Questions about multidisciplinary teams

Multidisciplinary care (MDC) involves health professionals from a range of disciplines working together as a team (a multidisciplinary team – MDT) to deliver comprehensive care that addresses as many of a patient's needs as possible. Writing in 2011, Wilcoxon and others concluded: ‘Multidisciplinary care is accepted as best practice in cancer treatment planning and care.’ Yet their report (of the national audit of multidisciplinary cancer care in Australia) indicated that two-thirds of the surveyed hospitals did not have a MDT. Further, they found that where teams did exist, one-third of patients were not told that their case would be discussed by the team; the MDT-recommended treatment plan was not included in the patient’s record one-quarter of the time; and less than 1 per cent of teams reported routine attendance by the tumour-specific minimum core team. There is sparse case authority as to the potential medico-legal consequences of MDC by MDTs. This article raises five questions about legal aspects of MDC for consideration. The questions are not limited to cancer care, as MDTs are increasingly used in other areas of medicine.

Automatic Lameness Detection in a Milking Robot : Instrumentation, measurement software, algorithms for data analysis and a neural network model

The aim of this thesis is to develop a fully automatic lameness detection system that operates in a milking robot. The instrumentation, measurement software, algorithms for data analysis and a neural network model for lameness detection were developed. Automatic milking has become a common practice in dairy husbandry, and in the year 2006 about 4000 farms worldwide used over 6000 milking robots. There is a worldwide movement with the objective of fully automating every process from feeding to milking. Increase in automation is a consequence of increasing farm sizes, the demand for more efficient production and the growth of labour costs. As the level of automation increases, the time that the cattle keeper uses for monitoring animals often decreases. This has created a need for systems for automatically monitoring the health of farm animals. The popularity of milking robots also offers a new and unique possibility to monitor animals in a single confined space up to four times daily. Lameness is a crucial welfare issue in the modern dairy industry. Limb disorders cause serious welfare, health and economic problems especially in loose housing of cattle. Lameness causes losses in milk production and leads to early culling of animals. These costs could be reduced with early identification and treatment. At present, only a few methods for automatically detecting lameness have been developed, and the most common methods used for lameness detection and assessment are various visual locomotion scoring systems. The problem with locomotion scoring is that it needs experience to be conducted properly, it is labour intensive as an on-farm method and the results are subjective. A four balance system for measuring the leg load distribution of dairy cows during milking in order to detect lameness was developed and set up in the University of Helsinki Research farm Suitia. The leg weights of 73 cows were successfully recorded during almost 10,000 robotic milkings over a period of 5 months. The cows were locomotion scored weekly, and the lame cows were inspected clinically for hoof lesions. Unsuccessful measurements, caused by cows standing outside the balances, were removed from the data with a special algorithm, and the mean leg loads and the number of kicks during milking was calculated. In order to develop an expert system to automatically detect lameness cases, a model was needed. A probabilistic neural network (PNN) classifier model was chosen for the task. The data was divided in two parts and 5,074 measurements from 37 cows were used to train the model. The operation of the model was evaluated for its ability to detect lameness in the validating dataset, which had 4,868 measurements from 36 cows. The model was able to classify 96% of the measurements correctly as sound or lame cows, and 100% of the lameness cases in the validation data were identified. The number of measurements causing false alarms was 1.1%. The developed model has the potential to be used for on-farm decision support and can be used in a real-time lameness monitoring system.

Lernen im Team: Zusammenhänge zwischen Personen- und Teammerkmalen und der Leistung von Multiplikatoren [Learning in teams: Relationships between individual and team characteristics and the performance of multipliers]

The continuous mutual transfer of knowledge and skills within work teams is increasingly important for organizational practice. According to the situational and experience-based approaches of applied learning research, certain individual and social prerequisites have to be met for successful learning in teams. In a field study at an automobile production site, it was investigated which personal characteristics of multipliers and which characteristics of teams are related to the performance of multipliers in 31 teams with 291 coworkers. Using multi-level analyses (HLM), the amount of variance explained by the predictor variables in teaching success of multipliers and learning success of coworkers was examined. Results showed that multipliers' conscientiousness and team cohesion were related to teaching success of multipliers; extraversion and team cohesion were related to the learning success of coworkers. In closing, the scientific and practical implications for the investigation and promotion of work-based learning processes in teams are discussed.

