Supervisor Continuity or Co-Location: Which Matters in Residency Education? Findings From a Qualitative Study of Remote Supervisor Family Physicians in Australia and Canada

Purpose: Changes to health care systems andworking hours have fragmentedresidents’ clinical experiences withpotentially negative effects ontheir development as professionals.Investigation of off-site supervision,which has been implemented in isolatedrural practice, could reveal importantbut less overt components of residencyeducation. 

Method: Insights from sociocultural learningtheory and work-based learning provideda theoretical framework. In 2011–2012,16 family physicians in Australia andCanada were asked in-depth how theyremotely supervised residents’ workand learning, and for their reflectionson this experience. The verbatiminterview transcripts and researchers’memos formed the data set. Templateanalysis produced a description andinterpretation of remote supervision. 

Results: Thirteen Australian family physiciansfrom five states and one territory, andthree Canadians from one province,participated. The main themes werehow remoteness changed the dynamicsof care and supervision; the importanceof ongoing, holistic, nonhierarchical,supportive supervisory relationships; andthat residents learned “clinical courage”through responsibility for patients’ careover time. Distance required supervisorsto articulate and pass on their expertiseto residents but made monitoringdifficult. Supervisory continuityencouraged residents to build on pastexperiences and confront deficiencies. 

Conclusions: Remote supervision enabled residents todevelop as clinicians and professionals.This questions the supremacy of co-locationas an organizing principle forresidency education. Future specialists maybenefit from programs that give themongoing and increasing responsibilityfor a group of patients and supportive.

Central-Tapped Node Linked Modular Fault-Tolerance Topology for SRM Applications

Electric vehicles (EVs) and hybrid electric vehicles (HEVs) can reduce greenhouse gas emissions while switched reluctance motor (SRM) is one of the promising motor for such applications. This paper presents a novel SRM fault-diagnosis and fault-tolerance operation solution. Based on the traditional asymmetric half-bridge topology for the SRM driving, the central tapped winding of the SRM in modular half-bridge configuration are introduced to provide fault-diagnosis and fault-tolerance functions, which are set idle in normal conditions. The fault diagnosis can be achieved by detecting the characteristic of the excitation and demagnetization currents. An SRM fault-tolerance operation strategy is also realized by the proposed topology, which compensates for the missing phase torque under the open-circuit fault, and reduces the unbalanced phase current under the short-circuit fault due to the uncontrolled faulty phase. Furthermore, the current sensor placement strategy is also discussed to give two placement methods for low cost or modular structure. Simulation results in MATLAB/Simulink and experiments on a 750-W SRM validate the effectiveness of the proposed strategy, which may have significant implications and improve the reliability of EVs/HEVs.

Inferring and Exploiting Categories for Next Location Prediction

Predicting the next location of a user based on their previous visiting pattern is one of the primary tasks over data from location based social networks (LBSNs) such as Foursquare. Many different aspects of these so-called “check-in” profiles of a user have been made use of in this task, including spatial and temporal information of check-ins as well as the social network information of the user. Building more sophisticated prediction models by enriching these check-in data by combining them with information from other sources is challenging due to the limited data that these LBSNs expose due to privacy concerns. In this paper, we propose a framework to use the location data from LBSNs, combine it with the data from maps for associating a set of venue categories with these locations. For example, if the user is found to be checking in at a mall that has cafes, cinemas and restaurants according to the map, all these information is associated. This category information is then leveraged to predict the next checkin location by the user. Our experiments with publicly available check-in dataset show that this approach improves on the state-of-the-art methods for location prediction.

Automating fault tolerance in high-performance computational biological jobs using multi-agent approaches

Background: Large-scale biological jobs on high-performance computing systems require manual intervention if one or more computing cores on which they execute fail. This places not only a cost on the maintenance of the job, but also a cost on the time taken for reinstating the job and the risk of losing data and execution accomplished by the job before it failed. Approaches which can proactively detect computing core failures and take action to relocate the computing core's job onto reliable cores can make a significant step towards automating fault tolerance. Method: This paper describes an experimental investigation into the use of multi-agent approaches for fault tolerance. Two approaches are studied, the first at the job level and the second at the core level. The approaches are investigated for single core failure scenarios that can occur in the execution of parallel reduction algorithms on computer clusters. A third approach is proposed that incorporates multi-agent technology both at the job and core level. Experiments are pursued in the context of genome searching, a popular computational biology application. Result: The key conclusion is that the approaches proposed are feasible for automating fault tolerance in high-performance computing systems with minimal human intervention. In a typical experiment in which the fault tolerance is studied, centralised and decentralised checkpointing approaches on an average add 90% to the actual time for executing the job. On the other hand, in the same experiment the multi-agent approaches add only 10% to the overall execution time

The Effect of Seat Location in Lecture Theatres on Academic Performance

The purpose of this research study was to investigate and identify possible patterns relating to academic performance on the effects of university students self-selecting where to sit in a lecture theatre.
The key research questions are:
1. Does seating position affect student performance?
2. Do the most academically able and engaged students regularly sit at the front of lecture theatres?
Academic achievement
Preliminary results suggest significant assessment score differences between those that sit at the front and those that sit further the back. Of those that received a grade of 75%+ (Grade A) 6.67% regularly sat at the back. With the same group 46.67% regularly sat at the front. Of the group that scored less than 50% (Grade D) 0% of students regularly sat at the front. 12.50% regularly sat in the middle zones with 37.50% sitting at the back. It was also observed that the remaining numbers did not consistently sit in the same zone.

Temporal movement
There is little evidence of movement between seating zones of the Grade A group throughout the 24 week period. However there was considerable movement with the Grade D group. Although still under analysis there appears be a pattern of students in this group graduating towards the back seating positions over the course of the programme.

Engagement
The frequency of completed entries on PinPoint was also used as an indicator of engagement. With the Grade A group 75% of them regularly completed an entry whereas in the Grade D group this drops to less than 50%.
Further analysis on the attitudinal factors in relational to seating position and performance are ongoing, but preliminary results suggest that those students that scored highly in attitude tended to sit at the front and middle sections.
It would indeed appear that the more highly engaged and academically capable students voluntarily sit at the front for most lectures. Interestingly as the course progresses those who had lesser engagement and below average midterm results tend to began to sit progressively toward the back. If this is a repeatable pattern then a linear regression analysis of the seating positions and midterm results could help predict students in danger of failing.