Deep learning-based improvement for the outcomes of glaucoma clinical trials

Glaucoma is the leading cause of irreversible blindness worldwide. It is a progressive optic neuropathy in which retinal ganglion cell (RGC) axon loss, probably as a consequence of damage at the optic disc, causes a loss of vision, predominantly affecting the mid-peripheral visual field (VF). Glaucoma results in a decrease in vision-related quality of life and, therefore, early detection and evaluation of disease progression rates is crucial in order to assess the risk of functional impairment and to establish sound treatment strategies. The aim of my research is to improve glaucoma diagnosis by enhancing state of the art analyses of glaucoma clinical trial outcomes using advanced analytical methods. This knowledge would also help better design and analyse clinical trials, providing evidence for re-evaluating existing medications, facilitating diagnosis and suggesting novel disease management. To facilitate my objective methodology, this thesis provides the following contributions: (i) I developed deep learning-based super-resolution (SR) techniques for optical coherence tomography (OCT) image enhancement and demonstrated that using super-resolved images improves the statistical power of clinical trials, (ii) I developed a deep learning algorithm for segmentation of retinal OCT images, showing that the methodology consistently produces more accurate segmentations than state-of-the-art networks, (iii) I developed a deep learning framework for refining the relationship between structural and functional measurements and demonstrated that the mapping is significantly improved over previous techniques, iv) I developed a probabilistic method and demonstrated that glaucomatous disc haemorrhages are influenced by a possible systemic factor that makes both eyes bleed simultaneously. v) I recalculated VF slopes, using the retinal never fiber layer thickness (RNFLT) from the super-resolved OCT as a Bayesian prior and demonstrated that use of VF rates with the Bayesian prior as the outcome measure leads to a reduction in the sample size required to distinguish treatment arms in a clinical trial

Definition and process-based classification of caves

Cave is re-defined in order to be linked to the cave formation processes, to cover the known cave types, to differentiate from porosity and contiguous spaces, to be applied also in a continuum of size and to avoid explorational bias. Despite the scientific basis, the proposed definition remains simple enough to be used by cavers and non-specialists. Following this definition, a classification scheme that is also process-based combines the known cave types. Clustering is based on five levels of classification, from which the first two levels define the major cave categories. The rest of the branching is the result of variation in settings and formation agents. A discussion on various classifications and definitions reveals the non-static character of such schemes that tend to change in relation to the progress of research cave census and improved communication of scientists on previously and new discovered caves

Morfološke značilnosti in pogoji nastanka jam v speleoparku Almopia (Loutra Almopias, severna Grčija)

The Almopia Speleopark caves are located at the Almopia basin in northern Greece, at the foothill of Voras Mountain, and are formed in the Maestrichtian limestones of the Pelagonian zone. They are studied on the basis of their meso- and micro-scale morphology as well as their horizontal pattern, in order to investigate the character of the forming aquifer. Emphasis is given on the morphological description of the Loutra Almopias Cave. Cave morphology is dominated by the presence of cupolas, rock bridges, ridges and “windows”, abrupt terminations of fracture guided passages, pendants, rising channels, pseudonotches, false-floors and spongework. Speleogens indicate a speleogenesis due to slowly natural convecting hot water bodies. Phreatic calcite from the Varathron Cave is analyzed on the basis of the fluid inclusions in order to investigate the physicochemical conditions of the convecting water bodies. This has shown that the calcite was formed at temperatures ranging between 120 and 189 ºC, with a peak around 150 ºC. The fluids were dominated by NaCl of very low salinities (0.2-1.0 wt% NaCl equiv.), showing probably the incorporation of meteoric waters.Speleopark Almopia je ob vznožju gore Voras v porečju Almopije v Severni Grčiji. Jame so nastale v maastrichtskih apnencih Pelagonske cone. Z analizo jamskih skalnih oblik in tlorisov jam smo raziskovali pogoje njihovega nastanka. Največ raziskav smo naredili v jami Loutra Almopias. Med oblikami so značilne kupole, skalni mostovi, skalni grebeni, okna, nezvezni zaključki rovov nastalih ob razpokah, dvigajoči se kanali, lažne korozijske zajede in tla ter prepleti drobnih kanalov. Iz skalnih oblik sklepamo, da so jame nastale s korozijo počasnih konvekcijskih tokov termalne vode. Fizikalno-kemične pogoje speleogene­ze smo določali z analizo tekočinskih vključkov v freatičnem kalcitu iz jame Varathron. Temperatura izločanja kalcita je bila med 120°C in 180°C, z vrhom temperaturne porazdelitve pri 150°C. Nizka slanost tekočinskih vključkov, masni delež soli je med 0,1% in 1%, kaže da tekočinske vključke sestavlja pretežno meteorna voda

A Review of Models for Photovoltaic Crack and Hotspot Prediction

The accurate prediction of the performance output of photovoltaic (PV) installations is becoming ever more prominent. Its success can provide a considerable economic benefit, which can be adopted in maintenance, installation, and when calculating levelized cost. However, modelling the long-term performance output of PV modules is quite complex, particularly because multiple factors are involved. This article investigates the available literature relevant to the modelling of PV module performance drop and failure. A particular focus is placed on cracks and hotspots, as these are deemed to be the most influential. Thus, the key aspects affecting the accuracy of performance simulations were identified and the perceived relevant gaps in the literature were outlined. One of the findings demonstrates that microcrack position, orientation, and the severity of a microcrack determines its impact on the PV cell’s performance. Therefore, this aspect needs to be categorized and considered accordingly, for achieving accurate predictions. Additionally, it has been identified that physical modelling of microcracks is currently a considerable challenge that can provide beneficial results if executed appropriately. As a result, suggestions have been made towards achieving this, through the use of methods and software such as XFEM and Griddler