Evaluation of cooperative exploration strategies using full system simulation

This paper compares different state-of-the-art exploration strategies for teams of mobile robots exploring an unknown environment. The goal is to help in determining a best strategy for a given multi-robot scenario and optimization target. Experiments are done in a 2D-simulation environment with 5 robots that are equipped with a horizontal laser range finder. Required components like SLAM, path planning and obstacle avoidance of every robot are included in a full-system simulation. To evaluate different strategies the time to finish exploration, the number of measurements that have been integrated into the map and the development in size of the explored area over time are used. The results of extensive test runs on three environments with different characteristics show that simple strategies can perform fairly well in many situations but specialized strategies can improve performance with regards to their targeted evaluation measure.

Nursing and medical perceptions of a hospital rapid response system: New process but same old game?

Perhaps no other patient safety intervention depends so acutely on effective interprofessional teamwork for patient survival than the hospital rapid response system (RRS). Yet little is known about nurse-physician relationships when rescuing at-risk patients. This study compared nursing and medical staff perceptions of a mature RRS at a large tertiary hospital. Findings indicate the RRS may be failing to address a hierarchical culture and systems-level barriers to early recognition and response to patient deterioration.

Sectionalizing strategies for minimizing outage durations of critical loads in parallel power system restoration with bi-level programming

Fast restoration of critical loads and non-black-start generators can significantly reduce the economic losses caused by power system blackouts. In a parallel power system restoration scenario, the sectionalization of restoration subsystems plays a very important role in determining the pickup of critical loads before synchronization. Most existing research mainly focuses on the startup of non-black-start generators. The restoration of critical loads, especially the loads with cold load characteristics, has not yet been addressed in optimizing the subsystem divisions. As a result, sectionalized restoration subsystems cannot achieve the best coordination between the pickup of loads and the ramping of generators. In order to generate sectionalizing strategies considering the pickup of critical loads in parallel power system restoration scenarios, an optimization model considering power system constraints, the characteristics of the cold load pickup and the features of generator startup is proposed in this paper. A bi-level programming approach is employed to solve the proposed sectionalizing model. In the upper level the optimal sectionalizing problem for the restoration subsystems is addressed, while in the lower level the objective is to minimize the outage durations of critical loads. The proposed sectionalizing model has been validated by the New-England 39-bus system and the IEEE 118-bus system. Further comparisons with some existing methods are carried out as well.

Technology transfer offices as boundary spanners in the pre-spin-off process: The case of a hybrid model

Over the past decades, universities have increasingly become ambidextrous organizations reconciling scientific and commercial missions. In order to manage this ambidexterity, technology transfer offices (TTOs) were established in most universities. This paper studies a specific, often implemented, but rather understudied type of TTO, namely a hybrid TTO model uniting centralized and decentralized levels. Employing a qualitative research design, we examine how and why the two TTO levels engage in diverse boundary spanning activities to help nascent spin-off companies move through the pre-spin-off process. Our research identifies differences in the types of boundary spanning activities that centralized and decentralized TTOs perform and in the parties they engage with. We find geographical, technological and organizational proximity to be important antecedents of the TTOs’ engagement in external and internal boundary spanning activities. These results have important implications for both academics and practitioners interested in university technology transfer through spin-off creation.

Typologies and touchpoints: Designing multi-channel digital strategies

Purpose This study aims to gain a clearer understanding of digital channel design. The emergence of new technologies has revolutionised the way companies interact and engage with customers. The driver for this research was the suggestion that practitioners feel they do not possess the skills to understand and exploit new digital channel opportunities. To gain a clearer understanding of digital channel design, this paper addresses the research question: What digital channels do companies from a wide range of industries and sectors use? Design/methodology/approach A content analysis of 100 international companies was conducted with multiple data sources to form a typology of digital “touchpoints”. The appropriateness of a digital channel typology for this study was for developing rigorous and useful concepts for clarifying and refining the meaning of digital channels. Findings This study identifies what digital channels companies globally currently employ and explores the related needs across industries. A total of 34 digital touchpoints and 4 typologies of digital channels were identified across 16 industries. This research helps to identify the relationship between digital channels and enabling the connections with industry. Research limitations/implications The findings contribute to the growing research area of digital channels. The typology of digital channels is a useful starting point for developing a systematic, theory-based study for enabling the development of broader, comprehensive theories of digital channels. Practical implications Typologies and touchpoints are outlined in relation to industry, company objectives and customer needs to allow businesses to seize opportunities and optimise performance through individual touchpoints. A digital channel model as a key outcome of this research guides practitioners on what touchpoint to implement through an interrelated understanding of industry, company and customer needs. Originality/value This is the first paper to explore a range of industries in relation to their use of digital channels using a unique content analysis. Contributions include clarifying and refining digital channel meaning; identifying and refining the hierarchical relations among digital channels(typologies); and establishing typology and industry relationship model.

