Dissertations / Theses on the topic 'Random early detection (RED)'

Alors que les arrêts maladie sont le signe d’un mal-être croissant chez les salariés et qu’ils pèsent un coût certain pour la collectivité, la numérisation et le partage systématique des données offrent de belles opportunités pour leur prévention. Nous avons ainsi profité de cette opportunité pour développer un éventail d’outils de prévention basés sur des méthodes d’analyse statistique. Dans un premier temps, ces travaux de thèse proposent une analyse des mécanismes expliquant les arrêts maladie chez le salarié. L’analyse d’une enquête nationale a premièrement permis d’identifier et de hiérarchiser leurs principaux facteurs déterminants grâce à l’algorithme des forêts aléatoires. Ensuite, une analyse de données administratives a identifié des trajectoires d’absence pouvant mener à des arrêts graves grâce à des analyses séquentielles et à de la modélisation multi-état. Dans un second temps, des outils ont été développés afin d’identifier des situations anormales d’arrêt maladie à l’échelle de l’entreprise. Une typologie d’entreprise a premièrement été construite afin de produire des valeurs repère pour que les entreprises évaluent précisément leur situation. Un algorithme de détection des pics d’absence, adapté de modèles de surveillance épidémiologique, a enfin été développé pour pouvoir identifier automatiquement les entreprises en excès<br>At a time when sick leave is a sign of growing ill-being for workers and a cost burden for the society, the systematic digitalization and distribution of data offers great opportunities for its prevention. We have therefore taken advantage of this opportunity to develop a range of prevention tools based on statistical analysis methods. In a first part, this work proposes an analysis of the mechanisms explaining sick leave among workers. The analysis of a national survey has first identified and prioritised their main determinants using random forest. Then, an analysis of administrative data had helped to identify absence trajectories that could lead to serious sick leaves thanks to sequential analyses and multi-state modelling. In a second step, tools were developed to identify abnormal situations of sick leave at company level. A company typology was first built to produce benchmark values for companies to accurately assess their situation. Finally, an algorithm for identifying absence peaks, adapted from epidemiological surveillance models, was finally developed to automatically identify companies in difficulty

Metabolomics is the methodical scientific study of biochemical processes associated with the metabolome—which comprises the entire collection of metabolites in any biological entity. Metabolome changes occur as a result of modifications in the genome and proteome, and are, therefore, directly related to cellular phenotype. Thus, metabolomic analysis is capable of providing a snapshot of cellular physiology. Untargeted metabolomics is an impartial, all-inclusive approach for detecting as many metabolites as possible without a priori knowledge of their identity. Hence, it is a valuable exploratory tool capable of providing extensive chemical information for discovery and hypothesis-generation regarding biochemical processes. A history of metabolomics and advances in the field corresponding to improved analytical technologies are described in Chapter 1 of this dissertation. Additionally, Chapter 1 introduces the analytical workflows involved in untargeted metabolomics research to provide a foundation for Chapters 2 – 5. Part I of this dissertation which encompasses Chapters 2 – 3 describes the utilization of mass spectrometry (MS)-based untargeted metabolomic analysis to acquire new insight into cancer detection. There is a knowledge deficit regarding the biochemical processes of the origin and proliferative molecular mechanisms of many types of cancer which has also led to a shortage of sensitive and specific biomarkers. Chapter 2 describes the development of an in vitro diagnostic multivariate index assay (IVDMIA) for prostate cancer (PCa) prediction based on ultra performance liquid chromatography-mass spectrometry (UPLC-MS) metabolic profiling of blood serum samples from 64 PCa patients and 50 healthy individuals. A panel of 40 metabolic spectral features was found to be differential with 92.1% sensitivity, 94.3% specificity, and 93.0% accuracy. The performance of the IVDMIA was higher than the prevalent prostate-specific antigen blood test, thus, highlighting that a combination of multiple discriminant features yields higher predictive power for PCa detection than the univariate analysis of a single marker. Chapter 3 describes two approaches that were taken to investigate metabolic patterns for early detection of ovarian cancer (OC). First, Dicer-Pten double knockout (DKO) mice that phenocopy many of the features of metastatic high-grade serous carcinoma (HGSC) observed in women were studied. Using UPLC-MS, serum samples from 14 early-stage tumor DKO mice and 11 controls were analyzed. Iterative multivariate classification selected 18 metabolites that, when considered as a panel, yielded 100% accuracy, sensitivity, and specificity for early-stage HGSC detection. In the second approach, serum metabolic phenotypes of an early-stage OC pilot patient cohort were characterized. Serum samples were collected from 24 early-stage OC patients and 40 healthy women, and subsequently analyzed using UPLC-MS. Multivariate statistical analysis employing support vector machine learning methods and recursive feature elimination selected a panel of metabolites that differentiated between age-matched samples with 100% cross-validated accuracy, sensitivity, and specificity. This small pilot study demonstrated that metabolic phenotypes may be useful for detecting early-stage OC and, thus, supports conducting larger, more comprehensive studies. Many challenges exist in the field of untargeted metabolomics. Part II of this dissertation which encompasses Chapters 4 – 5 focuses on two specific challenges. While metabolomic data may be used to generate hypothesis concerning biological processes, determining causal relationships within metabolic networks with only metabolomic data is impractical. Proteins play major roles in these networks; therefore, pairing metabolomic information with that acquired from proteomics gives a more comprehensive snapshot of perturbations to metabolic pathways. Chapter 4 describes the integration of MS- and NMR-based metabolomics with proteomics analyses to investigate the role of chemically mediated ecological interactions between Karenia brevis and two diatom competitors, Asterionellopsis glacialis and Thalassiosira pseudonana. This integrated systems biology approach showed that K. brevis allelopathy distinctively perturbed the metabolisms of these two competitors. A. glacialis had a more robust metabolic response to K. brevis allelopathy which may be a result of its repeated exposure to K. brevis blooms in the Gulf of Mexico. However, K. brevis allelopathy disrupted energy metabolism and obstructed cellular protection mechanisms including altering cell membrane components, inhibiting osmoregulation, and increasing oxidative stress in T. pseudonana. This work represents the first instance of metabolites and proteins measured simultaneously to understand the effects of allelopathy or in fact any form of competition. Chromatography is traditionally coupled to MS for untargeted metabolomics studies. While coupling chromatography to MS greatly enhances metabolome analysis due to the orthogonality of the techniques, the lengthy analysis times pose challenges for large metabolomics studies. Consequently, there is still a need for developing higher throughput MS approaches. A rapid metabolic fingerprinting method that utilizes a new transmission mode direct analysis in real time (TM-DART) ambient sampling technique is presented in Chapter 5. The optimization of TM-DART parameters directly affecting metabolite desorption and ionization, such as sample position and ionizing gas desorption temperature, was critical in achieving high sensitivity and detecting a broad mass range of metabolites. In terms of reproducibility, TM-DART compared favorably with traditional probe mode DART analysis, with coefficients of variation as low as 16%. TM-DART MS proved to be a powerful analytical technique for rapid metabolome analysis of human blood sera and was adapted for exhaled breath condensate (EBC) analysis. To determine the feasibility of utilizing TM-DART for metabolomics investigations, TM-DART was interfaced with traveling wave ion mobility spectrometry (TWIMS) time-of-flight (TOF) MS for the analysis of EBC samples from cystic fibrosis patients and healthy controls. TM-DART-TWIMS-TOF MS was able to successfully detect cystic fibrosis in this small sample cohort, thereby, demonstrating it can be employed for probing metabolome changes. Finally, in Chapter 6, a perspective on the presented work is provided along with goals on which future studies may focus.

