2,142 research outputs found
A Method for the Combination of Stochastic Time Varying Load Effects
The problem of evaluating the probability that a structure becomes unsafe under a
combination of loads, over a given time period, is addressed. The loads and load effects
are modeled as either pulse (static problem) processes with random occurrence time, intensity and a specified shape or intermittent continuous (dynamic problem) processes which
are zero mean Gaussian processes superimposed 'on a pulse process. The load coincidence
method is extended to problems with both nonlinear limit states and dynamic responses,
including the case of correlated dynamic responses. The technique of linearization of a
nonlinear limit state commonly used in a time-invariant problem is investigated for timevarying
combination problems, with emphasis on selecting the linearization point. Results
are compared with other methods, namely the method based on upcrossing rate, simpler
combination rules such as Square Root of Sum of Squares and Turkstra's rule. Correlated
effects among dynamic loads are examined to see how results differ from correlated static
loads and to demonstrate which types of load dependencies are most important, i.e., affect'
the exceedance probabilities the most.
Application of the load coincidence method to code development is briefly discussed.National Science Foundation Grants CME 79-18053 and CEE 82-0759
Performance Evaluation in Single or Multi-Cluster C-RAN Supporting Quasi-Random Traffic
In this paper, a cloud radio access network (C-RAN) is considered where the remote radio heads (RRHs) are separated from the baseband units (BBUs). The RRHs in the C-RAN are grouped in different clusters according to their capacity while the BBUs form a centralized pool of computational resource units. Each RRH services a finite number of mobile users, i.e., the call arrival process is the quasi-random process. A new call of a single service-class requires a radio and a computational resource unit in order to be accepted in the C-RAN for a generally distributed service time. If these resource units are unavailable, then the call is blocked and lost. To analyze the multi-cluster C-RAN, we model it as a single-rate loss system, show that a product form solution exists for the steady state probabilities and propose a convolution algorithm for the accurate determination of congestion probabilities. The accuracy of this algorithm is verified via simulation. The proposed model generalizes our recent model where the RRHs in the C-RAN are grouped in a single cluster and each RRH accommodates quasi-random traffic
Probabilistic partial volume modelling of biomedical tomographic image data
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Discrete Time Analysis of Consolidated Transport Processes
Diese Arbeit beschĂ€ftigt sich mit der Entwicklung zeitdiskreter Modelle zur Analyse von TransportbĂŒndelungen. Mit den entwickelten Modellen fĂŒr Bestands- und FahrzeugbĂŒndelungen, insbesondere Milkrun-Systeme, kann eine detaillierte Leistungsbewertung in kurzer Zeit durchgefĂŒhrt werden. DarĂŒber hinaus erlauben die Modelle die Analyse der UmschlagslagerbĂŒndelungen, beispielweise Hub-und-Spoke-Netzwerke, indem sie im Rahmen einer Netzwerkanalyse mit einander verknĂŒpft werden
Markov and Semi-markov Chains, Processes, Systems and Emerging Related Fields
This book covers a broad range of research results in the field of Markov and Semi-Markov chains, processes, systems and related emerging fields. The authors of the included research papers are well-known researchers in their field. The book presents the state-of-the-art and ideas for further research for theorists in the fields. Nonetheless, it also provides straightforwardly applicable results for diverse areas of practitioners
Response times in healthcare systems
It is a goal universally acknowledged that a healthcare system should treat its patients â
and especially those in need of critical care â in a timely manner. However, this is
often not achieved in practice, particularly in state-run public healthcare systems that
suffer from high patient demand and limited resources. In particular, Accident and
Emergency (A&E) departments in England have been placed under increasing pressure,
with attendances rising year on year, and a national government target whereby 98% of
patients should spend 4 hours or less in an A&E department from arrival to admission,
transfer or discharge.
This thesis presents techniques and tools to characterise and forecast patient arrivals,
to model patient flow and to assess the response-time impact of different resource
allocations, patient treatment schemes and workload scenarios.
Having obtained ethical approval to access five years of pseudonymised patient timing
data from a large case study A&E department, we present a number of time series
models that characterise and forecast daily A&E patient arrivals. Patient arrivals are
classified as one of two arrival streams (walk-in and ambulance) by mode of arrival.
Using power spectrum analysis, we find the two arrival streams exhibit different statistical
properties and hence require separate time series models. We find that structural
time series models best characterise and forecast walk-in arrivals, but that time series
analysis may not be appropriate for ambulance arrivals; this prompts us to investigate
characterisation by a non-homogeneous Poisson process.
Next we present a hierarchical multiclass queueing network model of patient flow in
our case study A&E department. We investigate via a discrete-event simulation the
impact of class and time-based priority treatment of patients, and compare the resulting
service-time densities and moments with actual data. Then, by performing bottleneck
analysis and investigating various workload and resource scenarios, we pinpoint the
resources that have the greatest impact on mean service times.
