18,483 research outputs found
Assessing performance of artificial neural networks and re-sampling techniques for healthcare datasets.
Re-sampling methods to solve class imbalance problems have shown to improve classification accuracy by mitigating the bias introduced by differences in class size. However, it is possible that a model which uses a specific re-sampling technique prior to Artificial neural networks (ANN) training may not be suitable for aid in classifying varied datasets from the healthcare industry. Five healthcare-related datasets were used across three re-sampling conditions: under-sampling, over-sampling and combi-sampling. Within each condition, different algorithmic approaches were applied to the dataset and the results were statistically analysed for a significant difference in ANN performance. The combi-sampling condition showed that four out of the five datasets did not show significant consistency for the optimal re-sampling technique between the f1-score and Area Under the Receiver Operating Characteristic Curve performance evaluation methods. Contrarily, the over-sampling and under-sampling condition showed all five datasets put forward the same optimal algorithmic approach across performance evaluation methods. Furthermore, the optimal combi-sampling technique (under-, over-sampling and convergence point), were found to be consistent across evaluation measures in only two of the five datasets. This study exemplifies how discrete ANN performances on datasets from the same industry can occur in two ways: how the same re-sampling technique can generate varying ANN performance on different datasets, and how different re-sampling techniques can generate varying ANN performance on the same dataset
EVALUACIÓN ANALGÉSICA PERIOPERATORIA DEL ACETAMINOFÉN EN PERRAS SOMETIDAS A OVARIOHISTERECTOMÍA ELECTIVA
Tesis de doctorado que evalúa el efecto analgésico del acetaminofén en perras ovarihisterectomizadas.La administración de analgésicos antiinflamatorios no esteroidales (AINES) para el control del
dolor post-quirúrgico en perros es una práctica común, debido a sus efectos analgésicos,
antiinflamatorios y antipiréticos. En el presente trabajo se realizaron dos estudios. En el
experimento 1, el objetivo fue evaluar la analgesia post-operatoria del acetaminofén
(paracetamol) a través de la utilización de las escalas de reconocimiento clínico del dolor
DIVAS (Escala Dinámica e Interactiva Analógica Visual) y UMPS (Escala de la Universidad
de Melbourne), en perras sometidas a ovariohisterectomía electiva. Además de valorar la
seguridad y eficacia clínica del uso del acetaminofén en perros mediante pruebas de
funcionamiento hepático y renal en el post-operatorio inmediato. Para ello, se utilizaron 30
perras de diferentes razas que fueron asignadas aleatoriamente a uno de los tres grupos de
tratamiento: acetaminofén [GACET; n=10, 15 mg kg-1 intravenoso (IV)], carprofeno (GCARP;
n=10, 4 mg kg-1 IV) y meloxicam (GMELOX; n=10, 0.2 mg kg-1 IV). Todos los tratamientos se
administraron 30 minutos antes de la cirugía y posterior a esta durante 48 horas. En este período
el acetaminofén se administró por vía oral cada 8 horas (15 mg kg-1); el carprofeno (4 mg kg-1)
y el meloxicam (0.1 mg kg-1) se administraron por vía IV cada 24 horas. Durante el
postoperatorio, los sistemas de puntuación del dolor DIVAS y UMPS fueron medidos a las 1,
2, 4, 6, 8, 12, 16, 20, 24, 36 y 48 horas post-cirugía. Para evaluar la seguridad clínica de los
tratamientos, se recolectaron muestras de sangre de la vena yugular para realizar la medición de
enzimas ALT, AST, ALP, y los metabolitos bilirrubina directa, bilirrubina indirecta, bilirrubina
total, creatinina, urea, albúmina y glucosa. Esto fue realizado en T0 (pre-anestesia; TBASAL), 48
y 96 horas después de la cirugía (T48, T96). Los resultados indican que en la evaluación clínica
del dolor de todos los grupos de estudio, hubo una reducción gradual en la percepción del mismo
durante el postoperatorio en ambos sistemas de puntuación; no obstante, también fue observado
que ninguna escala difirió significativamente entre los tres grupos de tratamiento (P>0.05) en
cada momento de evaluación durante las 48 horas post-cirugía. En cuanto a los parámetros
bioquímico séricos, sólo la ALT aumentó significativamente en T96 en el GACET y GCARP con
respecto a los valores basales (P<0.01). El resto de los analitos séricos evaluados se mantuvo
en rangos normales. En el experimento 2 bajo el mismo diseño experimental de tratamientos
administrados, el objetivo fue evaluar el efecto analgésico perioperatorio del acetaminofén
2
administrado pre y post-quirúrgicamente en perras sometidas a ovariohisterectomía electiva a
través de la medición del índice de la actividad del tono parasimpático (PTA). Este parámetro
hemodinámico fue medido 60 minutos antes de la cirugía (TBASAL) y durante el transquirúrgico
en la aplicación de estímulos nociceptivos: colocación de las pinzas de campo backhouse
