The ambulance offload delay problem is a well-known result of overcrowding and congestion in emergency departments. Offload delay refers to the situation where area hospitals are unable to accept patients from regional ambulances in a timely manner due to lack of staff and bed capacity. The problem of offload delays is not a simple issue to resolve and has caused severe problems to the emergency medical services (EMS) providers, emergency department (ED) staff, and most importantly patients that are transferred to hospitals by ambulance. Except for several reports on the problem, not much research has been done on the subject. Almost all research to date has focused on either EMS or ED planning and operation and as far as we are aware there are no models which have considered the coordination of these units. We propose an analytical model which will allow us to analyze and explore the ambulance offload delay problem. We use queuing theory to construct a system representing the interaction of EMS and ED, and model the behavior of the system as a continuous time Markov chain. The matrix geometric method will be used to numerically compute various system performance measures under different conditions. We analyze the effect of adding more emergency beds in the ED, adding more ambulances, and reducing the ED patient length of stay, on various system performance measures such as the average number of ambulances in offload delay, average time in offload delay, and ambulance and bed utilization. We will show that adding more beds to the ED or reducing ED patient length of stay will have a positive impact on system performance and in particular will decrease the average number of ambulances experiencing offload delay and the average time in offload delay. Also, it will be shown that increasing the number of ambulances will have a negative impact on offload delays and increases the average number of ambulances in offload delay. However, other system performance measures are improved by adding more ambulances to the system. Finally, we will show the tradeoffs between adding more emergency beds, adding more ambulances, and reducing ED patient length of stay. We conclude that the hospital is the bottleneck in the system and in order to reduce ambulance offload delays, either hospital capacity has to be increased or ED patient length of stay is to be reduced.

Modeling of chemical engineering systems often necessitates using non-linear models. These models can range in complexity, from a simple analytical equation to a system of differential equations. Regardless of what type of model is being utilized, determining parameter estimates is essential in everyday chemical engineering practice. One promising approach to non-linear regression is a technique called Markov Chain Monte Carlo (MCMC).This method produces reliable parameter estimates and generates joint confidence regions (JCRs) with correct shape and correct probability content. Despite these advantages, its application in chemical engineering literature has been limited. Therefore, in this project, MCMC methods were applied to a variety of chemical engineering models. The objectives of this research is to (1) illustrate how to implement MCMC methods in complex non-linear models (2) show the advantages of using MCMC techniques over classical regression approaches and (3) provide practical guidelines on how to reduce the computational time. MCMC methods were first applied to the biological oxygen demand (BOD) problem. In this case study, an implementation procedure was outlined using specific examples from the BOD problem. The results from the study illustrated the importance of estimating the pure error variance as a parameter rather than fixing its value based on the mean square error. In addition, a comparison was carried out between the MCMC results and the results obtained from using classical regression approaches. The findings show that although similar point estimates are obtained, JCRs generated from approximation methods cannot model the parameter uncertainty adequately. Markov Chain Monte Carlo techniques were then applied in estimating reactivity ratios in the Mayo-Lewis model, Meyer-Lowry model, the direct numerical integration model and the triad fraction multiresponse model. The implementation steps for each of these models were discussed in detail and the results from this research were once again compared to previously used approximation methods. Once again, the conclusion drawn from this work showed that MCMC methods must be employed in order to obtain JCRs with the correct shape and correct probability content. MCMC methods were also applied in estimating kinetic parameter used in the solid oxide fuel cell study. More specifically, the kinetics of the water-gas shift reaction, which is used in generating hydrogen for the fuel cell, was studied. The results from this case study showed how the MCMC output can be analyzed in order to diagnose parameter observability and correlation. A significant portion of the model needed to be reduced due to these issues of observability and correlation. Point estimates and JCRs were then generated using the reduced model and diagnostic checks were carried out in order to ensure the model was able to capture the data adequately. A few select parameters in the Waterloo Polymer Simulator were estimated using the MCMC algorithm. Previous studies have shown that accurate parameter estimates and JCRs could not be obtained using classical regression approaches. However, when MCMC techniques were applied to the same problem, reliable parameter estimates and correct shape and correct probability content confidence regions were observed. This case study offers a strong argument as to why classical regression approaches should be replaced by MCMC techniques. Finally, a very brief overview of the computational times for each non-linear model used in this research was provided. In addition, a serial farming approach was proposed and a significant decrease in computational time was observed when this procedure was implemented.

