Scheduling and Platforming Trains at Busy Complex Stations

We consider the problem of train planning or scheduling for large, busy, complex train stations, which are common in Europe and elsewhere, though not in North America. We develop the constraints and objectives for this problem, but these are too computationally complex to solve by standard combinatorial search or integer programming methods. Also, the problem is somewhat political in nature, that is, it does not have a clear objective function because it involves multiple train operators with conflicting interests. We therefore develop scheduling heuristics analogous to those successfully adopted by train planners using ''manual'' methods. We tested the model and algorithms by applying to a typical large station that exhibits most of the complexities found in practice. The results compare well with those found by traditional methods, and take account of cost and preference trade-offs not handled by those methods. With successive refinements, the algorithm eventually took only a few seconds to run, the time depending on the version of the algorithm and the scheduling problem. The scheduling models and algorithms developed and tested here can be used on their own, or as key components for a more general system for train scheduling for a rail line or network.Train scheduling for a busy station includes ensuring that there are no conflicts between several hundred trains per day going in and out of the station on intersecting paths from multiple in-lines and out-lines to multiple platforms, while ensuring that each train is allowed at least its minimum required headways, dwell time, turnaround time and trip time. This has to be done while minimizing (costs of) deviations from desired times, platforms or lines, allowing for conflicts due to through-platforms, dead-end platforms, multiple sub-platforms, and possible constraints due to infrastructure, safety or business policy.

Keeping it real! Enhancing realism in standardised patient OSCE stations

Background: Objective structured clinical examinations (OSCEs) are a
commonly used method of assessing clinical competency in healthcare education. They can providean opportunity to observe candidates interacting with patients.
There are many challenges in using real patients in OSCEs, and increasingly standardised patients are being used as a preference. However, by using standardised patients there is a risk of making the encounter arti?cial and removed from actual clinical practice.
Context: Efforts made in terms of cognitive, auditory, visual, tactile, psychological and emotional cues can minimise the differences between a simulated
and real clinical scenario. However, a number of factors, including feasibility, cost and usability, need to be considered if such techniques are to be practicable
within an OSCE framework.
Innovation: This article describes a series of techniques that have been used in our institution to enhance the realism of a standardised patient encounter in an
OSCE. Efforts in preparing standardised patient roles, and how they portray these roles, will be considered. A wide variety of equipment can also be used in
combination with a patient and the surrounding environment, which can further enhance the authenticity of the simulated scenario.
Implications: By enhancing the realism in simulated patient OSCE encounters, there is potential to trigger more authentic conscious responses from candidates and implicit reactions that the candidates themselves may be less
aware of. Furthermore, using such techniques may allow faculty members to select scenarios that were previously not thought possible in an OSCE

Massive MIMO Systems with Hardware-Constrained Base Stations

Massive multiple-input multiple-output (MIMO) systems are cellular networks where the base stations (BSs) are equipped with unconventionally many antennas. Such large antenna arrays offer huge spatial degrees-of-freedom for transmission optimization; in particular, great signal gains, resilience to imperfect channel knowledge, and small inter-user interference are all achievable without extensive inter-cell coordination. The key to cost-efficient deployment of large arrays is the use of hardware-constrained base stations with low-cost antenna elements, as compared to today's expensive and power-hungry BSs. Low-cost transceivers are prone to hardware imperfections, but it has been conjectured that the excessive degrees-of-freedom of massive MIMO would bring robustness to such imperfections. We herein prove this claim for an uplink channel with multiplicative phase-drift, additive distortion noise, and noise amplification. Specifically, we derive a closed-form scaling law that shows how fast the imperfections increase with the number of antennas.