>Magister Scientiae - MSc / The Kuils River Catchment is an urban river catchment that forms part of the larger Kuils-Eerste River system draining the eastern half of the Cape Metropolitan Authority area and Stellenbosch Municipality. Rapid urbanisation has resulted in the encroachment of residential and industrial areas into the river system through channelization and sewage disposal. This research project intends to assess the quality of surface runoff in the Kuils River catchment and determining non-point source pollutant loading rates in the catchment using GIS-based modelling. The study results show how modelled potential sources of surface runoff and NPS pollutants using desktop GIS analysis tools in a sequential process that involved different levels of software applications could explain the characteristics of the catchment. With the help of the Expected Mean Concentration (EMC) values associated with surface runoff from land use/covers, NPS pollutant loads were assessed downstream towards the Kuils River Catchment outlet using the Nonpoint Source Pollution and Erosion Comparison Tool (N-SPECT) based in ArcGIS. The outputs from this model consist of predicted annual pollutant loading (mg/mvyear) for each Kuils-Eerste River that occurs in the catchment. The results have shown clearly the spatial distribution of sources of particular pollutants in the catchment. Further or advanced processing knowhow with this model might provide far reaching insights into the problem and it is however recommended that these results produced using N-SPECT be compared to those of other hydrologic models using the same inputs.

This master’s thesis deals with the automatic detection of myocardial infarction in ECG. Semester work consists of two parts. The theoretical part provides a description of the electrical conduction system of the heart, spreading of electrical activity through the heart muscle, the methods of ECG scanning and the ECG curve. There are also mentioned the causes of myocardial ischemia and various methods of its detection. Another part is devoted to high-frequency ECG, analysis of HFQRS and clinical studies which describe the use of high-frequency ECG in diagnosis of myocardial infarction. In the practical part is proposed an algorithm using low-frequency components ECG and an algorithm using high-frequency components ECG for automatic detection of myocardial infarction. The proposed algorithms are implemented in programming environment MATLAB and tested on signals from the PTB database. The final part of the master‘s thesis is devoted to the comparison of the success of myocardial infarction by means of low frequency and high frequency components of ECG and comparison of achieved results with results from clinical studies.

This thesis discusses about using the worldwide satellite system GPS for time and frequency synchronization. This thesis presents study about basic principles of the GPS system, its segments and ways of using this system. Some GPS receivers suitable for receiving the time marks (pulses) used for time synchronization are described. Thesis contents designing of the circuit that will receive time marks and it will digitalize and record external signal and send it with precision time information to PC for displaying and post processing. Thesis also discusses about both hardware and software development of the synchronization module and software used in PC.

A subject of this Thesis is the design of the complex system of measures of soil conservation in given catchment area, which will serve as a concept of complex land consolidation in cadaster Jerlochovice. Based on the analysis and the land survey, a feasible solution was designed using the hydrological and erosive tools of ArcGIS. For the identification of areas endangered by erosion and for the identification of runoff conditions, the Universal Soil Loss Equation of Wischmeier-Smith was used (in grid modification). Based on the calculated values, the suitable technical and agrotechnical measures of soil erosion control were designed. Each component was designed for the values of Qn from the model DesQ. Subsequently the efectivity of the designed measures was evaluated by the comparison of results of erosive and runoff conditions before and after the aplication of the soil and water conservation measures.

