Efficient Multi-Robot Coverage of a Known Environment

This paper addresses the complete area coverage problem of a known environment by multiple-robots. Complete area coverage is the problem of moving an end-effector over all available space while avoiding existing obstacles. In such tasks, using multiple robots can increase the efficiency of the area coverage in terms of minimizing the operational time and increase the robustness in the face of robot attrition. Unfortunately, the problem of finding an optimal solution for such an area coverage problem with multiple robots is known to be NP-complete. In this paper we present two approximation heuristics for solving the multi-robot coverage problem. The first solution presented is a direct extension of an efficient single robot area coverage algorithm, based on an exact cellular decomposition. The second algorithm is a greedy approach that divides the area into equal regions and applies an efficient single-robot coverage algorithm to each region. We present experimental results for two algorithms. Results indicate that our approaches provide good coverage distribution between robots and minimize the workload per robot, meanwhile ensuring complete coverage of the area.Comment: In proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 201

ESRC-DFID Research for Policy and Practice: Women's Life Choices

This collection of ESRC-DFID-funded research identifies critical elements that are important to address if women’s and girls’ lives are to change for the better. The research looks at the mobility constraints experienced by girls and how a lack of access to means of transport hampers their access to paid work, health services, and schooling. It also identifies the barriers that women face when it comes to attending lifesaving diagnostic treatment or accessing maternal health-care services. Furthermore, it emphasises the significant role of education systems in not only enhancing women’s economic opportunities but also in helping to bridge the gender gap by shaping young people’s aspirations in their future career choices.ESRC-DFI

Improvements in 3D sediment transport modelling with application to water quality issues

Water Qualit

Automated Grain Yield Behavior Classification

A method for classifying grain stress evolution behaviors using unsupervised learning techniques is presented. The method is applied to analyze grain stress histories measured in-situ using high-energy X-ray diffraction microscopy (HEDM) from the aluminum-lithium alloy Al-Li 2099 at the elastic-plastic transition (yield). The unsupervised learning process automatically classified the grain stress histories into four groups: major softening, no work-hardening or softening, moderate work-hardening, and major work-hardening. The orientation and spatial dependence of these four groups are discussed. In addition, the generality of the classification process to other samples is explored

Recent advances in the application of stable isotope ratio analysis in forensic chemistry

This review paper updates the previous literature in relation to the continued and developing use of stable isotope ratio analysis in samples which are relevant to forensic science. Recent advances in the analysis of drug samples, explosive materials, and samples derived from human and animal samples are discussed. The paper also aims to put the use of isotope ratio mass spectrometry into a forensic context and discuss its evidential potential

New Representation of Bearings in LS-DYNA

Non-linear, dynamic, finite element analysis is used in various engineering disciplines to evaluate high-speed, dynamic impact and vibration events. Some of these applications require connecting rotating to stationary components. For example, bird impacts on rotating aircraft engine fan blades are a common analysis performed using this type of analysis tool. Traditionally, rotating machines utilize some type of bearing to allow rotation in one degree of freedom while offering constraints in the other degrees of freedom. Most times, bearings are modeled simply as linear springs with rotation. This is a simplification that is not necessarily accurate under the conditions of high-velocity, high-energy, dynamic events such as impact problems. For this reason, it is desirable to utilize a more realistic non-linear force-deflection characteristic of real bearings to model the interaction between rotating and non-rotating components during dynamic events. The present work describes a rolling element bearing model developed for use in non-linear, dynamic finite element analysis. This rolling element bearing model has been implemented in LS-DYNA as a new element, *ELEMENT_BEARING

Burnout and Substance Use in Collegiate Athletic Trainers

CONTEXT: The Smith Cognitive-Affective Model of Athletic Burnout suggests that athletic trainers (ATs) suffering from burnout may engage in substance use as a coping behavior. Increases in self-reported burnout symptoms are often associated with increases in heavy episodic drinking and tobacco use among various health care providers. However, this relationship has not been examined thoroughly. OBJECTIVE: To investigate the prevalence of substance use in ATs and identify relationships between symptoms of burnout and substance use among ATs. DESIGN: Cross-sectional study. SETTING: Web-based survey. PATIENTS OR OTHER PARTICIPANTS: A total of 783 certified ATs working full time in the collegiate or university setting were sampled for this study. Graduate assistant and other part-time ATs were excluded. The survey was distributed via the National Athletic Trainers\u27 Association membership directory e-mail broadcast service. MAIN OUTCOME MEASURE(S): A 100-item online questionnaire consisting of items from previously used scales was used for this study. The survey included the Maslach Burnout Inventory and questions on substance use from the Monitoring the Future study. Multiple regression analyses were performed to analyze the survey data. All independent (Maslach Burnout Inventory subscales) and dependent (use of alcohol, tobacco, and marijuana) variables were mapped to the Smith Cognitive-Affective Model of Athletic Burnout to determine which dimensions of burnout altered the odds of self-reported substance use. RESULTS: Almost half (46.3%) of participants admitted to at least 1 binge-drinking episode. However, the use of cigarettes, smokeless tobacco, marijuana, and energy drinks during the previous month was less pronounced in the sample. Emotional exhaustion (B = .008, P = .023) and personal accomplishment (B = -.016, P = .02) were significantly correlated with binge drinking. Emotional exhaustion (Exp[B] = 1.017, P \u3c .001) was also significantly positively correlated with energy-drink consumption. CONCLUSIONS: Some ATs engaged in heavy episodic drinking. Emotional exhaustion and a decreased sense of personal accomplishment were significantly correlated with this behavior