(TPINZ), incisión de piel y abordaje quirúrgico primario (TINC), ligadura y extracción de pedículo
ovárico izquierdo (TOVI) y derecho (TOVD), ligadura y transfixión del cuello uterino (TLIGUT),
sección quirúrgica del cuello uterino (TCUT), reconstrucción de peritoneo y planos anatómicos
musculares (TMUSC) y sutura de piel (TSUT). Durante el postoperatorio, el índice PTA fue
valorado a las 1, 2, 4, 6, 8, 12, 16, 20, 24, 36 y 48 horas, en los mismos tiempos en que fueron
evaluadas las escalas de reconocimiento de dolor DIVAS y UMPS. Los resultados obtenidos en
la medición del índice PTA basal para GACET fue 65 ± 8, para GCARP 65 ± 7 y para GMELOX 62 ±
5. Durante los diferentes tiempos transquirúrgicos, los valores promedio de índice PTA indican
que GACET (76 ± 14) y GMELOX (72 ± 18) muestran tendencia a manifestar mayores niveles en
comparación con GCARP (62 ± 13) desde el inicio del procedimiento quirúrgico sin que esto
pudiera comprobarse estadísticamente, ya que no hubo diferencias significativas entre grupos
de tratamiento ni entre los tiempos quirúrgicos evaluados (P>0.05). En el postoperatorio, el
índice PTA fue de 65 ± 9 en el GACET, 63 ± 8 en el GCARP y 65 ± 8 en el GMELOX. Los resultados
tampoco mostraron diferencias estadísticamente significativas con los valores basales o entre
los tratamientos (P>0.05). El índice PTA postoperatorio mostró una sensibilidad del 40%,
especificidad del 98.46% y valor predictivo negativo del 99.07% con respecto a la escala
validada de UMPS. En conclusión, el acetaminofén puede considerarse una herramienta para el
tratamiento efectivo del dolor perioperatorio agudo en perros, ya que mostró la misma eficacia
clínica que el meloxicam y el carprofeno para la analgesia postquirúrgica en perras sometidas a
ovariohisterectomía electiva. Además, la evidencia del uso de este medicamento no condujo a
reacciones adversas o cambios en los parámetros evaluados, lo que indica su seguridad clínica.
Finalmente, destacar que el índice PTA representa una medición objetiva del comfort y
analgesia postoperatoria, por lo que es una herramienta que podría ayudar a predecir las
respuestas hemodinámicas asociadas con el dolor
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European Heart Rhythm Association (EHRA)/Heart Rhythm Society (HRS)/Asia Pacific Heart Rhythm Society (APHRS)/Latin American Heart Rhythm Society (LAHRS) Expert Consensus Statement on the state of genetic testing for cardiac diseases.
Exploring the effects of spinal cord stimulation for freezing of gait in parkinsonian patients
Dopaminergic replacement therapies (e.g. levodopa) provide limited to no response for axial motor symptoms including gait dysfunction and freezing of gait (FOG) in Parkinson’s disease (PD) and Richardson’s syndrome progressive supranuclear palsy (PSP-RS) patients. Dopaminergic-resistant FOG may be a sensorimotor processing issue that does not involve basal ganglia (nigrostriatal) impairment. Recent studies suggest that spinal cord stimulation (SCS) has positive yet variable effects for dopaminergic-resistant gait and FOG in parkinsonian patients. Further studies investigating the mechanism of SCS, optimal stimulation parameters, and longevity of effects for alleviating FOG are warranted. The hypothesis of the research described in this thesis is that mid-thoracic, dorsal SCS effectively reduces FOG by modulating the sensory processing system in gait and may have a dopaminergic effect in individuals with FOG. The primary objective was to understand the relationship between FOG reduction, improvements in upper limb visual-motor performance, modulation of cortical activity and striatal dopaminergic innervation in 7 PD participants. FOG reduction was associated with changes in upper limb reaction time, speed and accuracy measured using robotic target reaching choice tasks. Modulation of resting-state, sensorimotor cortical activity, recorded using electroencephalography, was significantly associated with FOG reduction while participants were OFF-levodopa. Thus, SCS may alleviate FOG by modulating cortical activity associated with motor planning and sensory perception. Changes to striatal dopaminergic innervation, measured using a dopamine transporter marker, were associated with visual-motor performance improvements. Axial and appendicular motor features may be mediated by non-dopaminergic and dopaminergic pathways, respectively. The secondary objective was to demonstrate the short- and long-term effects of SCS for alleviating dopaminergic-resistant FOG and gait dysfunction in 5 PD and 3 PSP-RS participants without back/leg pain. SCS programming was individualized based on which setting best improved gait and/or FOG responses per participant using objective gait analysis. Significant improvements in stride velocity, step length and reduced FOG frequency were observed in all PD participants with up to 3-years of SCS. Similar gait and FOG improvements were observed in all PSP-RS participants up to 6-months. SCS is a promising therapeutic option for parkinsonian patients with FOG by possibly influencing cortical and subcortical structures involved in locomotion physiology
