Привредно-политичко схварање jугоисточне Европе у Вајмарској Републици : (прилог расветљавању идеје „великог привредног простора" у Немачком Pajxy)

Am 10. November 1932 stellte der zuständige Beamte des Auswärtigen Amtes, Busse, anlässlich eines Reiseberichts von Vertretern der deutschen Gruppe im Europäischen Wirtschaftstag vom 2. November, in dem viel von den wirtschaftlichen Möglichkeiten Deutschlands in Jugoslawien und Rumänien, d.h. von ihrer Einbeziehung ins deutsche Wirtschaftsgebiet die Rede war, mit Recht fest : „Es ist aber darauf hinzuweisen, dass namentlich die deutsche Export industrie in jenen Ländern zum Teil schon lebhaft tä tig ist und dass ihre Interessen dort durch ständige reichsdeutsche und ein heimische Vertreter wahrgenommen werden. Als Neuland können die er wähnten Länder jedenfalls nicht angesprochen werden."Из анализе а) уверења кoja су између себе, у виду писама и меморандума, размењивали припадници немачких водећих кругова, из б) привредних потеза кoje су немачки привредници и веће и мање фирме, односно банке повлачили према земљама југоисточне Европе, из ц) књига и чланака ко)и су објављивани у немачкој јавности и из д) планова и практично предузетих корака немачке дипломатије, произлази да je у раздобљу Bajмapcкe Републике европоки Jyroисток схватан у оквиру једне целовите концепције

Migraine with aura detection and subtype classification using machine learning algorithms and morphometric magnetic resonance imaging data

IntroductionMigraine with aura (MwA) is a neurological condition manifested in moderate to severe headaches associated with transient visual and somatosensory symptoms, as well as higher cortical dysfunctions. Considering that about 5% of the world’s population suffers from this condition and manifestation could be abundant and characterized by various symptoms, it is of great importance to focus on finding new and advanced techniques for the detection of different phenotypes, which in turn, can allow better diagnosis, classification, and biomarker validation, resulting in tailored treatments of MwA patients.MethodsThis research aimed to test different machine learning techniques to distinguish healthy people from those suffering from MwA, as well as people with simple MwA and those experiencing complex MwA. Magnetic resonance imaging (MRI) post-processed data (cortical thickness, cortical surface area, cortical volume, cortical mean Gaussian curvature, and cortical folding index) was collected from 78 subjects [46 MwA patients (22 simple MwA and 24 complex MwA) and 32 healthy controls] with 340 different features used for the algorithm training.ResultsThe results show that an algorithm based on post-processed MRI data yields a high classification accuracy (97%) of MwA patients and precise distinction between simple MwA and complex MwA with an accuracy of 98%. Additionally, the sets of features relevant to the classification were identified. The feature importance ranking indicates the thickness of the left temporal pole, right lingual gyrus, and left pars opercularis as the most prominent markers for MwA classification, while the thickness of left pericalcarine gyrus and left pars opercularis are proposed as the two most important features for the simple and complex MwA classification.DiscussionThis method shows significant potential in the validation of MwA diagnosis and subtype classification, which can tackle and challenge the current treatments of MwA

On the image inpainting problem from the viewpoint of a nonlocal Cahn-Hilliard type equation

Motivated by the fact that the fractional Laplacean generates a wider choice of the interpolation curvesthan the Laplacean or bi-Laplacean, we propose a new non-local partial differential equation inspired bythe Cahn-Hilliard model for recovering damaged parts of an image. We also note that our model is linearand that the computational costs are lower than those for the standard Cahn-Hilliard equation, while theinpainting results remain of high quality. We develop a numerical scheme for solving the resulting equa-tions and provide an example of inpainting showing the potential of our method

The Effect of Inert Gas in the Mixture with Natural Gas on the Parameters of the Combustion Engine

The article discusses the influence of inert gases (carbon dioxide and nitrogen) in a mixture with natural gas on power and economic parameters of the internal combustion engine LGW 702 designed for micro-cogeneration units. The experimental measurements were made under various engine operating modes and under various compositions of fuel mixtures. The aim of the experiment was to analyze and assess the full impact of the inert components in the gaseous fuel, especially on the particular integral parameters, as well as on the internal combustion engine parameters relating to the course of burning the mixture. Experimental results indicate a decrease in performance parameters and an increase in specific fuel consumption with an increase in the proportion of internal gases in the mixture. Increasing proportion of inert gases leads to decreasing maximum pressure in the cylinder (a decrease approximately by 50% with the mixture CO2NG50 or by 30% with the mixture N2NG50, compared to natural gas) and the position of maximum pressure value is shifted further into the area of expansion stroke

Machine learning approach for Migraine Aura Complexity Score prediction based on magnetic resonance imaging data

Abstract Background Previous studies have developed the Migraine Aura Complexity Score (MACS) system. MACS shows great potential in studying the complexity of migraine with aura (MwA) pathophysiology especially when implemented in neuroimaging studies. The use of sophisticated machine learning (ML) algorithms, together with deep profiling of MwA, could bring new knowledge in this field. We aimed to test several ML algorithms to study the potential of structural cortical features for predicting the MACS and therefore gain a better insight into MwA pathophysiology. Methods The data set used in this research consists of 340 MRI features collected from 40 MwA patients. Average MACS score was obtained for each subject. Feature selection for ML models was performed using several approaches, including a correlation test and a wrapper feature selection methodology. Regression was performed with the Support Vector Machine (SVM), Linear Regression, and Radial Basis Function network. Results SVM achieved a 0.89 coefficient of determination score with a wrapper feature selection. The results suggest a set of cortical features, located mostly in the parietal and temporal lobes, that show changes in MwA patients depending on aura complexity. Conclusions The SVM algorithm demonstrated the best potential in average MACS prediction when using a wrapper feature selection methodology. The proposed method achieved promising results in determining MwA complexity, which can provide a basis for future MwA studies and the development of MwA diagnosis and treatment