Academic literature on the topic 'Analytical variability'

Basat en un mecanisme de transport de corrent de banda a banda, el túnel-FET és capaç de superar la limitació de pendent sub-llindar física del MOSFET de 60 mV /dec. Per tant, s'ha convertit en un dels dispositius més prometedors per ser el successor del MOSFET clàssic en els últims anys. Aquesta tesi descriu tots els passos necessaris per modelar analíticament un Túnel-FET de doble porta. El model inclou una solució electrostàtica de dues dimensions en totes les regions del dispositiu, el que permet fins i tot simulacions hetero-unió del dispositiu. Per a un comportament més realista del dispositiu, cal tenir en compte el rendiment del dispositiu que limita els perfils de dopatge de forma Gaussiana en les unions del canal. Les expressions per a les probabilitats de túnel de banda a banda i les de Trap-Assisted-Tunneling (TAT) són executades per un enfocament WKB quasi bidimensional. El corrent del dispositiu es calcula mitjançant la teoria de transmissió de Landauer. El model és vàlid per a dispositius de canal curt i les estàncies estan ben comparades amb les dades de simulació TCAD Sentaurus i amb les medicions proporcionades. S'introdueix un modelo general per les flactuacions del dopant aleatoria, que prediu les influencies característiques del dispositiu en el corrent de sortida i el voltatge llindar. El model s'aplica al MOSFET, així com a dispositius TFET.<br>Basado en un mecanismo de transporte de corriente banda a banda, el Tunnel-FET es capaz de superar la limitación de pendiente sub-umbral física del MOSFET de 60 mV/dec. Por lo tanto, esto lo convierte en uno de los dispositivos más prometedores para ser el sucesor del MOSFET clásico en los últimos años. Esta tesis describe todos los pasos necesarios para modelar analíticamente un Tunnel-FET de doble puerta. El modelo incluye una solución electrostática bidimensional en todas las regiones del dispositivo, lo que permite incluso simulaciones de hetero-unión del dispositivo. Para un comportamiento más realista del dispositivo se tiene en cuenta el rendimiento del dispositivo que limita los perfiles de dopaje de forma Gaussiana en las uniones del canal. Las expresiones para las probabilidades de túnel de banda a banda y de Trap-Assisted-Tunneling (TAT) se implementan mediante un enfoque de WKB cuasi bidimensional. La corriente del dispositivo se calcula mediante la teoría de transmisión de Landauer. El modelo es válido para dispositivos de canal corto y las estancias están bien comparadas con los datos de simulación TCAD Sentaurus y con las mediciones proporcionadas. Se introduce un modelo general para las fluctuaciones del dopado aleatorio, que predice las influencias características del dispositivo en la corriente de salida y el voltaje umbral. El modelo se aplica al MOSFET, así como a los dispositivos TFET.<br>Based on a band-to-band current transport mechanism, the Tunnel-FET is able to overcome the physical subthreshold slope limitation of the MOSFET of 60 mV/dec. Therefore, it has become one of the most promising devices to be the successor of the classical MOSFET in the last few years. This thesis describes all necessary steps to analytically model a double-gate Tunnel-FET. The model includes a two-dimensional electrostatic solution in all device regions, which enables even hetero-junction device simulations. Device performance limiting Gaussian-shaped doping profiles at the channel junctions are taken into account for a realistic device behavior. Expressions for the band-to-band and trap-assisted-tunneling probabilities are implemented by a quasi two-dimensional WKB approach. The device current is calculated based on Landauer's transmission theory. The model is valid for short-channel devices and stays is good agreement with the TCAD Sentaurus simulation data and with the provided measurements. A general model for random-dopant-fluctuations is introduced, which predicts characteristic device influences on the output current and threshold voltage. The model is applied to MOSFET, as well as TFET devices.