It is known that a highly ordered medium allows certain wave functions to move unhindered throughout and in this manner achieve delocalization. It is also known that if one introduces disorder into a medium, wave packets will not be able to move as freely and will instead be trapped or localized. In this thesis, I have simulated a medium in which the amount of disorder can be modified and using this I have shown that the shape of the localization can be altered.

In this article we use spatial birth-death processes to estimate the number of states k of a switching model. Following Preston (1976) and Stephens (1998) matching the detailed balance condition for the underlying birth-death process results in an unique invariant probability measure with the corresponding stationary distribution of the number of states. This concept could be easily integrated to Bayesian sampling to derive the marginal posterior distribution of the number of states within the sampling procedure. We apply this technique to simulated AR(1)data and to quarterly Austrian data on unemployment and real gross domestic product. (author's abstract) / Series: Working Papers SFB "Adaptive Information Systems and Modelling in Economics and Management Science"

The paper presents an Markov Chain Monte Carlo algorithm for both variable and covariance selection in the context of logistic mixed effects models. This algorithm allows us to sample solely from standard densities, with no additional tuning being needed. We apply a stochastic search variable approach to select explanatory variables as well as to determine the structure of the random effects covariance matrix. For logistic mixed effects models prior determination of explanatory variables and random effects is no longer prerequisite since the definite structure is chosen in a data-driven manner in the course of the modeling procedure. As an illustration two real-data examples from finance and tourism studies are given. (author's abstract) / Series: Research Report Series / Department of Statistics and Mathematics

The ambulance offload delay problem is a well-known result of overcrowding and congestion in emergency departments. Offload delay refers to the situation where area hospitals are unable to accept patients from regional ambulances in a timely manner due to lack of staff and bed capacity. The problem of offload delays is not a simple issue to resolve and has caused severe problems to the emergency medical services (EMS) providers, emergency department (ED) staff, and most importantly patients that are transferred to hospitals by ambulance. Except for several reports on the problem, not much research has been done on the subject. Almost all research to date has focused on either EMS or ED planning and operation and as far as we are aware there are no models which have considered the coordination of these units. We propose an analytical model which will allow us to analyze and explore the ambulance offload delay problem. We use queuing theory to construct a system representing the interaction of EMS and ED, and model the behavior of the system as a continuous time Markov chain. The matrix geometric method will be used to numerically compute various system performance measures under different conditions. We analyze the effect of adding more emergency beds in the ED, adding more ambulances, and reducing the ED patient length of stay, on various system performance measures such as the average number of ambulances in offload delay, average time in offload delay, and ambulance and bed utilization. We will show that adding more beds to the ED or reducing ED patient length of stay will have a positive impact on system performance and in particular will decrease the average number of ambulances experiencing offload delay and the average time in offload delay. Also, it will be shown that increasing the number of ambulances will have a negative impact on offload delays and increases the average number of ambulances in offload delay. However, other system performance measures are improved by adding more ambulances to the system. Finally, we will show the tradeoffs between adding more emergency beds, adding more ambulances, and reducing ED patient length of stay. We conclude that the hospital is the bottleneck in the system and in order to reduce ambulance offload delays, either hospital capacity has to be increased or ED patient length of stay is to be reduced.

On July 1996, Taiwan government opened up mobile communication market to private telecommunication companies. For the next few years, mobile communication market reached its most glorious period. Almost every carrier had an outstanding profit, until recent years, NCC began to control pricing regulation. To obtain exceptional profits, telecommunication companies have started to cut expenditures in the direction toward the effort, but competitors constantly employ new strategy in the market. If a company cuts its cost too much, it is likely to result in the loss of a large number of consumers. On the other hand, if a company invests blindly, it will cause a great burden on the company¡¦s resources. Thus, this paper concentrates on formulating a scientific analysis that assists the executive officers of telecommunication companies to determine best marketing strategy of existing market. The research method of this paper will first survey the current mobile communication users to examine the significant factors from their preferences and then utilize Markov chain to analyze the mobile communication market trend. Based on the quantification of population growth and decline in the market, we will be able to better understand the trend of consumers¡¦ preferences. Combined with the overall assessment of telecommunication industry, we will be able to recommend an effective marketing strategy in the telecommunication market.