<p>Ishemijska bolest srca najče&scaron;će nastaje kao posledica razvoja aterosklerotskih promena na koronarnim krvnim sudovima koji dovode do suženja lumena i posledičnog pada protoka arterijske krvi u području vaskularizacije. Akutni oblik koronarne bolesti koji zahteva hitnu primenu reperfuzione terapije je ST elevirani infarkt miokarda. EKG ima veliki značaj u postavljanju dijagnoze ali i u proceni uspe&scaron;nosti same reperfuzije. &Scaron;irina QRS kompleksa jedan je od EKG parametara čija dinamika promena može ukazati na uspe&scaron;nost pPKI i veličinu infarktne zone. Evaluacija &scaron;irine QRS kompleksa kao prediktora veličine infarkta miokarda i reperfuzije nakon pPKI kod pacijenata sa STEMI. Ispitivanje je sprovedeno kao prospektivna, opservaciona klinička studija na Klinici za kardiologiju, Instituta za kardiovaskularne bolesti Vojvodine u periodu od januara 2016. do decembra 2018. godine. U isptivanje je uključeno 200 pacijenata sa STEMI kod kojih je urađena pPKI. Na osnovu dužine trajanja tegoba formirane su dve grupe od po 100 pacijenata. Grupa A kod kojih je totalno ishemijsko vreme bilo &lt;6h i grupa B kod kojih je totalno ishemijsko vreme između 6 i 12h. . Sprovedeno je EKG praćenje radi procene &scaron;irine QRS kompleksa intrahospitalno (pre procedure, odmah nakon pPKI kao i posle 1h i 72h) i na dve vizite ambulantno tokom &scaron;estomesečnog praćenja (nakon mesec dana i &scaron;est meseci). Ehokardiografija je urađena kod svih pacijenata intrahospitalno i na &scaron;estomesečnom ambulantnom pregledu. &Scaron;irine QRS kompleksa su korelirane sa rezultatima interventne procedure procenjene TIMI protokom i TMPG, dinamikom kardiospecifičnih enzima i ehokardiografskim nalazima. U istraživanje je uključeno 71% mu&scaron;karaca i 29% žena, prosečna starost uzorka iznosila je 60.6&plusmn;11.39. Dužina trajanja tegoba značajno se razlikovala između grupa. U grupi A tegobe su trajale prosečno 120 minuta (90-180), dok su u grupi B trajale 420 minuta (360-600) (p&lt;0.0005). DTB nije se značajno razlikovao, 42 minuta (31-54.5) u odnosu na 40.5 minuta (34.5-55) (p=0.818). Prosečna &scaron;irina QRS kompeksa na EKG-u pre pPKI nije se značajno razlikovala između grupa, 100 msec (90-110) u odnosu na 100 msec (93-110) (p=0.308). Nakon reperfuzije uočena je značajna razlika u &scaron;irini QRS kompleksa između grupa na svim intrahospitalnim kao i EKG-ima načinjenim tokom perioda praćenja. QRS kompleks je &scaron;iri kod pacijenata iz grupe B (p&lt;0.0005). Pacijenti iz grupe A koji su imali prohodnu infarktnu arteriju sa TIMI 3 protokom pre implantacije stenta imali su značajno uži QRS kompleks na incijilanom EKG-u u odnosu na pacijente kod kojih je IRA bila sub/okludirana sa TIMI protokom &le;2 (p=0.001). U grupi B prohodna infarktna arterija sa TIMI 3 protokom nije značajno uticala na &scaron;irinu QRS kompleksa na inicijalnom EKG-u (p=0.144). Na EKG-ima nakon procedure QRS kompleks bio je značajno &scaron;iri kod pacijenata kod kojih je TIMI protok &le;2, ali samo za grupu pacijenata koja se javila unutar 6h od početka tegoba (p=0.001). QRS kompleks kod pacijenata koji su se javili nakon 6h od početka tegoba jeste bio uži, ali bez statistički značajne razlike (p=0.336). Pearsonovim testom registrovano je postojanje negativne korelacije &scaron;irine QRS kompleksa i istisne frakcije leve komore, ali i pozitivne korelacije sa WMSI i indeksiranim end sistolnim i end dijastolnim volumenom. ROC analizom pokazano je da ukoliko je QRS kompleks &scaron;iri od 89 msec nakon mesec dana, 8.5 puta je veći rizik od snižene EF na &scaron;estomesečnoj kontroli (p&lt;0.0005, AUC=0.808, cut-off=89msec.). ROC analiza pokazala je i da ukoliko je QRS kompleks &scaron;iri od 99msec 1h nakon procedure, 5 puta je veći rizik od pojave MACE (p&lt;0.0005, AUC=0.744, cut-off=99msec). Izvedena su dva matematička modela zasnovana na &scaron;irini QRS kompleksa koja vr&scaron;e predikciju snižene EF i pojave MACE tokom perioda praćenja. &Scaron;irina QRS kompleksa je pokazatelj reperfuzije kod pacijenata sa STEMI kod kojih se načini revaskularizacija unutar 6h od nastanka tegoba. &Scaron;irina QRS kompleksa mesec dana nakon STEMI predstavlja nezavisni prediktor snižene EF. Pro&scaron;irenje preko 89msec 8.5 povećava rizik od snižene EF. &Scaron;irina QRS kompleksa jedan sat nakon pPKI predstavlja nezavisni prediktor za MACE. Pro&scaron;irenje preko 99msec 5 puta povećava rizik od neželjenog kardiolo&scaron;kog događaja. Izvedena su dva matematička modela koja koriste &scaron;irinu QRS kompleksa i sa visokom precizno&scaron;ću vr&scaron;e predikciju MACE-a, odnosno snižene EF nakon &scaron;est meseci.