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.

Familial genes in sporadic disease: Common variants of a-Synuclein gene associate with Parkinson’s disease

Genetic variation of the alpha-synuclein gene (SNCA) is known to cause familial parkinsonism, however the role of SNCA variants in sporadic Parkinson's disease (PD) remains elusive. The present study identifies an association of common SNCA polymorphisms with disease susceptibility in a series of Irish PD patients. There is evidence for association with alternate regions, of protection and risk which may act independently/synergistically, within the promoter region (Rep1; OR: 0.59, 95% CI: 0.37-0.84) and the 3'UTR of the gene (rs356165; OR: 1.67, 95% CI: 1.08-2.58). Given previous reports of association a collaborative effort is required which may exploit global linkage disequilibrium patterns for SNCA and standardise polymorphic markers used in each population. It is now crucial to identify the susceptibility allele and elucidate its functionality which may generate a therapeutic target for PD.

Synchronous Islanded Operation of a Diesel Generator

The amount of distributed generation connected to the distribution network is increasing. To use this resource more effectively, splitting of the distribution network, or islanding the system, for prevention of power outages is being considered by some utilities. In this paper an islanding method that avoids out-ofsynchronism re-closure is proposed. The island is kept in synchronism with the rest of the utility while it is not electrically connected. This is referred to as synchronous islanded operation. A phase difference control algorithm, developed by the authors, was tested in a single set scenario on a 50-kVA diesel generator using two different governors. These are the “standard product” variable gain governor of the diesel generator and a governor developed by the authors, which utilizes supplementary inputs in addition to engine speed. The results show that phase difference can be controlled within acceptable limits, both in steady state and after load disturbances are applied. The advantages of employing supplementary governor inputs are fully evaluated.

Nonlinear PCA with Local Approach for Diesel Engine Fault Detection and Diagnosis

This brief examines the application of nonlinear statistical process control to the detection and diagnosis of faults in automotive engines. In this statistical framework, the computed score variables may have a complicated nonparametric distri- bution function, which hampers statistical inference, notably for fault detection and diagnosis. This brief shows that introducing the statistical local approach into nonlinear statistical process control produces statistics that follow a normal distribution, thereby enabling a simple statistical inference for fault detection. Further, for fault diagnosis, this brief introduces a compensation scheme that approximates the fault condition signature. Experimental results from a Volkswagen 1.9-L turbo-charged diesel engine are included.