Placing Arrows in Directed Graph Drawings

We consider the problem of placing arrow heads in directed graph drawings without them overlapping other drawn objects. This gives drawings where edge directions can be deduced unambiguously. We show hardness of the problem, present exact and heuristic algorithms, and report on a practical study.Comment: Appears in the Proceedings of the 24th International Symposium on Graph Drawing and Network Visualization (GD 2016

Effects of workplace-based dietary and/or physical activity interventions for weight management targeting healthcare professionals : a systematic review of randomised controlled trials

BACKGROUND: The prevalence of overweight and obesity is high amongst healthcare professionals and there is growing interest in delivering weight loss interventions in the workplace. We conducted a systematic review to (i) examine the effectiveness of workplace-based diet and/or physical activity interventions aimed at healthcare professionals and to (ii) identify and describe key components of effective interventions. Seven electronic databases were systematically searched. RESULTS: Thirteen randomised controlled trials met the inclusion criteria, of which seven had data available for meta-analysis. Where meta-analysis was possible, studies were grouped according to length of follow-up (<12 months and ≥12 months) and behavioural target (diet only, physical activity only or diet and physical activity), with outcome data pooled using a weighted random effects model. Nine studies reported statistically significant (between-group) differences. Four studies reported being informed by a behaviour change theory. Meta-analysis of all trials reporting weight data demonstrated healthcare professionals allocated to dietary and physical activity interventions lost significantly more body weight (-3.95 Kg, [95% CI -4.96 to- 2.95 Kg]) than controls up to 12 months follow up. CONCLUSIONS: Workplace diet and/or physical activity interventions targeting healthcare professionals are limited in number and are heterogeneous. To improve the evidence base, we recommend additional evaluations of theory-based interventions and adequate reporting of intervention content.Peer reviewedFinal Published versio

A participatory physical and psychosocial intervention for balancing the demands and resources among industrial workers (PIPPI): study protocol of a cluster-randomized controlled trial

Background: Need for recovery and work ability are strongly associated with high employee turnover, well-being and sickness absence. However, scientific knowledge on effective interventions to improve work ability and decrease need for recovery is scarce. Thus, the present study aims to describe the background, design and protocol of a cluster randomized controlled trial evaluating the effectiveness of an intervention to reduce need for recovery and improve work ability among industrial workers. Methods/Design: A two-year cluster randomized controlled design will be utilized, in which controls will also receive the intervention in year two. More than 400 workers from three companies in Denmark will be aimed to be cluster randomized into intervention and control groups with at least 200 workers (at least 9 work teams) in each group. An organizational resources audit and subsequent action planning workshop will be carried out to map the existing resources and act upon initiatives not functioning as intended. Workshops will be conducted to train leaders and health and safety representatives in supporting and facilitating the intervention activities. Group and individual level participatory visual mapping sessions will be carried out allowing team members to discuss current physical and psychosocial work demands and resources, and develop action plans to minimize strain and if possible, optimize the resources. At all levels, the intervention will be integrated into the existing organization of work schedules. An extensive process and effect evaluation on need for recovery and work ability will be carried out via questionnaires, observations, interviews and organizational data assessed at several time points throughout the intervention period. Discussion: This study primarily aims to develop, implement and evaluate an intervention based on the abovementioned features which may improve the work environment, available resources and health of industrial workers, and hence their need for recovery and work ability

Extending our scientific reach in arboreal ecosystems for research and management

The arboreal ecosystem is vitally important to global and local biogeochemical processes, the maintenance of biodiversity in natural systems, and human health in urban environments. The ability to collect samples, observations, and data to conduct meaningful scientific research is similarly vital. The primary methods and modes of access remain limited and difficult. In an online survey, canopy researchers (n = 219) reported a range of challenges in obtaining adequate samples, including ∼10% who found it impossible to procure what they needed. Currently, these samples are collected using a combination of four primary methods: (1) sampling from the ground; (2) tree climbing; (3) constructing fixed infrastructure; and (4) using mobile aerial platforms, primarily rotorcraft drones. An important distinction between instantaneous and continuous sampling was identified, allowing more targeted engineering and development strategies. The combination of methods for sampling the arboreal ecosystem provides a range of possibilities and opportunities, particularly in the context of the rapid development of robotics and other engineering advances. In this study, we aim to identify the strategies that would provide the benefits to a broad range of scientists, arborists, and professional climbers and facilitate basic discovery and applied management. Priorities for advancing these efforts are (1) to expand participation, both geographically and professionally; (2) to define 2–3 common needs across the community; (3) to form and motivate focal teams of biologists, tree professionals, and engineers in the development of solutions to these needs; and (4) to establish multidisciplinary communication platforms to share information about innovations and opportunities for studying arboreal ecosystems

Labeling points with circles

We present a new algorithm for labeling points with circles of equal size. Our algorithm tries to maximize the label size. It improves the approximation factor of the only known algorithm for this problem by more than 50% to about 1/20. At the same time, our algorithm keeps the O(nlog n) time bound of its predecessor. In addition, we show that the decision problem is NP-hard and that it is NP-hard to approximate the maximum label size beyond a certain constant factor

Labeling points with weights

Annotating maps, graphs, and diagrams with pieces of text is an important step in information visualization that is usually referred to as label placement. We define nine label-placement models for labeling points with axis-parallel rectangles given a weight for each point. There are two groups: fixed-position models and slider models. We aim to maximize the weight sum of those points that receive a label. We first compare our models by giving bounds for the ratios between the weights of maximum-weight labelings in different models. Then we present algorithms for labeling n points with unit-height rectangles. We show how an 0 (n log n)-time factor-2 approximation algorithm and a PTAS for fixed-position models can be extended to handle the weighted case. Our main contribution is the first algorithm for weighted sliding labels. Its approximation factor is (2 + epsilon), it runs in O(n(2)/epsilon) time and uses 0(n/epsilon) space. We show that other than for fixed-position models even the projection to one dimension remains NP-hard. For slider models we also investigate some special cases, namely (a) the number of different point weights is bounded, (b) all labels are unit squares, and (c) the ratio between maximum and minimum label height is bounded