The need for improved management of the subsurface

The subsurface is used intensively to support economic stability and growth. Human interaction with the shallow subsurface ranges from exploitation of resources, accommodation of utilities, harnessing of energy (ground source heat pumps) and storage of waste. Current practice of managing these shallow subsurface zones is far from ideal. Many subsurface interventions are preceded by feasibility studies, predictive models or investigative measures to mitigate risks or predict the impacts of the work. However, the complex interactions between the anthropogenic structures and natural processes mean that a holistic impact assessment is often not achievable. By integrating these subsurface infrastructures within three dimensional framework models, a comprehensive assessment of the potential hazards in these shallow subsurface environments may be made. Some Geological Survey Organizations (GSOs) are currently developing subsurface management systems that will aid decision making in the shallow subsurface [1]. The British Geological Survey (BGS) is developing an open Environmental Modeling Platform [2] to provide the data standards and applications to link models, numerical simulations and ultimately socio-economic models so as to generate predictive responses to questions concerning sustainable us of the subsurface

EU Simulations and Engagement: Motivating Greater Interest in European Union Politics

While the effects of simulation based courses on the knowledge of participating students may be marginal in relation to standard lecture and discussion-based courses, this paper argues that the greatest leverage is gained by increasing participating students’ level of interest in the subject of study and in politics more broadly. Participants tend to become increasingly absorbed in their roles and in the politics of the institutions at the center of the simulation. To better consider this possibility, we conduct a survey of students participating in the 2015 Mid-Atlantic European Union Simulation and of appropriate control populations. The survey results indeed suggest that, much more than simply acquiring knowledge about the EU, the simulation experience serves to generate more robust interest in the subject of study

Relationship between shear energy input and sedimentation properties of exopolysaccharide-producing StreptococcusStreptococcus thermophilusthermophilus strains

separation of the bacteria cells. This separation is most commonly carried out with disc stack separators and needs to be adjusted to the respective strain to obtain a high cell recovery rate. Exopolysaccharides (EPS) produced by several starter cultures, however, have a large negative impact on the separation properties of the cells. These EPS can be divided into cell-bound capsular EPS or free EPS that are released into the surrounding fermentation medium. To improve the separation step, shear forces were applied after fermentation with a gear ring disperser to simulate the impact of a homogenizer and the influence on the separation properties of six Streptococcus thermophilus strains was examined. In case of capsular EPS, the sedimentation velocity of the bacteria increased due to shearing off the capsular EPS layer. Shearing media with free EPS resulted in a viscosity decrease and, hence, in a higher sedimentation velocity, as was determined using a disc centrifuge and a LUMiSizer. Sediment compression as measured with the LUMiSizer was also affected by the shearing step. The results of this study suggest that a defined shear treatment of EPS producing bacterial starter cultures leads to improved separation properties and, hence, higher bacteria yields. We assume that both EPS types affect separation efficiency of the bacteria cells, free EPS because of increased media viscosity and capsular EPS because they act like a friction pad

Caractérisation de filtrabilité par la filtration centrifuge – CEFU

Il existe une grande diversité des techniques pour la mesure de la filtrabilité de suspensions. Cependant, les techniques existantes ne sont pas adaptées à la caractérisation rapide d’un grand nombre de petits échantillons (surtout, des échantillons liquides).Depuis quelques années l’Université Technologique de Compiègne et la société LUM GmbH travaillent sur le développement de la centrifugation analytique pour la caractérisation accélérée de la filtrabilité des suspensions et dispersions. La méthode consiste à réaliser des essais de filtration à l’aide de la centrifugation analytique puis à analyser les courbes de la filtration centrifuge obtenues pour en extraire des propriétés des solutions.La comparaison simple des courbes de filtration obtenues permet de classifier les échantillons selon leur filtrabilité. De plus, l’analyse des courbes de filtration permet la caractérisation quantitative de la filtrabilité : détermination de la résistance de la membrane colmatée, de la résistance spécifique du gâteau et des propriétés de réversibilité de la compression du gâteau. La méthode d’analyse des données dépend de la nature des échantillons : deux méthodes adaptées pour des suspensions concentrées et des solutions des colloïdes sont validées actuellement .Le congrès MemPro6 sera l’occasion de présenter les résultats les plus récents sur l’ultra- et la microfiltration centrifuge pour la caractérisation de la filtrabilité des solution