&nbsp;</p> / <p>Ischemic heart disease most commonly occurs as a result of the atherosclerotic changes in the coronary vessels that lead to the narrowing of the lumen and consequent fall in arterial blood flow in the vascularization area. An acute form of coronary artery disease requiring immediate reperfusion therapy is ST-elevation myocardial infarction. The ECG is of great importance not only in making the diagnosis but also in evaluating the success of the reperfusion itself. The duration of the QRS complex is one of the ECG parameters whose change in dynamics can indicate the success of pPCI as well as the size of the infarct zone. Evaluation of the width of the QRS complex as a predictor of myocardial infarction size and reperfusion after pPCI in patients with STEMI. The study was conducted as a prospective, observational clinical study at the Cardiology Clinic of the Institute of Cardiovascular Diseases of Vojvodina between January 2016 and December 2018. The study included 200 patients with STEMI in whom pPCI was performed. Based on the length of discomforts two groups with 100 patients were formed. Group A had a total ischemic time &lt;6h and the total ischemic time in group B was between 6-12h. To assess the duration of the QRS complex, the ECG monitoring was performed intrahospital (before the procedure, immediately after pPCI as well as 1h and 72h after the procedure) and on two outpatient visits during the six-month follow-up period (after one month and six months). Echocardiography was performed in all patients intrahospital and at a six-month outpatient visit. The duration of the QRS complex correlated with the results of the interventional procedure that was evaluated by the TIMI flow and TMPG, the dynamics of cardiospecific enzymes and echocardiography findings. The survey included 71% of men and 29% of women with an average age of 60.6 &plusmn; 11.39. The duration of the discomforts varied significantly between the groups. In group A the discomforts lasted 120 minutes in an average (90-180), while they lasted 420 minutes in group B (360-600) (p &lt;0.0005). DTB did not differ significantly, 42 minutes (31-54.5) versus 40.5 minutes (34.5-55) (p = 0.818). The average duration of the QRS complex on the ECG before pPCI did not differ significantly between the groups, 100 msec (90-110) versus 100 msec (93-110) (p = 0.308). After the reperfusion, a significant difference in the duration of the QRS complex was observed between the groups at all intrahospital ECGs and the ECGs performed during the follow-up period. The QRS complex was broader in group B patients (p &lt;0.0005). Group A patients who had a patent infarct artery with TIMI 3 flow before the stent implantation had a significantly narrower QRS complex on the initial ECG compared to the patients whose IRA was sub / occluded with TIMI flow &le;2 (p = 0.001). In group B, the patent infarct artery with TIMI 3 flow did not significantly affect the duration of the QRS complex at the initial ECG. (p = 0.144). At the post-procedural ECGs the QRS complex was significantly broader in patients with TIMI flow &le;2, but only in the group of patients who arrived within 6 h from the onset of discomforts (p = 0.001). The QRS complex in patients who arrived 6 h after the onset of discomforts was narrower but without statistically significant difference (p = 0.336). The Pearson test registered the existence of a negative correlation of the QRS complex width and the left ventricular ejection fraction, but also a positive correlation with the WMSI and index end-systolic and end-diastolic volumes. The ROC analysis showed that if the QRS complex was wider than 89 msec after one month, there was an 8.5 times higher risk of decreased EF at the six-month control examination (p &lt;0.0005, AUC = 0.808, cut-off = 89msec.). The ROC analysis also showed that if the QRS complex was wider than 99msec 1h after the procedure, there was a 5 times higher risk of MACE (p &lt;0.0005, AUC = 0.744, cut-off = 99msec). Two mathematical models based on the width of the QRS complex were derived that predicted the lowered EF and the occurrence of MACE during the monitored period. The width of the QRS complex is an indicator of reperfusion in patients with STEMI who undergo revascularization within 6 hours from the onset of discomforts. The width of the QRS complex one month after STEMI is an independent predictor of decreased EF. Broadening over 89msec increases the risk of lowered EF for 8.5 times. The width of the QRS complex one hour after pPCI represents an independent predictor of MACE. Broadening over 99msec increases the risk of an adverse cardiac event 5 times. Two mathematical models have derived that use the width of the QRS complex and predict MACE with high precision as well as reduced EF after six months.</p>

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Cette thèse cherche à examiner les conditions optimales dans lesquelles les surfaces cornéennes antérieures peuvent être efficacement pré-traitées, classifiées et prédites en utilisant des techniques de modélisation géométriques (MG) et d’apprentissage automatiques (AU). La première étude (Chapitre 2) examine les conditions dans lesquelles la modélisation géométrique peut être utilisée pour réduire la dimensionnalité des données utilisées dans un projet d’apprentissage automatique. Quatre modèles géométriques ont été testés pour leur précision et leur rapidité de traitement : deux modèles polynomiaux (P) – polynômes de Zernike (PZ) et harmoniques sphériques (PHS) – et deux modèles de fonctions rationnelles (R) : fonctions rationnelles