Dependence Logic: Investigations into Higher-Order Semantics Defined on Teams

This thesis is a study of a rather new logic called dependence logic and its closure under classical negation, team logic. In this thesis, dependence logic is investigated from several aspects. Some rules are presented for quantifier swapping in dependence logic and team logic. Such rules are among the basic tools one must be familiar with in order to gain the required intuition for using the logic for practical purposes. The thesis compares Ehrenfeucht-Fraïssé (EF) games of first order logic and dependence logic and defines a third EF game that characterises a mixed case where first order formulas are measured in the formula rank of dependence logic. The thesis contains detailed proofs of several translations between dependence logic, team logic, second order logic and its existential fragment. Translations are useful for showing relationships between the expressive powers of logics. Also, by inspecting the form of the translated formulas, one can see how an aspect of one logic can be expressed in the other logic. The thesis makes preliminary investigations into proof theory of dependence logic. Attempts focus on finding a complete proof system for a modest yet nontrivial fragment of dependence logic. A key problem is identified and addressed in adapting a known proof system of classical propositional logic to become a proof system for the fragment, namely that the rule of contraction is needed but is unsound in its unrestricted form. A proof system is suggested for the fragment and its completeness conjectured. Finally, the thesis investigates the very foundation of dependence logic. An alternative semantics called 1-semantics is suggested for the syntax of dependence logic. There are several key differences between 1-semantics and other semantics of dependence logic. 1-semantics is derived from first order semantics by a natural type shift. Therefore 1-semantics reflects an established semantics in a coherent manner. Negation in 1-semantics is a semantic operation and satisfies the law of excluded middle. A translation is provided from unrestricted formulas of existential second order logic into 1-semantics. Also game theoretic semantics are considerd in the light of 1-semantics.

Unpacking the impact of attachment to project teams on boundary-spanning behaviors

As business environments become even more competitive, project teams are required to make an effort to operate external linkages from within an organization or across organizational boundaries. Nevertheless, some members boundary-span less extensively, isolating themselves and their project teams from external environments. Our study examines why some members boundary-span more or less through the framework of group attachment theory. Data from 521 project-team members in construction and engineering industries revealed that the more individuals worry about their project team’s acceptance (group attachment anxiety), the more likely they are to perceive intergroup competition, and thus put more efforts into operating external linkages and resources to help their own teams outperform competitors. In contrast, a tendency to distrust their project teams (group attachment avoidance) generates members’ negative construal of their team’s external image, and thus fewer efforts are made at operating external linkages. Thus, project leaders and members with high group-attachment-anxiety may be best qualified for external tasks.

The first cut is the deepest: Repeated interactions of co-authorship and academic productivity in Nobel laureate teams

Despite much in-depth investigation of factors influencing the co-authorship evolution in various scientific fields, our knowledge about how efficiency or creativity is linked to the longevity of collaborative relationships remains very limited. We explore what Nobel laureates’ co-authorship patterns reveal about the nature of scientific collaborations looking at the intensity and success of scientific collaborations across fields and across laureates’ collaborative lifecycles in physics, chemistry, and physiology/medicine. We find that more collaboration with the same researcher is actually no better for advancing creativity: publications produced early in a sequence of repeated collaborations with a given coauthor tend to be published better and cited more than papers that come later in the collaboration with the same coauthor. Our results indicate that scientific collaboration involves conceptual complementarities that may erode over a sequence of repeated interactions.

Managing teams: Comparing organizational and sport psychological approaches to teamwork

This paper examines how teams and teamwork research have been conceptualised in the fields of sport psychology and organizational psychology. Specifically, it provides a close inspection of the general theoretical assumptions that inhere in the two disciplines. The results of a discursive analysis of research literature suggest that the fields have significantly different ways of conceptualising teams and teamwork and that conceptual borrowing may prove fruitful. A key argument is however, that in order for meaningful cross-fertilisation to take place a sound understanding of these differences is necessary. Working from this premise, the essential differences between sport and organizational approaches to teams are outlined. The paper is concluded with a discussion of contributions that organizational psychology can make to understandings of sport-oriented teams.

My robot Valentine: Could you fall in love with a robot?

Imagine it’s Valentine’s Day and you’re sitting in a restaurant across the table from your significant other, about to start a romantic dinner. As you gaze into each other’s eyes, you wonder how it can possibly be true that as well as not eating, your sweetheart does not – cannot – love you. Impossible, you think, as you squeeze its synthetic hand...