Biofilm Forming Antibiotic Resistant Gram-Positive Pathogens Isolated From Surfaces on the International Space Station

The International Space Station (ISS) is a closed habitat in a uniquely extreme and hostile environment. Due to these special conditions, the human microflora can undergo unusual changes and may represent health risks for the crew. To address this problem, we investigated the antimicrobial activity of AGXX®, a novel surface coating consisting of micro-galvanic elements of silver and ruthenium along with examining the activity of a conventional silver coating. The antimicrobial materials were exposed on the ISS for 6, 12, and 19 months each at a place frequently visited by the crew. Bacteria that survived on the antimicrobial coatings [AGXX® and silver (Ag)] or the uncoated stainless steel carrier (V2A, control material) were recovered, phylogenetically affiliated and characterized in terms of antibiotic resistance (phenotype and genotype), plasmid content, biofilm formation capacity and antibiotic resistance transferability. On all three materials, surviving bacteria were dominated by Gram-positive bacteria and among those by Staphylococcus, Bacillus and Enterococcus spp. The novel antimicrobial surface coating proved to be highly effective. The conventional Ag coating showed only little antimicrobial activity. Microbial diversity increased with increasing exposure time on all three materials. The number of recovered bacteria decreased significantly from V2A to V2A-Ag to AGXX®. After 6 months exposure on the ISS no bacteria were recovered from AGXX®, after 12 months nine and after 19 months three isolates were obtained. Most Gram-positive pathogenic isolates were multidrug resistant (resistant to more than three antibiotics). Sulfamethoxazole, erythromycin and ampicillin resistance were most prevalent. An Enterococcus faecalis strain recovered from V2A steel after 12 months exposure exhibited the highest number of resistances (n = 9). The most prevalent resistance genes were ermC (erythromycin resistance) and tetK (tetracycline resistance). Average transfer frequency of erythromycin, tetracycline and gentamicin resistance from selected ISS isolates was 10−5 transconjugants/recipient. Most importantly, no serious human pathogens such as methicillin resistant Staphylococcus aureus (MRSA) or vancomycin-resistant Enterococci (VRE) were found on any surface. Thus, the infection risk for the crew is low, especially when antimicrobial surfaces such as AGXX® are applied to surfaces prone to microbial contamination

Numerical and experimental analysis of the sedimentation of spherical colloidal suspensions under centrifugal force

Understanding the sedimentation behaviour of colloidal suspensions is crucial in determining their stability. Since sedimentation rates are often very slow, centrifugation is used to expedite sedimentation experiments. The effect of centrifugal acceleration on sedimentation behaviour is not fully understood. Furthermore, in sedimentation models, interparticle interactions are usually omitted by using the hard-sphere assumption. This work proposes a one-dimensional model for sedimentation using an effective maximum volume fraction, with an extension for sedimentation under centrifugal force. A numerical implementation of the model using an adaptive finite difference solver is described. Experiments with silica suspensions are carried out using an analytical centrifuge. The model is shown to be a good fit with experimental data for 480 nm spherical silica, with the effects of centrifugation at 705 rpm studied. A conversion of data to Earth gravity conditions is proposed, which is shown to recover Earth gravity sedimentation rates well. This work suggests that the effective maximum volume fraction accurately captures interparticle interactions and provides insights into the effect of centrifugation on sedimentation

"Right-Wing Sentiment and European Integration"

The purpose of this paper is to explore the "anti-Europe" potential of the far right in five countries of Western Europe: Denmark, Germany, France, Belgium, and Italy. The conceptualization of the far right employed in this paper has two components: one focused on voting behavior and one on ideology (i.e., values and beliefs). It is important to keep both analytically distinct. Voting (behavior), in general, is critical because it determines the distribution of power in a democratic political system. The degree of success of radical parties therefore affects (and is effected by) the dynamics of competition in a political system, and it may also suggest something about the state of the democracy in it. But people often vote for a radical or extremist party even though they do not agree with its platform. Rather, they seek to "send a message" to the established elites (a phenomenon generally labeled "protest voting"). If one wants to know more about individual citizens' attitudes and motivations, one must study these more directly. This approach, which most often employs survey research, may also tell us much more about the underlying stability of a democracy, although this point often seems to be lost among students of extremism