de Zernike (RZ) et fonctions rationnelles d’harmoniques sphériques (RSH). Il est connu que les modèles PHS et RZ sont plus précis que les modèles PZ pour un même nombre de coefficients (J), mais on ignore si les modèles PHS performent mieux que les modèles RZ, et si, de manière plus générale, les modèles SH sont plus précis que les modèles R, ou l’inverse. Et prenant en compte leur temps de traitement, est-ce que les modèles les plus précis demeurent les plus avantageux? Considérant des valeurs de J (nombre de coefficients du modèle) relativement basses pour respecter les contraintes de dimensionnalité propres aux taches d’apprentissage automatique, nous avons établi que les modèles HS (PHS et RHS) étaient tous deux plus précis que les modèles Z correspondants (PZ et RR), et que l’avantage de précision conféré par les modèles HS était plus important que celui octroyé par les modèles R. Par ailleurs, les courbes de temps de traitement en fonction de J démontrent qu’alors que les modèles P sont traités en temps quasi-linéaires, les modèles R le sont en temps polynomiaux. Ainsi, le modèle SHR est le plus précis, mais aussi le plus lent (un problème qui peut en partie être remédié en appliquant une procédure de pré-optimisation). Le modèle ZP était de loin le plus rapide, et il demeure une option intéressante pour le développement de projets. SHP constitue le meilleur compromis entre la précision et la rapidité. La classification des cornées selon des paramètres cliniques a une longue tradition, mais la visualisation des effets moyens de ces paramètres sur la forme de la cornée par des cartes topographiques est plus récente. Dans la seconde étude (Chapitre 3), nous avons construit un atlas de cartes d’élévations moyennes pour différentes variables cliniques qui pourrait s’avérer utile pour l’évaluation et l’interprétation des données d’entrée (bases de données) et de sortie (prédictions, clusters, etc.) dans des tâches d’apprentissage automatique, entre autres. Une base de données constituée de plusieurs milliers de surfaces cornéennes antérieures normales enregistrées sous forme de matrices d’élévation de 101 by 101 points a d’abord été traitée par modélisation géométrique pour réduire sa dimensionnalité à un nombre de coefficients optimal dans une optique d’apprentissage automatique. Les surfaces ainsi modélisées ont été regroupées en fonction de variables cliniques de forme, de réfraction et de démographie. Puis, pour chaque groupe de chaque variable clinique, une surface moyenne a été calculée et représentée sous forme de carte d’élévations faisant référence à sa SMA (sphère la mieux ajustée). Après avoir validé la conformité de la base de donnée avec la littérature par des tests statistiques (ANOVA), l’atlas a été vérifié cliniquement en examinant si les transformations de formes cornéennes présentées dans les cartes pour chaque variable étaient conformes à la littérature. C’était le cas. Les applications possibles d’un tel atlas sont discutées. La troisième étude (Chapitre 4) traite de la classification non-supervisée (clustering) de surfaces cornéennes antérieures normales. Le clustering cornéen un domaine récent en ophtalmologie. La plupart des études font appel aux techniques d’extraction des caractéristiques pour réduire la dimensionnalité de la base de données cornéennes. Le but est généralement d’automatiser le processus de diagnostique cornéen, en particulier en ce qui a trait à la distinction entre les cornées normales et les cornées irrégulières (kératocones, Fuch, etc.), et dans certains cas, de distinguer différentes sous-classes de cornées irrégulières. L’étude de clustering proposée ici se concentre plutôt sur les cornées normales afin de mettre en relief leurs regroupements naturels. Elle a recours à la modélisation géométrique pour réduire la dimensionnalité de la base de données, utilisant des polynômes de Zernike, connus pour leur interprétativité transparente (chaque terme polynomial est associé à une caractéristique cornéenne particulière) et leur bonne précision pour les cornées normales. Des méthodes de différents types ont été testées lors de prétests (méthodes de clustering dur (hard) ou souple (soft), linéaires or non-linéaires. Ces méthodes ont été testées sur des surfaces modélisées naturelles (non-normalisées) ou normalisées avec ou sans traitement d’extraction de traits, à l’aide de différents outils d’évaluation (scores de séparabilité et d’homogénéité, représentations par cluster des coefficients de modélisation et des surfaces modélisées, comparaisons statistiques des clusters sur différents paramètres cliniques). Les résultats obtenus par la meilleure méthode identifiée, k-means sans extraction de traits, montrent que les clusters produits à partir de surfaces cornéennes naturelles se distinguent essentiellement en fonction de la courbure de la cornée, alors que ceux produits à partir de surfaces normalisées se distinguent en fonction de l’axe cornéen. La dernière étude présentée dans cette thèse (Chapitre 5) explore différentes techniques d’apprentissage automatique pour prédire la forme de la cornée à partir de données