碩士<br>國立中興大學<br>資訊科學系所<br>95<br>Many proposals have been adopted in controlling the congestions in the routers, including Random Early Detection (RED) and Drop-tail, and have shown to improve the loss rate, throughput, fairness, etc. of the network. Although RED algorithm is designed for TCP for a active queue management, we found that when comes to dropping the packets, it treats packets equally, ignoring the effect of the the size of the packets. This results in higher loss rate of packets and lower throughput for smaller packets. In this thesis, we propose to improve the original RED algorithm by differentiating packet sizes and devise RED_average algorithm and further improved PS_average algorithm. We then use ns-2 to simulate the performance of the aforementioned three algorithm.based on three MTU sizes. The results showed that if we take the factor of the packet size into consideration, the RED_average algorithm has a better loss rate and throughput. The PS_average, which takes the average packet size into consideration to adjust the intended loss rate for smaller packates, has a even further improved performance. We have shown that by the above two new algorithms, a better balance for the loss rate for all packets can be achieved, and thus improved utilization of the network resources.

Access to the large web content hosted all over the world by users of the Internet engage many hosts, routers/switches and faster links. They challenge the internet backbone to operate at its capacity to assure e±cient content access. This may result in congestion and raises concerns over various Quality of Service (QoS) issues like high delays, high packet loss and low throughput of the system for various Internet applications. Thus, there is a need to develop effective congestion control mechanisms in order to meet various Quality of Service (QoS) related performance parameters. In this paper, our emphasis is on the Active Queue Management (AQM) mechanisms, particularly Random Early Detection (RED). We propose a threshold based novel analytical model based on standard RED mechanism. Various numerical examples are presented for Internet traffic scenarios containing both the burstiness and correlation properties of the network traffic.

