Evolution of bed form height and length during a discharge wave

This research focusses on modeling the evolution of bed form during a discharge wave for application in operational flood forecasting. The objective of this research was to analyze and predict the bed form evolution during a discharge wave in a flume experiment. We analyzed the data of a flume experiment and show that dune length is determined by development of secondary bed forms during the receding limb of the discharge wave. Secondly, three models were compared to predict the bed form evolution: an equilibrium model, a time-lag model and the physically-based, numerical model of Paarlberg et al. (2010). We show some preliminary results and show that the numerical model seems promising for modeling bed form evolution for operational flood forecasting

The Value of Information in an (R,s,Q) Inventory Model

In this paper we compare three methods for the determination of the reorder point s in an (R; s; Q) inventory model subject to a service level constraint. The three methods di er in the modelling assumptions of the demand process which in turn leads to three di erent approximations for the distribution function of the demand during the lead time.The rst model is most common in the literature, and assumes that the time axis is divided in time units (e.g. days).It is assumed that the demands per time unit are independent and identically distributed random variables.The second model monitors the customers individually.In this model it is assumed that the demand process is a compound renewal process, and that the distribution function of the interarrival times as well as that of the demand per customer are approximated by the rst two moments of the associated random variable.The third method directly collects information about the demand during the lead time plus undershoot, avoiding convolutions of stochastic random variables and residual lifetime distributions.Consequently, the three methods require di erent types of information for the calculation of the reorder point in an operational setting.The purpose of this paper is to derive insights into the value of information; therefore it compares the target service level with the actual service level associated with the calculated reorder point.It will be shown that the performance of the rst model (discrete time model) depends on the coe cient ofvariation of the interarrival times. Furthermore, because we use asymptotic relations in the compound renewal model, we derive some bounds for the input parameters within which this model applies. Finally we show that the aggregated information model is superior to the other two models.

On the (R,s,Q) Inventory Model when Demand is Modelled as a Compound Process

In this paper we present an approximation method to compute the reorder point s in a (R; s; Q) inventory model with a service level restriction, where demand is modelled as a compound Bernoulli process, that is, with a xed probability there is positive demand during a time unit, otherwise demand is zero.The demand size and replenishment leadtime are stochastic variables.It is shown that this kind of mod- elling is especially suitable for intermittent demand.Furthermore, an approximation for the expected average physical stock is derived.The quality of both the reorder point determination as well as the approximation for the expected average physical stock turn out to be excellent, as is veri ed by discrete event simulation.

On the (R,s,Q) Inventory Model when Demand is Modelled as a Compound Process

In this paper we present an approximation method to compute the reorder point s in a (R; s; Q) inventory model with a service level restriction, where demand is modelled as a compound Bernoulli process, that is, with a xed probability there is positive demand during a time unit, otherwise demand is zero.The demand size and replenishment leadtime are stochastic variables.It is shown that this kind of mod- elling is especially suitable for intermittent demand.Furthermore, an approximation for the expected average physical stock is derived.The quality of both the reorder point determination as well as the approximation for the expected average physical stock turn out to be excellent, as is veri ed by discrete event simulation.inventory models;demand

The Value of Information in an (R,s,Q) Inventory Model

In this paper we compare three methods for the determination of the reorder point s in an (R; s; Q) inventory model subject to a service level constraint. The three methods di er in the modelling assumptions of the demand process which in turn leads to three di erent approximations for the distribution function of the demand during the lead time.The rst model is most common in the literature, and assumes that the time axis is divided in time units (e.g. days).It is assumed that the demands per time unit are independent and identically distributed random variables.The second model monitors the customers individually.In this model it is assumed that the demand process is a compound renewal process, and that the distribution function of the interarrival times as well as that of the demand per customer are approximated by the rst two moments of the associated random variable.The third method directly collects information about the demand during the lead time plus undershoot, avoiding convolutions of stochastic random variables and residual lifetime distributions.Consequently, the three methods require di erent types of information for the calculation of the reorder point in an operational setting.The purpose of this paper is to derive insights into the value of information; therefore it compares the target service level with the actual service level associated with the calculated reorder point.It will be shown that the performance of the rst model (discrete time model) depends on the coe cient ofvariation of the interarrival times. Furthermore, because we use asymptotic relations in the compound renewal model, we derive some bounds for the input parameters within which this model applies. Finally we show that the aggregated information model is superior to the other two models.inventory models;information

Reflections of patients and therapists on a multidisciplinary rehabilitation programme for persons with brachial plexus injuries

Contains fulltext : 203605.pdf (publisher's version ) (Open Access

Comparison of outcome and characteristics between 6343 COVID-19 patients and 2256 other community-acquired viral pneumonia patients admitted to Dutch ICUs

Purpose: Describe the differences in characteristics and outcomes between COVID-19 and other viral pneumonia patients admitted to Dutch ICUs. Materials and methods: Data from the National-Intensive-Care-Evaluation-registry of COVID-19 patients admitted between February 15th and January 1th 2021 and other viral pneumonia patients admitted between January 1st 2017 and January 1st 2020 were used. Patients' characteristics, the unadjusted, and adjusted in-hospital mortality were compared. Results: 6343 COVID-19 and 2256 other viral pneumonia patients from 79 ICUs were included. The COVID-19 patients included more male (71.3 vs 49.8%), had a higher Body-Mass-Index (28.1 vs 25.5), less comorbidities (42.2 vs 72.7%), and a prolonged hospital length of stay (19 vs 9 days). The COVID-19 patients had a significantly higher crude in-hospital mortality rate (Odds ratio (OR) = 1.80), after adjustment for patient characteristics and ICU occupancy rate the OR was respectively 3.62 and 3.58. Conclusion: Higher mortality among COVID-19 patients could not be explained by patient characteristics and higher ICU occupancy rates, indicating that COVID-19 is more severe compared to other viral pneumonia. Our findings confirm earlier warnings of a high need of ICU capacity and high mortality rates among relatively healthy COVID-19 patients as this may lead to a higher mental workload for the staff. (c) 2021 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/)