cliniques. La base de données cornéennes a d’abord été traitée par modélisation géométrique (polynômes de Zernike) pour réduire sa dimensionnalité à de courts vecteurs de 12 à 20 coefficients, une fourchette de valeurs potentiellement optimales pour effectuer de bonnes prédictions selon des prétests. Différentes méthodes de régression non-linéaires, tirées de la bibliothèque scikit-learn, ont été testées, incluant gradient boosting, Gaussian process, kernel ridge, random forest, k-nearest neighbors, bagging, et multi-layer perceptron. Les prédicteurs proviennent des variables cliniques disponibles dans la base de données, incluant des variables géométriques (diamètre horizontal de la cornée, profondeur de la chambre cornéenne, côté de l’œil), des variables de réfraction (cylindre, sphère et axe) et des variables démographiques (âge, genre). Un test de régression a été effectué pour chaque modèle de régression, défini comme la sélection d’une des 256 combinaisons possibles de variables cliniques (les prédicteurs), d’une méthode de régression, et d’un vecteur de coefficients de Zernike d’une certaine taille (entre 12 et 20 coefficients, les cibles). Tous les modèles de régression testés ont été évalués à l’aide de score de RMSE établissant la distance entre les surfaces cornéennes prédites (les prédictions) et vraies (les topographies corn¬éennes brutes). Les meilleurs d’entre eux ont été validés sur l’ensemble de données randomisé 20 fois pour déterminer avec plus de précision lequel d’entre eux est le plus performant. Il s’agit de gradient boosting utilisant toutes les variables cliniques comme prédicteurs et 16 coefficients de Zernike comme cibles. Les prédictions de ce modèle ont été évaluées qualitativement à l’aide d’un atlas de cartes d’élévations moyennes élaborées à partir des variables cliniques ayant servi de prédicteurs, qui permet de visualiser les transformations moyennes d’en groupe à l’autre pour chaque variables. Cet atlas a permis d’établir que les cornées prédites moyennes sont remarquablement similaires aux vraies cornées moyennes pour toutes les variables cliniques à l’étude. / This thesis aims to investigate the best conditions in which the anterior corneal surface of normal corneas can be preprocessed, classified and predicted using geometric modeling (GM) and machine learning (ML) techniques. The focus is on the anterior corneal surface, which is the main responsible of the refractive power of the cornea. Dealing with preprocessing, the first study (Chapter 2) examines the conditions in which GM can best be applied to reduce the dimensionality of a dataset of corneal surfaces to be used in ML projects. Four types of geometric models of corneal shape were tested regarding their accuracy and processing time: two polynomial (P) models – Zernike polynomial (ZP) and spherical harmonic polynomial (SHP) models – and two corresponding rational function (R) models – Zernike rational function (ZR) and spherical harmonic rational function (SHR) models. SHP and ZR are both known to be more accurate than ZP as corneal shape models for the same number of coefficients, but which type of model is the most accurate between SHP and ZR? And is an SHR model, which is both an SH model and an R model, even more accurate? Also, does modeling accuracy comes at the cost of the processing time, an important issue for testing large datasets as required in ML projects? Focusing on low J values (number of model coefficients) to address these issues in consideration of dimensionality constraints that apply in ML tasks, it was found, based on a number of evaluation tools, that SH models were both more accurate than their Z counterparts, that R models were both more accurate than their P counterparts and that the SH advantage was more important than the R advantage. Processing time curves as a function of J showed that P models were processed in quasilinear time, R models in polynomial time, and that Z models were fastest than SH models. Therefore, while SHR was the most accurate geometric model, it was the slowest (a problem that can partly be remedied by applying a preoptimization procedure). ZP was the fastest model, and with normal corneas, it remains an interesting option for testing and development, especially for clustering tasks due to its transparent interpretability. The best compromise between accuracy and speed for ML preprocessing is SHP. The classification of corneal shapes with clinical parameters has a long tradition, but the visualization of their effects on the corneal shape with group maps (average elevation maps, standard deviation maps, average difference maps, etc.) is relatively recent. In the second study (Chapter 3), we constructed an atlas of average elevation maps for different clinical variables (including geometric, refraction and demographic variables) that can be instrumental in the evaluation of ML task inputs (datasets) and outputs (predictions, clusters, etc.). A large dataset of normal adult anterior corneal surface topographies recorded in the form of 101×101 elevation matrices was first preprocessed by geometric modeling to reduce