Dans cette thèse, nous nous intéressons aux variations induites par différentes méthodes d'analyse, ou variabilité analytique, dans les études d'imagerie cérébrale. C'est un phénomène qui est désormais connu dans la communauté, et notre objectif est maintenant de mieux comprendre les facteurs menant à cette variabilité et de trouver des solutions pour mieux la prendre en compte. Pour cela, j’analyse des données et j’explore les relations entre les résultats de différentes méthodes. Parallèlement, j’étudie les contraintes liées à la réutilisation de données et je propose des solutions basées sur l'intelligence artificielle afin de rendre les études plus robustes<br>In this thesis, we focus on the variations induced by different analysis methods, also known as analytical variability, in brain imaging studies. This phenomenon is now well known in the community, and our aim is now to better understand the factors leading to this variability and to find solutions to better account for it. To do so, I analyse data and explore the relationships between the results of different methods. At the same time, I study the constraints related to data reuse and I propose solutions based on artificial intelligence to build more robust studies

This thesis demonstrates that the half-t distribution is the prior of choice for estimating uncertainty of variance estimators in routine analysis of analytical and process variance components studies. Industrial studies are often performed to estimate sources of variation e.g. to improve and quantify measurement or process capability. Understanding the uncertainty of those estimators is important, especially for small studies. A Bayesian analysis is proposed – providing a flexible methodology which easily copes with the complex and varied nature of the studies and the varied quantities of interest. The prior is a fundamental component of a Bayesian analysis. The choice of prior is appraised and the coverage of the credible intervals obtained using six families of priors is assessed. A half-t prior (with several degrees of freedom) on the standard deviation is recommended in preference to a uniform or half-Cauchy prior, when some information exists on the magnitude of variability ‘core’ to the process or analytical method. Whilst a half-t prior has been previously proposed, through extensive simulation it is demonstrated that it is the prior of choice for estimating uncertainty of variance estimators in routine analysis of analytical and process variation studies. The coverage of 95% credible intervals for variance components and total variance is 93% (approximately) or above across a range of realistic scenarios. Other priors investigated, including Jeffreys’, a FLAT prior and inverse gamma distributions on stratum variances available in PROC MIXED1 in the SAS/STAT® software, are less satisfactory. This evaluation is novel: for one-way variance component designs there is very limited evaluation of the half-t prior when estimating the uncertainty of the variance component estimators; for the two-way or more complex none has been found. Since the coverage issues were primarily for the mid-level variance component, evaluation of designs more complex than one-way is important. Highest posterior density intervals are recommended with the metric of the parameter being important. Additionally, a scale based on the intra-class correlation coefficient is proposed for plotting the credible intervals.

Interpretation of laboratory tests in clinical practice is based on an understanding of the disease process within or between individuals. This is demonstrated by the variability of pathology results as compared to the previous result or against the reference range, made up from the intrinsic pathophysiological changes and also variation associated with the in vitro changes to the sample. My work is on identification and minimisation of the result variation in the pre-analytical phase, accounting for 60-70% of the errors associated with laboratory testing. The first project of my thesis is based on four studies that consider the in vitro stability of parathyroid hormone (PTH) and C-reactive protein (CRP), in which significant sample degradation is observed due to sample tube type, anticoagulant used and time to separation. The second project considers ethnic variation as a source of intra individual variation. Specifically considering intra individual ethnic variation in total cholesterol (TC) and high density lipoprotein cholesterol (HDLC), reporting significant differences were observed between Caucasian Indo-Asians in HDLC, in addition I investigated the relationship between low maternal vitamin B12 concentrations in Caucasian women and cord blood cholesterol. The third project considered the variation in laboratory results due to pre-existing conditions causing interference in common laboratory tests. I published on the effect of lipaemia on common laboratory tests, showing lipaemia does have a significant effect on laboratory tests. The following study found that the raised prolactin seen in rheumatoid arthritis is not artefactual but due to changes in cross reactivity due of prolactin subtypes. The final paper of this project shows, through a collection of case studies falsely elevated serum calcium levels in patients with paraproteinaemia.