碩士<br>輔仁大學<br>資訊工程學系<br>99<br>The architecture of and the network formed by Peer-to-peer (P2P) technology is one of the most popular applications on the Internet. However, most of P2P networks currently in use operate in the form of unstructured style. The query broadcasting employed by the nodes to search for a file in unstructured networks often results in great burdens on the nodes. Furthermore, if nodes are located on popular paths, those nodes will receive more messages and have higher loads than others. How to reduce the load of the nodes in unstructured P2P networks is the major issue of this thesis. In this work, we propose a mechanism for forwarding and processing messages in unstructured P2P networks, which does not lead to overloads on the nodes. When a node searches for a file in unstructured P2P networks, it would use our method to choose the neighbors to send the Search Request messages to. When a node receives a Search Reply message, it would consider it own load situation to decide whether to forward the message or not. Simulation result shows that our method can save twenty percent on the number of messages of the network. When the average load of the network is heavy, we can choose a path those load is about three times less than not using the RED method. It means that our method can reduce the press of the nodes with heavy load, lower the standard deviation of the loads of all the nodes, and achieve the effect of balancing the load of the nodes over the network.

碩士<br>雲林科技大學<br>電機工程系碩士班<br>96<br>With the rapid development of Internet services and applications, the demand for network bandwidth is ever-increasing. Network congestion issue is inevitably critical and gets widespread attention all the time. Hence, how to make good use of limited network bandwidth and solve the congestion issue effectively has always been one of the most popular topics. In this paper, a progressive adjustment method is proposed to improve on Active Queue Management (AQM), especially on Random Early Detection (RED) and Modified Random Early Detection (MRED). This reformed AQM is therefore referred to as Progressive Random Early Detection (PRED). According to the reasonable and practical factor of instantaneous queue size, the proposed PRED algorithm adjusts the maximum threshold dynamically at first. Then, based on conventional RED and MRED algorithms, it regulates the packet dropping probability nonlinearly by comparing the instantaneous queue size with the progressive maximum queue threshold parameters. So its flexibility can realize better network bandwidth utilization and management efficiency under various network conditions. This paper uses Network Simulator 2 (NS2) to run a series of simulations about performance comparison between RED, MRED, and PRED. Simulation results demonstrate that the proposed PRED algorithm can achieve highest transmission throughput and lowest end-to-end average delay for almost all network topologies.

abstract: Breast cancer is the most common cancer and currently the second leading cause of death among women in the United States. Patients’ five-year relative survival rate decreases from 99% to 25% when breast cancer is diagnosed late. Immune checkpoint blockage has shown to be a promising therapy to improve patients’ outcome in many other cancers. However, due to the lack of early diagnosis, the treatment is normally given in the later stages. An early diagnosis system for breast cancer could potentially revolutionize current treatment strategies, improve patients’ outcomes and even eradicate the disease. The current breast cancer diagnostic methods cannot meet this demand. A simple, effective, noninvasive and inexpensive early diagnostic technology is needed. Immunosignature technology leverages the power of the immune system to find cancer early. Antibodies targeting tumor antigens in the blood are probed on a high-throughput random peptide array and generate a specific binding pattern called the immunosignature. In this dissertation, I propose a scenario for using immunosignature technology to detect breast cancer early and to implement an early treatment strategy by using the PD-L1 immune checkpoint inhibitor. I develop a methodology to describe the early diagnosis and treatment of breast cancer in a FVB/N neuN breast cancer mouse model. By comparing FVB/N neuN transgenic mice and age-matched wild type controls, I have found and validated specific immunosignatures at multiple time points before tumors are palpable. Immunosignatures change along with tumor development. Using a late-stage immunosignature to predict early samples, or vice versa, cannot achieve high prediction performance. By using the immunosignature of early breast cancer, I show that at the time of diagnosis, early treatment with the checkpoint blockade, anti-PD-L1, inhibits tumor growth in FVB/N neuN transgenic mouse model. The mRNA analysis of the PD-L1 level in mice mammary glands suggests that it is more effective to have treatment early. Novel discoveries are changing understanding of breast cancer and improving strategies in clinical treatment. Researchers and healthcare professionals are actively working in the early diagnosis and early treatment fields. This dissertation provides a step along the road for better diagnosis and treatment of breast cancer.<br>Dissertation/Thesis<br>Doctoral Dissertation Biological Design 2015

No<br>Traffic congestion in communication networks can dramatically deteriorate user-perceived Quality-of-Service (QoS). The integration of the Random Early Detection (RED) and priority scheduling mechanisms is a promising scheme for congestion control and provisioning of differentiated QoS required by multimedia applications. Although analytical modelling of RED congestion control has received significant research efforts, the performance models reported in the current literature were primarily restricted to the RED algorithm only without consideration of traffic scheduling scheme for QoS differentiation. Moreover, for analytical tractability, these models were developed under the simplified assumption that the traffic follows Short-Range-Dependent (SRD) arrival processes (e.g., Poisson or Markov processes), which are unable to capture the self-similar nature (i.e., scale-invariant burstiness) of multimedia traffic in modern communication networks. To fill these gaps, this paper presents a new analytical model of RED congestion control for prioritized multi-class self-similar traffic. The closed-form expressions for the loss probability of individual traffic classes are derived. The effectiveness and accuracy of the model are validated through extensive comparison between analytical and simulation results. To illustrate its application, the model is adopted as a cost-effective tool to investigate the optimal threshold configuration and minimize the required buffer space with congestion control.