Machine learning based adaptive soft sensor for flash point inference in a refinery realtime process
In industrial control processes, certain characteristics are sometimes difficult to measure by a physical sensor due to technical and/or economic limitations. This fact is especially true in the petrochemical industry. Some of those quantities are especially crucial for operators and process safety. This is the case for the automotive diesel Flash Point Temperature (FT). Traditional methods for FT estimation are based on the study of the empirical inference between flammability properties and the denoted target magnitude. The necessary measures are taken indirectly by samples from the process and analyzing them in the laboratory, this process implies time (can take hours from collection to flash temperature measurement) and thus make it very difficult for real-time monitorization, which in fact results in security and economical losses. This study defines a procedure based on Machine Learning modules that demonstrate the power of real-time monitorization over real data from an important international refinery. As input, easily measured values provided in real-time, such as temperature, pressure, and hydraulic flow are used and a benchmark of different regressive algorithms for FT estimation is presented. The study highlights the importance of sequencing preprocessing techniques for the correct inference of values. The implementation of adaptive learning strategies achieves considerable economic benefits in the productization of this soft sensor. The validity of the method is tested in the reality of a refinery. In addition, real-world industrial data sets tend to be unstable and volatile, and the data is often affected by noise, outliers, irrelevant or unnecessary features, and missing data. This contribution demonstrates with the inclusion of a new concept, called an adaptive soft sensor, the importance of the dynamic adaptation of the conformed schemes based on Machine Learning through their combination with feature selection, dimensional reduction, and signal processing techniques. The economic benefits of applying this soft sensor in the refinery's production plant and presented as potential semi-annual savings.This work has received funding support from the SPRI-Basque Gov-
ernment through the ELKARTEK program (OILTWIN project, ref. KK-
2020/00052)
How to Be a God
When it comes to questions concerning the nature of Reality, Philosophers and Theologians have the answers.
Philosophers have the answers that can’t be proven right. Theologians have the answers that can’t be proven wrong.
Today’s designers of Massively-Multiplayer Online Role-Playing Games create realities for a living. They can’t spend centuries mulling over the issues: they have to face them head-on. Their practical experiences can indicate which theoretical proposals actually work in practice.
That’s today’s designers. Tomorrow’s will have a whole new set of questions to answer.
The designers of virtual worlds are the literal gods of those realities. Suppose Artificial Intelligence comes through and allows us to create non-player characters as smart as us. What are our responsibilities as gods? How should we, as gods, conduct ourselves?
How should we be gods
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Reliable Decision-Making with Imprecise Models
The rapid growth in the deployment of autonomous systems across various sectors has generated considerable interest in how these systems can operate reliably in large, stochastic, and unstructured environments. Despite recent advances in artificial intelligence and machine learning, it is challenging to assure that autonomous systems will operate reliably in the open world. One of the causes of unreliable behavior is the impreciseness of the model used for decision-making. Due to the practical challenges in data collection and precise model specification, autonomous systems often operate based on models that do not represent all the details in the environment. Even if the system has access to a comprehensive decision-making model that accounts for all the details in the environment and all possible scenarios the agent may encounter, it may be intractable to solve this complex model optimally. Consequently, this complex, high fidelity model may be simplified to accelerate planning, introducing imprecision. Reasoning with such imprecise models affects the reliability of autonomous systems. A system\u27s actions may sometimes produce unexpected, undesirable consequences, which are often identified after deployment. How can we design autonomous systems that can operate reliably in the presence of uncertainty and model imprecision?