A “downburst” is defined as a diverging wind system that occurs when a strong downdraft induces an outflow of damaging winds on or near the ground. Severe wind damage in many parts of the world are often due to thunderstorm outflows and their knowledge is therefore relevant for structural safety and design wind speed evaluation. Nevertheless, there is not yet a shared model for thunderstorm outflows and their actions on structures. In this paper, an analytical model that simulates the horizontal mean wind velocity originated from a travelling downburst is proposed. The horizontal wind velocity is expressed as the vector summation of three independent components: the stationary radial velocity generated by an impinging jet over a flat surface, the downdraft translating velocity, which corresponds to the parent cloud motion, and the boundary layer background wind field at the surface where the downburst is immersed. A parametric analysis is also developed and coupled with the analytical model aiming to investigate two observed downburst events and extract their main parameters – e.g. downdraft diameter, touch-down position, translating downdraft speed and direction, intensity and decay period - in order to reconstruct the space-time evolution of these events. Due to large computational cost for the reconstruction of a single downburst wind field a novel strategy is implemented to speed up the process. Two metaheuristic optimization algorithms are used, namely the Teaching Learning Based Optimization and the Differential Evolution. The metric to evaluate the algorithm’s efficiency is based on the convergence behaviour of the objective function towards the best solution as the number of iterations increases. The decision variable parameters (e.g. downdraft diameter, touch-down position, translating downdraft speed and direction, iintensity,and decay period and so on) that minimize the objective function are very important in Wind Engineering since their knowledge allows statistical analysis of the intense wind fields that are generated during downburst winds, and therefore allows to better define the actions that these extreme events have on structures. Lastly the proposed model was validated against s strong downburst event that took place in Sânnicolau Mare (Romania) during the summer of 2021. This event was accompanied by hail of 2-3 cm in size and the hail near the surface was driven by the downburst wind. This means that the horizontal velocity of the ice projectile near the surface was less or equal to the horizontal downburst wind velocity. After this strong event, a damage survey was carried out in collaboration between the University of Genoa (Italy) and the University of Bucharest (Romania). The damage survey identified locations of buildings in Sânnicolau Mare that suffered hail damage during the event. The analytical model was used to reproduce the recorded wind speed and direction due to the severe downburst. Using the simulated wind field, the simulated damage “footprint” (i.e., the maximum wind speed that occurred at a given place at any time during the passage of the downburst) was calculated. The simulated footprint was able to matches with a very good extent the areas that suffered from hail damage, and consequently permit to validate the proposed analytical model.

The relationships among the balance of payments and other macroeconomic variables in the Korean economy for the period 1961-85 are analyzed in this study. Theoretical studies on the effects of government policies on the economy and the balance of payments were conducted under both the Keynesian and monetary approaches. The Keynesian approach concentrates on the commodity and capital market adjustment factors and does not focus on the money market factors, whereas the monetary approach considers the balance of payments adjustments as a symptom of money market disequilibrium alone. The basic assumptions of those two approaches, taken seperately, are not fully relevant to the Korean economy, which has unemployed resources, a high proportion of non-traded goods to traded goods, and monetary effects of balance of payments changes. Therefore, a model combining monetary and real factors to explain the short-run behavior of Korea's balance of payments in a single framework is developed. The empirical results of the combined model show that its explanatory power is much higher than either of the two models taken separately. For balance of payments adjustment policy in Korea during the period 1961-85, fiscal and foreign exchange rate policy instruments were found to be very effective in the short-run, but monetary policy instruments were not.