Intracranial aneurysm rupture management: Comparing morphologic and deep learning features

Intracranial Aneurysms are a prevalent vascular pathology present in 3-4% of the population with an inherent risk of rupture. The growing accessibility of angiography has led to a rising incidence of detected aneurysms. An accurate assessment of the rupture risk is of utmost importance for the very high disability and mortality rates in case of rupture and the non-negligible risk inherent to surgical treatment. However, human evaluation is rather subjective, and current treatment guidelines, such as the PHASES score, remain inefficient. Therefore we aimed to develop an automatic machine learning-based rupture prediction model. Our study utilized 686 CTA scans, comprising 844 intracranial aneurysms. Among these aneurysms, 579 were classified as ruptured, while 265 were categorized as non-ruptured. Notably, the CTAs of ruptured aneurysms were obtained within a week after rupture, during which negligible morphological changes were observed compared to the aneurysm’s pre-rupture shape, as established by previous research. Based on this observation, our rupture risk assessment focused on the models’ ability to classify between ruptured and unruptured IAs. In our investigation, we implemented an automated vessel and aneurysm segmentation, vessel labeling, and feature extraction framework. The rupture risk prediction involved the use of deep learning-based vessel and aneurysm shape features, along with a combination of demographic features (patient sex and age) and morphological features (aneurysm location, size, surface area, volume, sphericity, etc.). An ablation-type study was conducted to evaluate these features. Eight different machine learning models were trained with the objective of identifying ruptured aneurysms. The best performing model achieved an area under the receiver operating characteristic curve (AUC) of 0.833, utilizing a random forest algorithm with feature selection based on Spearman’s rank correlation thresholding, which effectively eliminated highly correlated and anti-correlated features...:1 Introduction 1.1 Intracranial aneurysms 1.1.1 Treatment strategy 1.1.2 Rupture risk assesment 1.2 Artificial Intelligence 1.3 Thesis structure 1.4 Contribution of the author 2 Theory 2.1 Rupture risk assessment guidelines 2.1.1 PHASES score 2.1.2 ELAPSS score 2.2 Literature review: Aneurysm rupture prediction 2.3 Machine learning classifiers 2.3.1 Decision Tree 2.3.2 Random Forests 2.3.3 XGBoost 2.3.4 K-Nearest-Neighbor 2.3.5 Multilayer Perceptron 2.3.6 Logistic Regression 2.3.7 Support Vector Machine 2.3.8 Naive Bayes 2.4 Latent feature vectors in deep learning 2.5 PointNet++ 3 Methodology 3.1 Data 3.2 Vessel segmentation 3.3 Feature extraction 3.3.1 Deep vessel features 3.3.2 Deep aneurysm features 3.3.3 Conventional features 3.4 Rupture classification 3.4.1 Univariate approach 3.4.2 Multivariate approach 3.4.3 Deep learning approach 3.4.4 Deep learning amplified multivariate approach 3.5 Feature selection 3.5.1 Correlation-based feature selection 3.5.2 Permutation feature importance 3.6 Implementation 3.7 Evaluation 4 Results 4.1 Univariate approach 4.2 Multivariate approach 4.3 Deep learning approach 4.3.1 Deep vessel features 4.3.2 Deep aneurysm features 4.3.3 Deep vessel and deep aneurysm features 4.4 Deep learning amplified multivariate approach 4.4.1 Conventional and deep vessel features 4.4.2 Conventional and deep aneurysm features 4.4.3 Conventional, deep vessel, and deep aneurysm features 5 Discussion and Conclusions 5.1 Overview of results 5.2 Feature selection 5.3 Feature analysis 5.3.1 Deep vessel features 