In both experiments, on a grouplevel, AI-assisted pathologists outperformed the unassisted pathologists and the standalone AI system. Our results show the potential of AI systems for Gleason grading, but more importantly, show the benefits of pathologist-AI synergy. Members of the ISUP Pathology Imagebase Expert Panel are listed below Acknowledgements.

This paper addresses the problem of transporting objects with multiple mobile robots using the concept of object closure. In contrast to other manipulation techniques that are typically derived from form or force closure constraints, object closure requires the less stringent condition that the object be trapped or caged by the robots. Our basic goal in this paper is to develop decentralized control policies for a group of robots to achieve a condition of object closure, and then, move toward a goal position while maintaining this condition. We present experimental results that show car-like robots controlled using visual feedback, transporting an object in an obstacle free environment toward a prescribed goal. Abstract. This paper addresses the problem of transporting objects with multiple mobile robots using the concept of object closure. In contrast to other manipulation techniques that are typically derived from form or force closure constraints, object closure requires the less stringent condition that the object be trapped or caged by the robots. Our basic goal in this paper is to develop decentralized control policies for a group of robots to achieve a condition of object closure, and then, move toward a goal position while maintaining this condition. We present experimental results that show car-like robots controlled using visual feedback, transporting an object in an obstacle free environment toward a prescribed goal.