Finally we describe an approximate generating function analysis technique which efficiently
approximates the first two moments of customer response time in class-dependent
priority queueing networks with population constraints. This technique is applied to
the model of A&E and the results compared with those from simulation. We find good
agreement for mean service times especially when minors patients are given priority
Unreliable Retrial Queues in a Random Environment
This dissertation investigates stability conditions and approximate steady-state performance measures for unreliable, single-server retrial queues operating in a randomly evolving environment. In such systems, arriving customers that find the server busy or failed join a retrial queue from which they attempt to regain access to the server at random intervals. Such models are useful for the performance evaluation of communications and computer networks which are characterized by time-varying arrival, service and failure rates. To model this time-varying behavior, we study systems whose parameters are modulated by a finite Markov process. Two distinct cases are analyzed. The first considers systems with Markov-modulated arrival, service, retrial, failure and repair rates assuming all interevent and service times are exponentially distributed. The joint process of the orbit size, environment state, and server status is shown to be a tri-layered, level-dependent quasi-birth-and-death (LDQBD) process, and we provide a necessary and sufficient condition for the positive recurrence of LDQBDs using classical techniques. Moreover, we apply efficient numerical algorithms, designed to exploit the matrix-geometric structure of the model, to compute the approximate steady-state orbit size distribution and mean congestion and delay measures. The second case assumes that customers bring generally distributed service requirements while all other processes are identical to the first case. We show that the joint process of orbit size, environment state and server status is a level-dependent, M/G/1-type stochastic process. By employing regenerative theory, and exploiting the M/G/1-type structure, we derive a necessary and sufficient condition for stability of the system. Finally, for the exponential model, we illustrate how the main results may be used to simultaneously select mean time customers spend in orbit, subject to bound and stability constraints
Preclinical risk of bias assessment and PICO extraction using natural language processing
Drug development starts with preclinical studies which test the efficacy and
toxicology of potential candidates in living animals, before proceeding to
clinical trials examined on human subjects. Many drugs shown to be effective
in preclinical animal studies fail in clinical trials, indicating the potential
reproducibility issues and translation failure. To obtain less biased research
findings, systematic reviews are performed to collate all relevant evidence from
publications. However, systematic reviews are time-consuming and
researchers have advocated the use of automation techniques to speed the
process and reduce human efforts. Good progress has been made in
implementing automation tools into reviews for clinical trials while the tools
developed for preclinical systematic reviews are scarce. Tools for preclinical
systematic reviews should be designed specifically because preclinical
experiments differ from clinical trials. In this thesis, I explore natural language
processing models for facilitating two stages in preclinical systematic reviews:
risk of bias assessment and PICO extraction.
There are a range of measures used to reduce bias in animal experiments and
many checklist criteria require the reporting of those measures in publications.
In the first part of the thesis, I implement several binary classification models
to indicate the reporting of random allocation to groups, blinded assessment
of outcome, conflict of interests, compliance of animal welfare regulations, and
statement of animal exclusions in preclinical publications. I compare traditional
machine learning classifiers with several text representation methods,
convolutional/recurrent/hierarchical neural networks, and propose two
strategies to adapt BERT models to long documents. My findings indicate that
neural networks and BERT-based models achieve better performance than
traditional classifiers and rule-based approaches. The attention mechanism
and hierarchical architecture in neural networks do not improve performance
but are useful for extracting relevant words or sentences from publications to
inform usersâ judgement. The advantages of the transformer structure are
hindered when documents are long and computing resources are limited.
In literature retrieval and citation screening of published evidence, the key
elements of interest are Population, Intervention, Comparator and Outcome,
which compose the framework of PICO. In the second part of the thesis, I first
apply several question answering models based on attention flows and
transformers to extract phrases describing intervention or method of induction
of disease models from clinical abstracts and preclinical full texts. For
preclinical datasets describing multiple interventions or induction methods in
the full texts, I apply additional unsupervised information retrieval methods to
extract relevant sentences. The question answering models achieve good
performance when the text is at abstract-level and contains only one
intervention or induction method, while for truncated documents with multiple
PICO mentions, the performance is less satisfactory. Considering this
limitation, I then collect preclinical abstracts with finer-grained PICO
annotations and develop named entity recognition models for extraction of
preclinical PICO elements including Species, Strain, Induction, Intervention,
Comparator and Outcome. I decompose PICO extraction into two independent
tasks: 1) PICO sentences classification, and 2) PICO elements detection. For
PICO extraction, BERT-based models pre-trained from biomedical corpus
outperform recurrent networks and the conditional probabilistic module only
shows advantages in recurrent networks. Self-training strategy applied to
enlarge training set from unlabelled abstracts yields better performance for
PICO elements which lack enough amount of instances.
Experimental results demonstrate the possibilities of facilitating preclinical risk
of bias assessment and PICO extraction by natural language processing
Representative queueing network models of computer systems in terms of time delay probability distributions
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