Place categorization and semantic mapping on a mobile robot

In this paper we focus on the challenging problem of place categorization and semantic mapping on a robot with-out environment-specific training. Motivated by their ongoing success in various visual recognition tasks, we build our system upon a state-of-the-art convolutional network. We overcome its closed-set limitations by complementing the network with a series of one-vs-all classifiers that can learn to recognize new semantic classes online. Prior domain knowledge is incorporated by embedding the classification system into a Bayesian filter framework that also ensures temporal coherence. We evaluate the classification accuracy of the system on a robot that maps a variety of places on our campus in real-time. We show how semantic information can boost robotic object detection performance and how the semantic map can be used to modulate the robot’s behaviour during navigation tasks. The system is made available to the community as a ROS module.

Glowworm-inspired robot swarm for simultaneous taxis towards multiple radiation sources

This paper presents a glowworm metaphor based distributed algorithm that enables a collection of minimalist mobile robots to split into subgroups, exhibit simultaneous taxis-behavior towards, and rendezvous at multiple radiation sources such as nuclear/hazardous chemical spills and fire-origins in a fire calamity. The algorithm is based on a glowworm swarm optimization (GSO) technique that finds multiple optima of multimodal functions. The algorithm is in the same spirit as the ant-colony optimization (ACO) algorithms, but with several significant differences. The agents in the glowworm algorithm carry a luminescence quantity called luciferin along with them. Agents are thought of as glowworms that emit a light whose intensity is proportional to the associated luciferin. The key feature that is responsible for the working of the algorithm is the use of an adaptive local-decision domain, which we use effectively to detect the multiple source locations of interest. The glowworms have a finite sensor range which defines a hard limit on the local-decision domain used to compute their movements. Extensive simulations validate the feasibility of applying the glowworm algorithm to the problem of multiple source localization. We build four wheeled robots called glowworms to conduct our experiments. We use a preliminary experiment to demonstrate the basic behavioral primitives that enable each glowworm to exhibit taxis behavior towards source locations and later demonstrate a sound localization task using a set of four glowworms.

Dynamical Resolution of Redundancy for Robot Manipulators

A large class of work in the robot manipulator literature deals with the kinematical resolution of redundancy based on the pseudo-inverse of the manipulator Jacobian. In this paper an alternative dynamical approach to redundancy resolution is developed which utilizes the mapping between the actuator torques and the acceleration of the end-effector, at a given dynamic state of the manipulator. The potential advantages of the approach are discussed and an example of a planar 3R manipulator following a circular end-effector trajectory is used to illustrate the proposed approach as well as to compare it with the more well-known approach based on the pseudo-inverse.

An Efficient Random Walk Strategy for Sampling Based Robot Motion Planners

Sampling based planners have been successful in path planning of robots with many degrees of freedom, but still remains ineffective when the configuration space has a narrow passage. We present a new technique based on a random walk strategy to generate samples in narrow regions quickly, thus improving efficiency of Probabilistic Roadmap Planners. The algorithm substantially reduces instances of collision checking and thereby decreases computational time. The method is powerful even for cases where the structure of the narrow passage is not known, thus giving significant improvement over other known methods.

Performance of a class of multi-robot deploy and search strategies based on centroidal voronoi configurations

This article considers a class of deploy and search strategies for multi-robot systems and evaluates their performance. The application framework used is deployment of a system of autonomous mobile robots equipped with required sensors in a search space to gather information. The lack of information about the search space is modelled as an uncertainty density distribution. The agents are deployed to maximise single-step search effectiveness. The centroidal Voronoi configuration, which achieves a locally optimal deployment, forms the basis for sequential deploy and search (SDS) and combined deploy and search (CDS) strategies. Completeness results are provided for both search strategies. The deployment strategy is analysed in the presence of constraints on robot speed and limit on sensor range for the convergence of trajectories with corresponding control laws responsible for the motion of robots. SDS and CDS strategies are compared with standard greedy and random search strategies on the basis of time taken to achieve reduction in the uncertainty density below a desired level. The simulation experiments reveal several important issues related to the dependence of the relative performances of the search strategies on parameters such as the number of robots, speed of robots and their sensor range limits.