5.3.2 Deep aneurysm features 5.3.3 Conventional features 5.3.4 Summary 5.4 Comparison to other methods 5.5 Outlook BibliographyIntrakranielle Aneurysmen sind eine weit verbreitete vaskuläre Pathologie, die bei 3 bis 4% der Bevölkerung auftritt und ein inhärentes Rupturrisiko birgt. Mit der zunehmenden Verfügbarkeit von Angiographie wird eine steigende Anzahl von Aneurysmen entdeckt. Angesichts der sehr hohen permanenten Beeinträchtigungs- und Sterblichkeitsraten im Falle einer Ruptur und des nicht zu vernachlässigenden Risikos einer chirurgischen Behandlung ist eine genaue Bewertung des Rupturrisikos von größter Bedeutung. Die Beurteilung durch den Menschen ist jedoch sehr subjektiv, und die derzeitigen Behandlungsrichtlinien, wie der PHASES-Score, sind nach wie vor ineffizient. Daher wollten wir ein automatisches, auf maschinellem Lernen basierendes Modell zur Rupturvorhersage entwickeln. Für unsere Studie wurden 686 CTA-Scans von 844 intrakraniellen Aneurysmen verwendet, von denen 579 rupturiert waren und 265 nicht rupturiert waren. Dabei ist zu beachten, dass die CTAs der rupturierten Aneurysmen innerhalb einer Woche nach der Ruptur gewonnen wurden, in der im Vergleich zur Form des Aneurysmas vor der Ruptur nur geringfügige morphologische Veränderungen zu beobachten waren, wie in vorhergegangenen Studient festgestellt wurde. Im Rahmen unserer Untersuchung haben wir eine automatische Segmentierung von Adern und Aneurysmen, ein Aderlabeling und eine Merkmalsextraktion implementiert. Für die Vorhersage des Rupturrisikos wurden auf Deep Learning basierende Ader- und Aneurysmaformmerkmale zusammen mit einer Kombination aus demografischen Merkmalen (Geschlecht und Alter des Patienten) und morphologischen Merkmalen (u. A. Lage, Größe, Oberfläche, Volumen, Sphärizität des Aneurysmas) verwendet. Zur Bewertung dieser Merkmale wurde eine Ablationsstudie durchgeführt. Acht verschiedene maschinelle Lernmodelle wurden mit dem Ziel trainiert, rupturierte Aneurysmen zu erkennen...:1 Introduction 1.1 Intracranial aneurysms 1.1.1 Treatment strategy 1.1.2 Rupture risk assesment 1.2 Artificial Intelligence 1.3 Thesis structure 1.4 Contribution of the author 2 Theory 2.1 Rupture risk assessment guidelines 2.1.1 PHASES score 2.1.2 ELAPSS score 2.2 Literature review: Aneurysm rupture prediction 2.3 Machine learning classifiers 2.3.1 Decision Tree 2.3.2 Random Forests 2.3.3 XGBoost 2.3.4 K-Nearest-Neighbor 2.3.5 Multilayer Perceptron 2.3.6 Logistic Regression 2.3.7 Support Vector Machine 2.3.8 Naive Bayes 2.4 Latent feature vectors in deep learning 2.5 PointNet++ 3 Methodology 3.1 Data 3.2 Vessel segmentation 3.3 Feature extraction 3.3.1 Deep vessel features 3.3.2 Deep aneurysm features 3.3.3 Conventional features 3.4 Rupture classification 3.4.1 Univariate approach 3.4.2 Multivariate approach 3.4.3 Deep learning approach 3.4.4 Deep learning amplified multivariate approach 3.5 Feature selection 3.5.1 Correlation-based feature selection 3.5.2 Permutation feature importance 3.6 Implementation 3.7 Evaluation 4 Results 4.1 Univariate approach 4.2 Multivariate approach 4.3 Deep learning approach 4.3.1 Deep vessel features 4.3.2 Deep aneurysm features 4.3.3 Deep vessel and deep aneurysm features 4.4 Deep learning amplified multivariate approach 4.4.1 Conventional and deep vessel features 4.4.2 Conventional and deep aneurysm features 4.4.3 Conventional, deep vessel, and deep aneurysm features 5 Discussion and Conclusions 5.1 Overview of results 5.2 Feature selection 5.3 Feature analysis 5.3.1 Deep vessel features 5.3.2 Deep aneurysm features 5.3.3 Conventional features 5.3.4 Summary 5.4 Comparison to other methods 5.5 Outlook Bibliograph