Les travaux de recherche présentés portent sur l’étude de la variabilité inter et intra spécifique des substances extractibles des écorces d’Abies alba, Picea abies, Pseudotsuga menziesii, Quercus robur et Fagus sylvatica. La connaissance de la variabilité de la composition chimique des écorces est un outil d’aide à la décision pour une valorisation industrielle future des écorces, qui apporterait une plus-value à la filière bois. Ainsi, les constituants des écorces sont séparés en deux catégories avec les polyphénols qui ont été analysés principalement à partir des extraits eau/éthanol et les biopolymères qui ont été extraits à partir des résidus d’écorces provenant des extractions successives. Pour les extractions eau/éthanol (1:1), 5 essences avec 8 arbres par essence et plus de 10 hauteurs par arbres ont été extraits. Une augmentation des taux d’extraits avec la hauteur a été observée pour les résineux et d’après ces résultats, il semble que les résineux contiennent davantage de matières extractibles que les feuillus et que la quantité d’extractibles augmente avec la hauteur. Pour les extractions successives avec le mélange toluène/éthanol (2:1), puis avec l’éthanol (100%), seulement 4 arbres par essence ont été extraits car il s’agissait essentiellement de récupérer des écorces dépourvues d’extractibles en vue d’analyser les biopolymères. Par la suite, les extraits d’écorces ont été étudiés grâce à des méthodes analytiques complémentaires que sont la LC-UV-MS, GC-MS, IR, RMN1H, MALDI-TOF, SEC ainsi que des tests spécifiques permettant de déterminer les taux de phénols totaux, holocellulose, α-cellulose, hémicellulose, lignine, subérine et cendres. Les résultats mettent en évidence l’existence d’une variabilité de la composition chimique des écorces de résineux en fonction de la hauteur : les taux de polyphénols, subérine, lignine et holocellulose diminuent avec la hauteur tandis que les taux de terpènes et de cendres augmentent avec la hauteur. A l’aide du même protocole analytique, une variabilité interarbres importante a été mise en évidence pour les écorces de feuillus. Certaines différences observées ont été expliquées par des paramètres biologiques tels que la hauteur, l’âge des tissus, les conditions de stockage des écorces ou encore une allométrie différente entre les arbres<br>This work focuses on the study of the inter and intra specific variability of the extractives substances of the barks of Abies alba, Picea abies, Pseudotsuga menziesii, Quercus robur and Fagus sylvatica. New knowledge about the variability in chemical composition of different bark types can be used as a support decision tool for a future industrial valorization, which would bring an added value to the wood industry. Thus, the constituents of the bark are separated into two categories with the polyphenols which were analyzed mainly from the water/ethanol extracts and the biopolymers which were extracted from the bark residues from the successive extractions. For the water/ethanol extractions (1:1), 5 species with 8 trees per species and more than 10 heights per tree were extracted. An increase of extractives rates with height was observed for softwoods and from these results it appears that softwoods contain more extractives than hardwoods and that the amount of extractive increases with height. For the successive extractions with the toluene / ethanol mixture (2:1), then with the ethanol (100%), only 4 trees by essence were extracted because it was essentially a question of recovering bark devoid of extractives in view of analyze biopolymers. Subsequently, the bark extracts were studied using complementary analytical methods such as LC-UV-MS, GC-MS, IR, 1H NMR, MALDI-TOF, SEC as well as specific tests to determine the levels. total phenols, holocellulose, α-cellulose, hemicellulose, lignin, suberin and ash. The results demonstrate the existence of variability in the chemical composition of softwood bark as a function of height: the levels of polyphenols, suberin, lignin and holocellulose decrease with height while the levels of terpenes and ash increase. with height. Using the same analytical protocol, significant inter-tree variability was demonstrated for hardwood bark. Some observed differences have been explained by biological parameters such as height, age of tissues, bark storage conditions or even different allometry between trees

Medidas para gerenciamento de saúde e segurança em laboratórios são de extrema importância em laboratórios químicos. As pessoas que realizam qualquer atividade em um ambiente de laboratório estão expostas a diversos perigos e, consequentemente, existe o risco de ocorrência de eventos adversos para a saúde e segurança. Este trabalho foi desenvolvido em um laboratório químico de uma universidade federal tem como principal objetivo o aperfeiçoamento de uma framework que permite a realização de uma análise sistemática qualitativa e quantitativa das folgas presentes em um sistema sócio-técnico complexo. Ferramentas da Engenharia de Resiliência foram utilizadas para estudar o laboratório, o qual foi considerado como um sistema sócio-técnico complexo. Uma das características de um sistema resiliente é a capacidade de lidar com a variabilidade, o que pode ser obtido por meio de recursos de folgas (slack) no sistema. O uso da framework permitiu obter dados importantes para a análise do sistema e sugestões de melhorias. Os aperfeiçoamentos propostos na framework mostraram-se eficazes, principalmente na quantificação das folgas e variabilidades, em função da utilização do método AHP (Analytical Hierarchy Process) para a análise de dados. O método AHP tornou possível substituir o uso de questionários para toda a equipe por uma avaliação direcionada a especialistas. Ao utilizar o AHP, os dados podem ser adquiridos com maior rapidez. Outro ganho obtido com o uso do método AHP foi a possibilidade de redução de uma etapa da framework, tornando-a mais concisa.<br>Measures for health and safety management are of paramount importance in chemical laboratories. People who perform any activity in a laboratory environment are exposed to a variety of hazards and consequently there is a risk of adverse health and safety events. This work was developed in a chemical laboratory of a federal university, and has as main objective the improvement of a framework that allows the accomplishment of a systematic qualitative and quantitative analysis of the slack present in a complex socio-technical system. Tools of Resilience Engineering were used for studying a chemical laboratory, which was considered as a complex socio-technical system. One of the characteristics of a resilient system is the ability to deal with variability, which can be obtained through slack resources in the system. This work was developed in a chemical laboratory of a federal university and consists in the improvement of a framework that allows the accomplishment of a systematic qualitative and quantitative analysis of the slack present in the system. The use of the framework allowed to obtain data important for the analysis of the system and suggestions for improvements. The improvements proposed in the framework proved to be effective, especially in the quantification of slack and variability, as a function of the AHP (Analytical Hierarchy Process) method for data collection. The AHP method made it possible to replace the use of questionnaires for the entire team by an expert team assessment. By using AHP, data can be acquired more quickly. Another gain obtained with the use of the AHP method was the possibility of reducing one stage of the framework, making it more concise.