the dimensionality of the dataset to a small number of Zernike coefficients found to be optimal for ML tasks. The modeled corneal surfaces of the dataset were then grouped in accordance with the clinical variables available in the dataset transformed into categorical variables. An average elevation map was constructed for each group of corneal surfaces of each clinical variable in their natural (non-normalized) state and in their normalized state by averaging their modeling coefficients to get an average surface and by representing this average surface in reference to the best-fit sphere in a topographic elevation map. To validate the atlas thus constructed in both its natural and normalized modalities, ANOVA tests were conducted for each clinical variable of the dataset to verify their statistical consistency with the literature before verifying whether the corneal shape transformations displayed in the maps were themselves visually consistent. This was the case. The possible uses of such an atlas are discussed. The third study (Chapter 4) is concerned with the use of a dataset of geometrically modeled corneal surfaces in an ML task of clustering. The unsupervised classification of corneal surfaces is recent in ophthalmology. Most of the few existing studies on corneal clustering resort to feature extraction (as opposed to geometric modeling) to achieve the dimensionality reduction of the dataset. The goal is usually to automate the process of corneal diagnosis, for instance by distinguishing irregular corneal surfaces (keratoconus, Fuch, etc.) from normal surfaces and, in some cases, by classifying irregular surfaces into subtypes. Complementary to these corneal clustering studies, the proposed study resorts mainly to geometric modeling to achieve dimensionality reduction and focuses on normal adult corneas in an attempt to identify their natural groupings, possibly in combination with feature extraction methods. Geometric modeling was based on Zernike polynomials, known for their interpretative transparency and sufficiently accurate for normal corneas. Different types of clustering methods were evaluated in pretests to identify the most effective at producing neatly delimitated clusters that are clearly interpretable. Their evaluation was based on clustering scores (to identify the best number of clusters), polar charts and scatter plots (to visualize the modeling coefficients involved in each cluster), average elevation maps and average profile cuts (to visualize the average corneal surface of each cluster), and statistical cluster comparisons on different clinical parameters (to validate the findings in reference to the clinical literature). K-means, applied to geometrically modeled surfaces without feature extraction, produced the best clusters, both for natural and normalized surfaces. While the clusters produced with natural corneal surfaces were based on the corneal curvature, those produced with normalized surfaces were based on the corneal axis. In each case, the best number of clusters was four. The importance of curvature and axis as grouping criteria in corneal data distribution is discussed. The fourth study presented in this thesis (Chapter 5) explores the ML paradigm to verify whether accurate predictions of normal corneal shapes can be made from clinical data, and how. The database of normal adult corneal surfaces was first preprocessed by geometric modeling to reduce its dimensionality into short vectors of 12 to 20 Zernike coefficients, found to be in the range of appropriate numbers to achieve optimal predictions. The nonlinear regression methods examined from the scikit-learn library were gradient boosting, Gaussian process, kernel ridge, random forest, k-nearest neighbors, bagging, and multilayer perceptron. The predictors were based on the clinical variables available in the database, including geometric variables (best-fit sphere radius, white-towhite diameter, anterior chamber depth, corneal side), refraction variables (sphere, cylinder, axis) and demographic variables (age, gender). Each possible combination of regression method, set of clinical variables (used as predictors) and number of Zernike coefficients (used as targets) defined a regression model in a prediction test. All the regression models were evaluated based on their mean RMSE score (establishing the distance between the predicted corneal surfaces and the raw topographic true surfaces). The best model identified was further qualitatively assessed based on an atlas of predicted and true average elevation maps by which the predicted surfaces could be visually compared to the true surfaces on each of the clinical variables used as predictors. It was found that the best regression model was gradient boosting using all available clinical variables as predictors and 16 Zernike coefficients as targets. The most explicative predictor was the best-fit sphere radius, followed by the side and refractive variables. The average elevation maps of the true anterior corneal surfaces and the predicted surfaces based on this model were remarkably similar for each clinical variable.