This dissertation presents solutions to address three classes of model imprecision in a Markov decision process, along with an analysis of the conditions under which bounded-performance can be guaranteed. First, an adaptive outcome selection approach is introduced to devise risk-aware reduced models of the environment that efficiently balance the trade-off between model simplicity and fidelity, to accelerate planning in resource-constrained settings. Second, a framework that extends stochastic shortest path framework to problems with imperfect information about the goal state during planning is introduced, along with two solution approaches to solve this problem. Finally, two complementary solution approaches are presented to minimize the negative side effects of agent actions. The techniques presented in this dissertation enable an autonomous system to detect and mitigate undesirable behavior, without redesigning the model entirely
Response of saline reservoir to different phaseCO₂-brine: experimental tests and image-based modelling
Geological CO₂ storage in saline rocks is a promising method for meeting the target of net zero emission and minimizing the anthropogenic CO₂ emitted into the earth’s atmosphere. Storage of CO₂ in saline rocks triggers CO₂-brine-rock interaction that alters the properties of the rock. Properties of rocks are very crucial for the integrity and efficiency of the storage process. Changes in properties of the reservoir rocks due to CO₂-brine-rock interaction must be well predicted, as some changes can reduce the storage integrity of the reservoir. Considering the thermodynamics, phase behavior, solubility of CO₂ in brine, and the variable pressure-temperature conditions of the reservoir, there will be undissolved CO₂ in a CO₂ storage reservoir alongside the brine for a long time, and there is a potential for phase evolution of the undissolved CO₂. The phase of CO₂ influence the CO₂-brine-rock interaction, different phaseCO₂-brine have a unique effect on the properties of the reservoir rocks, Therefore, this study evaluates the effect of four different phaseCO₂-brine reservoir states on the properties of reservoir rocks using experimental and image-based approach.
Samples were saturated with the different phaseCO₂-brine, then subjected to reservoir conditions in a triaxial compression test. The representative element volume (REV)/representative element area (REA) for the rock samples was determined from processed digital images, and rock properties were evaluated using digital rock physics and rock image analysis techniques. This research has evaluated the effect of different phaseCO₂-brine on deformation rate and deformation behavior, bulk modulus, compressibility, strength, and stiffness as well as porosity and permeability of sample reservoir rocks. Changes in pore geometry properties, porosity, and permeability of the rocks in CO₂ storage conditions with different phaseCO₂-brine have been evaluated using digital rock physics techniques. Microscopic rock image analysis has been applied to provide evidence of changes in micro-fabric, the topology of minerals, and elemental composition of minerals in saline rocks resulting from different phaseCO₂-br that can exist in a saline CO₂ storage reservoir. It was seen that the properties of the reservoir that are most affected by the scCO₂-br state of the reservoir include secondary fatigue rate, bulk modulus, shear strength, change in the topology of minerals after saturation as well as change in shape and flatness of pore surfaces. The properties of the reservoir that is most affected by the gCO₂-br state of the reservoir include primary fatigue rate, change in permeability due to stress, change in porosity due to stress, and change topology of minerals due to stress. For all samples, the roundness and smoothness of grains as well as smoothness of pores increased after compression while the roundness of pores decreased. Change in elemental composition in rock minerals in CO₂-brine-rock interaction was seen to depend on the reactivity of the mineral with CO₂ and/or brine and the presence of brine accelerates such change. Carbon, oxygen, and silicon can be used as index minerals for elemental changes in a CO₂-brine-rock system. The result of this work can be applied to predicting the effect the different possible phases of CO₂ will have on the deformation, geomechanics indices, and storage integrity of giant CO₂ storage fields such as Sleipner, In Salah, etc
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Privacy-aware Smart Home Interface Framework
Smart home user interfaces are pervasive and shared by multiple users who occupy the space. Therefore, they pose a risk to interpersonal privacy of occupants because an individual’s sensitive information can be leaked to other co-occupants (information privacy), or they can be disturbed by intrusions into their personal space (physical privacy) when the co-occupant interacts with the smart home user interfaces. This thesis hypothesises that interpersonal privacy violations can be mitigated by adapting the user interface layer and presents insights into how to achieve usable user interface adaptation to mitigate or minimise interpersonal privacy violations in smart homes.
The thesis reports two case studies and two user studies. The first case study identifies the key characteristics needed to model the rich context of interpersonal privacy violations scenarios. Then it presents knowledge representation models that are required to represent the identified characteristics and evaluates them for adequacy in modelling the context information of interpersonal privacy violation scenarios. The second case study presents a software architecture and a set of algorithms that can detect interpersonal privacy violations and generate usable user interface adaptations. Then it evaluates the architecture and the algorithms for adequacy in generating usable privacy-aware user interface adaptations. The first user study (N=15) evaluates the usability of the adaptive user interfaces generated from the framework where storyboards were used as the stimulant. Extending the findings from the usability study and expanding the coverage of example scenarios, the second user study (N=23) evaluates the overall user experience of the adaptive user interfaces, using video prototypes as the stimulant.
The research demonstrates that the characteristics identified, and the respective knowledge representation models adequately captured the context of interpersonal privacy violation scenarios. Furthermore, the software architecture and the algorithms could detect possible interpersonal privacy violations and generate usable user interface adaptations to mitigate them. The two user studies demonstrate that the adaptive user interfaces, when used in appropriate situations, were a suitable solution for addressing interpersonal privacy violations while providing high usability and a positive user experience. The thesis concludes by providing recommendations for developing privacy-aware user interface adaptations and suggesting future work that can extend this research
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