Real-time train driver rescheduling by actor-agent techniques

Passenger railway operations are based on an extensive planning process for generating the timetable, the rolling stock circulation, and the crew duties for train drivers and conductors. In particular, crew scheduling is a complex process. After the planning process has been completed, the plans are carried out in the real-time operations. Preferably, the plans are carried out as scheduled. However, in case of delays of trains or large disruptions of the railway system, the timetable, the rolling stock circulation and the crew duties may not be feasible anymore and must be rescheduled. This paper presents a method based on multi-agent techniques to solve the train driver rescheduling problem in case of a large disruption. It assumes that the timetable and the rolling stock have been rescheduled already based on an incident scenario. In the crew rescheduling model, each train driver is represented by a driver-agent. A driver-agent whose duty has become infeasible by the disruption starts a recursive task exchange process with the other driver-agents in order to solve this infeasibility. The task exchange process is supported by a route-analyzer-agent, which determines whether a proposed task exchange is feasible, conditionally feasible, or not feasible. The task exchange process is guided by several cost parameters, and the aim is to find a feasible set of duties at minimal total cost. The train driver rescheduling method was tested on several realistic disruption instances of Netherlands Railways (NS), the main operator of passenger trains in the Netherlands. In general the rescheduling method finds an appropriate set of rescheduled duties in a short amount of time. This research was carried out in close cooperation by NS and the D-CIS Lab

Rates and risks for prolonged grief disorder in a sample of orphaned and widowed genocide survivors

<p>Abstract</p> <p>Background</p> <p>The concept of Prolonged Grief Disorder (PGD) has been defined in recent years by Prigerson and co-workers, who have developed and empirically tested consensus and diagnostic criteria for PGD. Using these most recent criteria defining PGD, the aim of this study was to determine rates of and risks for PGD in survivors of the 1994 Rwandan genocide who had lost a parent and/or the husband before, during or after the 1994 events.</p> <p>Methods</p> <p>The PG-13 was administered to 206 orphans or half orphans and to 194 widows. A regression analysis was carried out to examine risk factors of PGD.</p> <p>Results</p> <p>8.0% (<it>n </it>= 32) of the sample met criteria for PGD with an average of 12 years post-loss. All but one person had faced multiple losses and the majority indicated that their grief-related loss was due to violent death (70%). Grief was predicted mainly by time since the loss, by the violent nature of the loss, the severity of symptoms of posttraumatic stress disorder (PTSD) and the importance given to religious/spiritual beliefs. By contrast, gender, age at the time of bereavement, bereavement status (widow versus orphan), the number of different types of losses reported and participation in the funeral ceremony did not impact the severity of prolonged grief reactions.</p> <p>Conclusions</p> <p>A significant portion of the interviewed sample continues to experience grief over interpersonal losses and unresolved grief may endure over time if not addressed by clinical intervention. Severity of grief reactions may be associated with a set of distinct risk factors. Subjects who lose someone through violent death seem to be at special risk as they have to deal with the loss experience as such and the traumatic aspects of the loss. Symptoms of PTSD may hinder the completion of the mourning process. Religious beliefs may facilitate the mourning process and help to find meaning in the loss. These aspects need to be considered in the treatment of PGD.</p

Predictive model for the lightness of comminuted porcine lean meat during heating

A mathematical model for the prediction of lightness values (L*) of a comminuted porcine lean meat (PLM) system (about 2.5% fat) during heating was constructed using kinetic data collected from process temperatures of 50, 60, 80 and 100°C and heating times of up to 3 hr. The procedure of model formulation included regression analysis of L* versus time to obtain model parameters and a description of temperature dependence of these parameters using linear and Arrhenius models. By solving the final predictive model numerically, the value of L* was closely predicted for different temperature‐time combinations during heating of the PLM system. This can be used as basis for further development of predictive models during meat processing operations

Method for preparing a protein preparation having a reduced content of phenolic compounds

Described is a method for preparing from a protein source containing phenolic compounds a water-soluble protein preparation having a reduced content of phenolic compounds, comprising the steps of a) precipitating the protein by bringing the protein source into a mixture of an aqueous medium and at least a water-miscible organic solvent, such that substantially no denaturation of the protein occurs, while the pH of which mixture deviates at most 1 pH value from the isoelectirc point of the protein, b) purifying the precipitate obtained in a)