<p dir="ltr">Digital soil mapping (DSM) combines field and laboratory data with environmental factors to predict soil properties. The accuracy of these predictions depends on factors such as model selection, data quality and quantity, and landscape characteristics. In our study, we investigated the impact of sample density and the use of various environmental covariates (ECs) including slope, topographic position index, topographic wetness index, multiresolution valley bottom flatness, and multiresolution ridge top flatness, as well as the spatial resolution of these ECs on the predictive accuracy of four predictive models; Cubist (CB), Random Forest (RF), Regression Kriging (RK), and Ordinary Kriging (OK). Our analysis was conducted at three sites in Indiana: the Purdue Agronomy Center for Research and Education (ACRE), Davis Purdue Agriculture Center (DPAC), and Southeast Purdue Agricultural Center (SEPAC). Each site had its unique soil data sampling designs, management practices, and topographic conditions. The primary focus of this study was to predict the spatial distribution of soil properties, including soil organic matter (SOM), cation exchange capacity (CEC), and clay content, at different depths (0-10cm, 0-15cm, and 10-30cm) by utilizing five environmental covariates and four spatial resolutions for the ECs (1-1.5 m, 5 m, 10 m, and 30 m).</p><p dir="ltr">Various evaluation metrics, including R², root mean square error (RMSE), mean square error (MSE), concordance coefficient (pc), and bias, were used to assess prediction accuracy. Notably, the accuracy of predictions was found to be significantly influenced by the site, sample density, model type, soil property, and their interactions. Sites exhibited the largest source of variation, followed by sampling density and model type for predicted SOM, CEC, and clay spatial distribution across the landscape.</p><p dir="ltr">The study revealed that the RF model consistently outperformed other models, while OK performed poorly across all sites and properties as it only relies on interpolating between the points without incorporating the landscape characteristics (ECs) in the algorithm. Increasing sample density improved predictions up to a certain threshold (e.g., 66 samples at ACRE for both SOM and CEC; 58 samples for SOM and 68 samples for CEC at SEPAC), beyond which the improvements were marginal. Additionally, the study highlighted the importance of spatial resolution, with finer resolutions resulting in better prediction accuracy, especially for SOM and clay content. Overall, comparing data from the two depths (0-10cm vs 10-30cm) for soil properties predications, deeper soil layer data (10-30cm) provided more accurate predictions for SOM and clay while shallower depth data (0-10cm) provided more accurate predictions for CEC. Finally, higher spatial resolution of ECs such as 1-1.5 m and 5 m contributed to more accurate soil properties predictions compared to the coarser data of 10 m and 30 m resolutions.</p><p dir="ltr">In summary, this research underscores the significance of informed decisions regarding sample density, model selection, and spatial resolution in digital soil mapping. It emphasizes that the choice of predictive model is critical, with RF consistently delivering superior performance. These findings have important implications for land management and sustainable land use practices, particularly in heterogeneous landscapes and areas with varying management intensities.</p>

