Automatic evaluation of wavelength spectra from quartz by means of machine learning

I have for this thesis worked on automating the steps in evaluating wavelength spectra from quartz. To do this I have applied different machine learning algorithms suitable to the automation requirement at each step, and they were implemented using Python. A neural network is used to fit a curve to cathodoluminescence spectra from quartz. From this curve are feature values extracted, which are then to be used by a machine learning classification algorithm to predict which of three defined groups a quartz sample belong to. Two machine learning classification algorithms have been evaluated: The kNN algorithm and the Random Forest algorithm. These algorithms were trained on multiple feature subsets derived from two different datasets. The difference between the two datasets is that one, the reduced dataset, has had cathodoluminescence spectra influenced, primarily by feldspar, removed. The classification algorithm whose model achieved the highest accuracy was the kNN algorithm with 84%. It achieved this when trained on a feature subset derived from the reduced dataset where only features representing intensity were included. The best performing model by the Random Forest algorithm achieved an accuracy of 81%

Hvordan kan sykepleieren gjennom faglig forsvarlig praksis forebygge komplikasjoner ved stell av sentralt venekateter?

Studentarbeid i sykepleie (bachelorgrad) - Høgskolen i Bodø, 201

Å være tilstede for pasienten : hva kan sykepleieren bidra med for å redusere noe av angsten til pasienter med KOLS eksacerbasjon?

Studentarbeid i sykepleie (bachelorgrad) - Universitetet i Nordland, 201

Frykt : hvordan kan frykt påvirke oss?

Fagessay i militære studier; ledelse og landmak

Holdninger til kjønn og kjønnsroller. En intervjustudie med helsesykepleiere

Denne studiens formål var å undersøke et utvalg av norske helsesykepleieres strategier og holdninger til kjønn og kjønnsroller i møte med barn og unge og om hvordan de følger opp de politiske intensjonene om å bruke et kjønnsnøytralt språk til ungdom. Det ble gjennomført fem dybdeintervjuer av helsesykepleiere i ulike kommuner i Nordland og Troms og Tjoras stegvis deduktive induktive metode ble benyttet i analysen

The discriminatory value of cardiorespiratory interactions in distinguishing awake from anaesthetised states: a randomised observational study

Depth of anaesthesia monitors usually analyse cerebral function with or without other physiological signals; noninvasive monitoring of the measured cardiorespiratory signals alone would offer a simple, practical alternative. We aimed to investigate whether such signals, analysed with novel, non-linear dynamic methods, would distinguish between the awake and anaesthetised states. We recorded ECG, respiration, skin temperature, pulse and skin conductivity before and during general anaesthesia in 27 subjects in good cardiovascular health, randomly allocated to receive propofol or sevoflurane. Mean values, variability and dynamic interactions were determined. Respiratory rate (p = 0.0002), skin conductivity (p = 0.03) and skin temperature (p = 0.00006) changed with sevoflurane, and skin temperature (p = 0.0005) with propofol. Pulse transit time increased by 17% with sevoflurane (p = 0.02) and 11% with propofol (p = 0.007). Sevoflurane reduced the wavelet energy of heart (p = 0.0004) and respiratory (p = 0.02) rate variability at all frequencies, whereas propofol decreased only the heart rate variability below 0.021 Hz (p < 0.05). The phase coherence was reduced by both agents at frequencies below 0.145 Hz (p < 0.05), whereas the cardiorespiratory synchronisation time was increased (p < 0.05). A classification analysis based on an optimal set of discriminatory parameters distinguished with 95% success between the awake and anaesthetised states. We suggest that these results can contribute to the design of new monitors of anaesthetic depth based on cardiovascular signals alone

Coherence and Coupling Functions Reveal Microvascular Impairment in Treated Hypertension

The complex interactions that give rise to heart rate variability (HRV) involve coupled physiological oscillators operating over a wide range of different frequencies and length-scales. Based on the premise that interactions are key to the functioning of complex systems, the time-dependent deterministic coupling parameters underlying cardiac, respiratory and vascular regulation have been investigated at both the central and microvascular levels. Hypertension was considered as an example of a globally altered state of the complex dynamics of the cardiovascular system. Its effects were established through analysis of simultaneous recordings of the electrocardiogram (ECG), respiratory effort, and microvascular blood flow [by laser Doppler flowmetry (LDF)]. The signals were analyzed by methods developed to capture time-dependent dynamics, including the wavelet transform, wavelet-based phase coherence, non-linear mode decomposition, and dynamical Bayesian inference, all of which can encompass the inherent frequency and coupling variability of living systems. Phases of oscillatory modes corresponding to the cardiac (around 1.0 Hz), respiratory (around 0.25 Hz), and vascular myogenic activities (around 0.1 Hz) were extracted and combined into two coupled networks describing the central and peripheral systems, respectively. The corresponding spectral powers and coupling functions were computed. The same measurements and analyses were performed for three groups of subjects: healthy young (Y group, 24.4 ± 3.4 y), healthy aged (A group, 71.1 ± 6.6 y), and aged treated hypertensive patients (ATH group, 70.3 ± 6.7 y). It was established that the degree of coherence between low-frequency oscillations near 0.1 Hz in blood flow and in HRV time series differs markedly between the groups, declining with age and nearly disappearing in treated hypertension. Comparing the two healthy groups it was found that the couplings to the cardiac rhythm from both respiration and vascular myogenic activity decrease significantly in aging. Comparing the data from A and ATH groups it was found that the coupling from the vascular myogenic activity is significantly weaker in treated hypertension subjects, implying that the mechanisms of microcirculation are not completely restored by current anti-hypertension medications

Laser Doppler flowmetry signals: pointwise Hölder exponents of experimental signals from young healthy subjects and numerically simulated data

We analyze the complexity of laser Doppler flowmetry (LDF) signals which give a peripheral view of the cardiovascular system. For this purpose, experimental and numerically simulated LDF signals are processed. The experimental signals are recorded in young healthy subjects. The numerically simulated LDF data are computed from a model containing six nonlinear coupled oscillators reflecting six almost periodic rhythmic activities present in experimental LDF signals. In the model, the oscillators are coupled with both linear and parametric couplings in order to represent cardiovascular system behaviors. To our knowledge this modeling has never been proposed yet. The complexity of all the experimental and simulated signals is studied by the computation of pointwise Hölder exponents. The latter identify the possible multifractal characteristics of data. The pointwise Hölder exponents are determined with a parametric generalized quadratic variation based estimation method first calibrated from white noise measures. The results of our signal processing analysis show that experimental LDF signals are weakly multifractal for young healthy subjects at rest. Furthermore, our findings together with another recent work of our group show that pointwise Hölder exponents of the simulated data do not describe the ones of the young healthy subjects but are closer to the ones of elderly healthy people. This paper provides useful information to go deeper into the modeling of LDF data, that could bring enlightenment for a better understanding of the peripheral cardiovascular system