Young and unaffected by road policing strategies: Using deterrence theory to explain provisional drivers' (non)compliance

Newly licenced drivers are disproportionately represented in traffic injuries and crash statistics. Despite the implementation of countermeasures designed to improve safety, such as graduated driver licencing (GDL) schemes, many young drivers do not comply with road rules. This study used a reconceptualised deterrence theory framework to investigate young drivers’ perceptions of the enforcement of road rules in general and those more specifically related to GDL. A total of 236 drivers aged 17–24 completed a questionnaire assessing their perceptions of various deterrence mechanisms (personal and vicarious) and their compliance with both GDL-specific and general road rules. Hierarchical multiple regressions conducted to explore noncompliant behaviour revealed that, contrary to theoretical expectations, neither personal nor vicarious punishment experiences affected compliance in the expected direction. Instead, the most influential factors contributing to noncompliance were licence type (P2) and, counterintuitively, having previously been exposed to enforcement. Parental enforcement was also significant in the prediction of transient rule violations, but not fixed rule violations or overall noncompliance. Findings are discussed in light of several possibilities, including an increase in violations due to more time spent on the road, an ‘emboldening effect’ noted in prior studies and possible conceptual constraints regarding the deterrence variables examined in this study.

DoGood : A gamified mobile app to promote civic engagement

The rise of the mobile Internet enables the creation of applications that provide new and easier ways for people to organise themselves, raise issues, take action and interact with their city. However, a lack of motivation or knowledge often prevents many citizens from regularly contributing to the common good. Therefore, this thesis presents DoGood, a smartphone app, that aims at motivating citizens to carry out civic activities. The thesis asks what kinds of activities citizens consider to be civic and to what extent gamification can motivate users in this context. The DoGood app uses gamified elements to encourage citizens to submit and promote their civic activities as well as to join the activities of others. Gamification is sometimes criticized for simply adding a limited number of game elements, such as leaderboards, on top of an existing experience. However, in the case of the DoGood app, the process of game design was an integral part of the development, and the gamified elements target the user’s intrinsic motivations instead of providing them with an external reward. DoGood was implemented as hybrid mobile app and deployed to citizens of Brisbane in a five weeks long user study. The app successfully motivated most of its users to do more civic activities and its gamified elements were well received. Based on the results of the user study, civic activities can be defined as activities that give citizens the opportunity to become involved and improve life in their local community.

What can game AI teach us?

Artificial intelligence (AI) applications typically involve encoding expert knowledge in machine form to find optimal solutions for a given problem. However, this paper deals with the opposite process of extracting new and human-comprehensible insights from emergent AI behaviour. Some examples of useful game-related insights drawn from observing AI players in action are presented.

Paediatric death and dying: Exploring coping strategies of health professionals and perceptions of support provision

Without question a child’s death is a devastating event for parents and their families. Health professionals working with the dying child and family draw upon their expertise and experience to engage with children, parents, and families on this painful journey. A delicate and sensitive area of practice, it has strong and penetrating effects on health professionals. They employ physical, emotional, spiritual and problem solving strategies to continue to perform this role effectively and to protect their continued sense of well-being. Aim To explore health professionals’ perceptions of bereavement support surrounding the loss of a child. Methods The research was underpinned by social constructionism. Semi-structured interviews were held with 10 health professionals including doctors, nurses and social workers who were directly involved in the care of the dying child and family in 7 cases of paediatric death. Health professional narratives were analysed consistent with Charmarz’s (2006) approach. Results For health professionals, constructions around coping emerged as peer support, personal coping strategies, family support, physical impact of support and spiritual beliefs . Analysis of the narratives also revealed health professionals’ perceptions of their support provision. Conclusion Health professionals involved in caring for dying children and their families use a variety of strategies to cope with the emotional and physical toll of providing support. They also engage in self-assessment to evaluate their support provision and this highlights the need for self-evaluation tools in paediatric palliative care.

Design and evaluation of decision and control strategies for autonomous vision-based see and avoid systems

This paper details the design and performance assessment of a unique collision avoidance decision and control strategy for autonomous vision-based See and Avoid systems. The general approach revolves around re-positioning a collision object in the image using image-based visual servoing, without estimating range or time to collision. The decision strategy thus involves determining where to move the collision object, to induce a safe avoidance manuever, and when to cease the avoidance behaviour. These tasks are accomplished by exploiting human navigation models, spiral motion properties, expected image feature uncertainty and the rules of the air. The result is a simple threshold based system that can be tuned and statistically evaluated by extending performance assessment techniques derived for alerting systems. Our results demonstrate how autonomous vision-only See and Avoid systems may be designed under realistic problem constraints, and then evaluated in a manner consistent to aviation expectations.