Die Höhe des optimalen zerebralen Perfusionsdruckes nach schwerem Schädel-Hirn-Trauma wird kontrovers diskutiert. Während im sogenannten Lund-Konzept ein niedriger Perfusionsdruck angestrebt und die Gabe von Katecholaminen aufgrund potentieller zerebraler vasokonstringierender und weiterer Nebeneffekte vermieden wird, befürwortet das CPP-Konzept nach Rosner eine Anhebung des zerebralen Perfusionsdruckes, wenn notwendig unter intravenöser Gabe von Katecholaminen. Vor diesem Hintergrund galt es, in einem experimentellen Schädel-Hirn-Trauma- Modell der Ratte (Controlled Cortical Impact Injury) den Bereich des optimalen zerebralen Perfusionsdruckes nach traumatischer Hirnkontusion zu ermitteln und den Effekt von Katecholaminen auf den posttraumatischen zerebralen Blutfluss und die Entwicklung des sekundären Hirnschadens zu untersuchen. Die wesentlichen Ergebnisse dieser Arbeit lassen sich wie folgt zusammenfassen: In der Akutphase nach Hirnkontusion liegt der Bereich des zerebralen Perfusionsdruckes, welcher die Entwicklung des Kontusionsvolumens nicht beeinflusst, zwischen 70 und 105 mm Hg. Eine Senkung des Perfusionsdruckes unterhalb bzw. Anhebung oberhalb dieser Schwellenwerte vergrößert das Kontusionsvolumen. Die Anhebung des Blutdruckes mittels intravenöser Infusion von Dopamin oder Noradrenalin führt sowohl in der Frühphase als auch in der Spätphase nach Trauma (4 Stunden bzw. 24 Stunden nach kortikaler Kontusion) zu einem signifikanten Anstieg im kortikalen perikontusionellen Blutfluss und in der Hirngewebe-Oxygenierung. Die durch Anhebung des zerebralen Perfusionsdruckes auf über 70 mm Hg induzierte Verbesserung des posttraumatischen zerebralen Blutflusses bewirkte jedoch keine Reduzierung der Hirnschwellung. Für eine Katecholamin-induzierte zerebrale Vasokonstriktion nach kortikaler Kontusion gibt es keinen Anhalt. Um die Entwicklung des sekundären Hirnschadens nach kortikaler Kontusion zu minimieren, sollte der zerebrale Perfusionsdruck nach traumatischem Hirnschaden nicht unterhalb 70 mm Hg liegen. Eine Anhebung des Perfusionsdruckes auf über 70 mm Hg erscheint nicht notwendig oder vorteilhaft zu sein. Wenn notwendig, kann sowohl in der Früh- als auch Spätphase nach Trauma der zerebrale Perfusionsdruck mittels intravenöser Gabe von Katecholaminen angehoben werden. / The optimum cerebral perfusion pressure after severe traumatic brain injury remains to be controversial. In the Lund concept a relatively low cerebral perfusion pressure is preferred, and administration of catecholamines is avoided due to potential catecholamine-mediated cerebral vasoconstriction and other side effects. In contrast, the CPP concept of Rosner recommends elevation of cerebral perfusion pressure, if needed by intravenous administration of catecholamines. Based on this, in an experimental model of traumatic brain injury of the rat (Controlled Cortical Impact Injury) the optimum range of cerebral perfusion pressure after traumatic brain contusion and the effects of catecholamines on posttraumatic cerebral perfusion and development of secondary brain injury were investigated. The most significant results can be summarized as follows: In the acute phase after brain contusion the range of cerebral perfusion pressure that does not affect the development of posttraumatic contusion volume was found to be between 70 and 105 mm Hg. Reduction of the cerebral perfusion pressure below or elevation above these thresholds increases contusion volume. Elevation of blood pressure by intravenous infusion of dopamine or norepinephrine during the early (4 hours) as well as late (24 hours) phase after trauma results in a significant increase in pericontusional blood flow and brain tissue oxygenation. The increase in cerebral blood flow by elevating cerebral perfusion pressure above 70 mm Hg did not decrease cerebral edema formation. There was no evidence of a catecholamine-induced cerebral vasoconstriction after cortical contusion. In order to minimize secondary brain injury after cortical contusion, cerebral perfusion pressure should not fall bellow 70 mm Hg. However, a further active elevation of cerebral perfusion pressure does not appear necessary or beneficial. If needed cerebral perfusion pressure can be elevated by administration of catecholamines in the early as well late phase after trauma.