Clinical applications for heart rate variability (HRV) have become increasingly popular, gaining momentum and value as societies increased understanding of physiology reveals their true potential to reflect health. An additional reason for the rising popularity of HRV analysis, along with many other algorithmic based medical processes, is the relatively recent exponential increase of computing power and capabilities. Despite this many medical standards lag behind this booming increase in scientific knowledge, as the risks and precautions involved with healthcare necessarily take priority. Resultantly, the standards which pertain to the acceptable tolerance for accurate R-peak detection have remain unchanged for decades. For similar reasons, medical software is also prone to lag behind state-of-the-art developments. Yet, society is currently on the precipice of an age of high computational abilities, mass data storage, and capabilities to apply deep learning algorithms to reveal patterns that were previously inconceivable. So, when considering the needs of the future in relation to the place of HRV in healthcare, there is a distinct need for its accurate and precise collection, storage, and processing. In the work presented in this dissertation, the overarching aim was to increase the reliability of electrocardiogram (ECG) based HRV for use in predictive health analytics. To ensure both clarity and attainability, this project-level aim was broken down and addressed in a series of several works. The first a im w ork w as t o address the problems associated with the precision specified f or a ccurate p eak d etection, and thereby increase the reliability of predictive health analytics generated using HRV metrics. The study conducted around this initial aim investigates the specifics of match window requirements, clarifies the difference between fiducial marker and QRS complex detection, and makes recommendations on the precision required for accurate HRV metric extraction. In the second work, the aim was to ensure that there is a reliable foundation for the conduction of HRV-related research. Here, a thorough investigation of relevant literature revealed the lack of a suitable software, particularly for research requiring the analysis of large databases. Consequently, an improved HRV analysis platform was developed. Through use of both user-feedback and quantitative comparison to highly regarded software, the proposed platform is shown to offer a similar standard in estimated HRV metrics but requires significantly l ess manual e ffort (batch-processing approach) than the traditional single patient focused approach. The third work also addressed this aim, providing the base peak detection algorithm implemented within the HRV analysis platform. Experimentation undertaken here ensured that the developed algorithm performed precise fiducial marker detection, thereby increasing the reliability of the generated HRV metrics (measured against the framework presented in the first work). In the fourth work, the aim was to address the lack of published literature on the relationship between ECG sampling frequency (fs) and extracted HRV, in order to further ensure the reliability of predictive health analytics generated using HRV metrics. Here, a quantitative experimental approach was taken to evaluate the impact of ECG fs on subsequent estimations of HRV. This experimentation resulted in a recommendation for the minimum required ECG fs for reliable HRV extraction. The aim of the final work was to further improve the foundation for future predicative health analytics, by developing a robust pre-processing algorithm capable of autonomous detection of regions of valid ECG signal. This type of algorithm should be considered of critical importance to furthering machine learning (ML) based applications in the medical field. ML algorithms are heavily reliant on access to vast amounts of data, and without an automated pre-processing stage would require an unrealistic amount of hand-processing for implementation.<br>Thesis (PhD Doctorate)<br>Doctor of Philosophy (PhD)<br>School of Eng & Built Env<br>Science, Environment, Engineering and Technology<br>Full Text