A hybrid of modified PSO and local search on a multi-robot search system

Particle swarm optimization (PSO), a new population based algorithm, has recently been used on multi-robot systems. Although this algorithm is applied to solve many optimization problems as well as multi-robot systems, it has some drawbacks when it is applied on multi-robot search systems to find a target in a search space containing big static obstacles. One of these defects is premature convergence. This means that one of the properties of basic PSO is that when particles are spread in a search space, as time increases they tend to converge in a small area. This shortcoming is also evident on a multi-robot search system, particularly when there are big static obstacles in the search space that prevent the robots from finding the target easily; therefore, as time increases, based on this property they converge to a small area that may not contain the target and become entrapped in that area.Another shortcoming is that basic PSO cannot guarantee the global convergence of the algorithm. In other words, initially particles explore different areas, but in some cases they are not good at exploiting promising areas, which will increase the search time.This study proposes a method based on the particle swarm optimization (PSO) technique on a multi-robot system to find a target in a search space containing big static obstacles. This method is not only able to overcome the premature convergence problem but also establishes an efficient balance between exploration and exploitation and guarantees global convergence, reducing the search time by combining with a local search method, such as A-star.To validate the effectiveness and usefulness of algorithms,a simulation environment has been developed for conducting simulation-based experiments in different scenarios and for reporting experimental results. These experimental results have demonstrated that the proposed method is able to overcome the premature convergence problem and guarantee global convergence.

Identifying and forecasting the reverse salient in video game consoles: A performance gap ratio comparative analysis

This study uses the reverse salient methodology to contrast subsystems in video game consoles in order to discover, characterize, and forecast the most significant technology gap. We build on the current methodologies (Performance Gap and Time Gap) for measuring the magnitude of Reverse Salience, by showing the effectiveness of Performance Gap Ratio (PGR). The three subject subsystems in this analysis are the CPU Score, GPU core frequency, and video memory bandwidth. CPU Score is a metric developed for this project, which is the product of the core frequency, number of parallel cores, and instruction size. We measure the Performance Gap of each subsystem against concurrently available PC hardware on the market. Using PGR, we normalize the evolution of these technologies for comparative analysis. The results indicate that while CPU performance has historically been the Reverse Salient, video memory bandwidth has taken over as the quickest growing technology gap in the current generation. Finally, we create a technology forecasting model that shows how much the video RAM bandwidth gap will grow through 2019 should the current trend continue. This analysis can assist console developers in assigning resources to the next generation of platforms, which will ultimately result in longer hardware life cycles.

Post traumatic success: Positive psychology and solution-focused strategies to help clients survive and thrive

I am an academic who has spent a career using a strengths-based approach in researching ways to promote and maintain mental health in people who have experienced trauma. When I read the title of the book, the notion of “post traumatic success” immediately brought many questions to my mind. What is success anyway? How do we measure success following trauma? Who decides if a person has been successful? However, as I started to read, the intention of the book became clear and my bias regarding the title lessened...

External knowledge and query strategies in active learning: A study in clinical information extraction

This paper presents a new active learning query strategy for information extraction, called Domain Knowledge Informativeness (DKI). Active learning is often used to reduce the amount of annotation effort required to obtain training data for machine learning algorithms. A key component of an active learning approach is the query strategy, which is used to iteratively select samples for annotation. Knowledge resources have been used in information extraction as a means to derive additional features for sample representation. DKI is, however, the first query strategy that exploits such resources to inform sample selection. To evaluate the merits of DKI, in particular with respect to the reduction in annotation effort that the new query strategy allows to achieve, we conduct a comprehensive empirical comparison of active learning query strategies for information extraction within the clinical domain. The clinical domain was chosen for this work because of the availability of extensive structured knowledge resources which have often been exploited for feature generation. In addition, the clinical domain offers a compelling use case for active learning because of the necessary high costs and hurdles associated with obtaining annotations in this domain. Our experimental findings demonstrated that 1) amongst existing query strategies, the ones based on the classification model’s confidence are a better choice for clinical data as they perform equally well with a much lighter computational load, and 2) significant reductions in annotation effort are achievable by exploiting knowledge resources within active learning query strategies, with up to 14% less tokens and concepts to manually annotate than with state-of-the-art query strategies.