In IEEE 802.16j MMR (Mobile Multi-hop Relay) networks, bandwidth is divided into two zones, access zone to mobile stations and relay zone to relay stations. To satisfy the requirements of Quality of Services (QoS) for different types of traffic between access zone and relay zone, we propose Bandwidth-Negotiation Scheduling (BNS) for BS and RS to adequately allocate bandwidth. For the purpose of satisfying higher-priority rtPS traffic, BNS can negotiate bandwidth between two zones if the allocated bandwidth is insufficient to meet its QoS requirement. Besides, BNS can satisfy bandwidth requirement for nrtPS as much as possible and it will also do negotiation to allocate at least minimum bandwidth if resource is not sufficient. At last, BNS may reduce the allocated bandwidth for nrtPS if PLR (Packet Loss Ratio) of BE is too high. Therefore, the starvation probability of BE can be decreased by earning this extra bandwidth from nrtPS. In short, the proposed BNS can adjust the boundary between access zone and relay zone dynamically and it can improve bandwidth utilization effectively. Through Markov-chain model, we evaluated the performance of BNS and compared its performance to a mechanism with fixed-boundary. Analytical results have shown that BNS can decrease the probability of exceeding delay constraint for rtPS, increase the throughput, and decrease the PLR for nrtPS when rtPS delay constraint is increased. Moreover, BNS can significantly reduce the possibility of starvation for BE traffic.

With the rapid growing of economic activities and populations, available land resources have become unable to meet the demand. Originally considered as a mere frontier between land and sea, the coastal zone has become an essential zone for human development. As any kind of human exploitation of the coastal zone is likely to induce some changes in the local environment. Gradual and permanent alterations in coastal ecosystems have led to losses in abundance in numerous species, such as the black-faced spoonbill. To mitigate the impact, the government declares the North bank of the Tsengwen River Estuary as a protected area. However, land-use changes outside the protected area, such as the development of fish ponds or changes in the spatial patterns of farmlands and bodies of water, are still affecting birds¡¦ survival. This study is based on landscape ecology, using several analytical tools including Geographic Information System (GIS), Spatial Analysis, Landscape metric and Markov Chains. Changes in landscape structure at Chi-Gu between 2003 and 2008, and long-term birds census have been combined in order to discuss the relations between land-based activities and bird¡¦s abundance. Furthermore, a Markov chain was built to predict the most likely land-use pattern in 2013 and its eventual impacts on birds¡¦ conservation. The results of this study show that the reduction of spatial segregation in Chi-Gu has, on average, mitigated the adverse effects on birds. However, further analysis showed that migrant birds are increasing whereas resident birds are decreasing. We therefore suggest that the land-uses, closely related to resident birds¡¦ conservation, such as forests, should be managed with special care in order to assure an effective protection of both migrant and resident birds.

More difficulties are now expected in exploring economically valuable reservoirs because most reservoirs have been already developed since beginning seismic exploration of the subsurface. In order to efficiently analyze heterogeneous fine-scale properties in subsurface layers, one ongoing challenge is accurately upscaling fine-scale (high frequency) logging measurements to coarse-scale data, such as surface seismic images. In addition, numerically efficient modeling cannot use models defined on the scale of log data. At this point, we need an upscaling method replaces the small scale data with simple large scale models. However, numerous unavoidable uncertainties still exist in the upscaling process, and these problems have been an important emphasis in geophysics for years. Regarding upscaling problems, there are predictable or unpredictable uncertainties in upscaling processes; such as, an averaging method, an upscaling algorithm, analysis of results, and so forth. To minimize the uncertainties, a Bayesian framework could be a useful tool for providing the posterior information to give a better estimate for a chosen model with a conditional probability. In addition, the likelihood of a Bayesian framework plays an important role in quantifying misfits between the measured data and the calculated parameters. Therefore, Bayesian methodology can provide a good solution for quantification of uncertainties in upscaling. When analyzing many uncertainties in porosities, wave velocities, densities, and thicknesses of rocks through upscaling well log data, the Markov Chain Monte Carlo (MCMC) method is a potentially beneficial tool that uses randomly generated parameters with a Bayesian framework producing the posterior information. In addition, the method provides reliable model parameters to estimate economic values of hydrocarbon reservoirs, even though log data include numerous unknown factors due to geological heterogeneity. In this thesis, fine layered well log data from the North Sea were selected with a depth range of 1600m to 1740m for upscaling using an MCMC implementation. The results allow us to automatically identify important depths where interfaces should be located, along with quantitative estimates of uncertainty in data. Specifically, interfaces in the example are required near depths of 1,650m, 1,695m, 1,710m, and 1,725m. Therefore, the number and location of blocked layers can be effectively quantified in spite of uncertainties in upscaling log data.