Proceedings of the 12th annual deep brain stimulation think tank: cutting edge technology meets novel applications.

The Deep Brain Stimulation (DBS) Think Tank XII was held on August 21st to 23rd. This year we showcased groundbreaking advancements in neuromodulation technology, focusing heavily on the novel uses of existing technology as well as next-generation technology. Our keynote speaker shared the vision of using neuro artificial intelligence to predict depression using brain electrophysiology. Innovative applications are currently being explored in stroke, disorders of consciousness, and sleep, while established treatments for movement disorders like Parkinsons disease are being refined with adaptive stimulation. Neuromodulation is solidifying its role in treating psychiatric disorders such as depression and obsessive-compulsive disorder, particularly for patients with treatment-resistant symptoms. We estimate that 300,000 leads have been implanted to date for neurologic and neuropsychiatric indications. Magnetoencephalography has provided insights into the post-DBS physiological changes. The field is also critically examining the ethical implications of implants, considering the long-term impacts on clinicians, patients, and manufacturers

Machine Learning based background correction for jet shapes in Pb-Pb collisions at 5.02 TeV

Jet shapes and, furthermore, jet substructure observables are of utmost interest for the field of heavy-ion physics. However, the overwhelmingly large background from soft processes complicates a measurement in particular for low jet transverse momenta: Both, the jet energy scale as well as the jet shape itself, are strongly affected by the background. While promising studies on the correction of the jet energy scale and first pilot studies on two-parameter regression (jet momentum/jet shape) were already made, detailed studies of the latter are yet missing. In this project, we evaluate the performance of several machine learning algorithms and combinations of input parameters on the background correction of the jet properties

CNN with residual learning extensions in neutrino high energy physics

Abstract As many reconstruction steps in neutrino high energy physics (HEP) are similar to image pattern recognition tasks, we explore the potential of Convolutional Neural Networks (CNN) combined with residual machine learning algorithm. Characteristic features from neutrino track image pixelmaps are extracted at different scales and these features are used for classification of the type of neutrino interaction. In this contribution, we sumarize observed performance of the residual neural networks (ResNet) for neutrino charged current (CC) interaction detections using image-like Monte Carlo simulated data for muon and electron neutrinos. The two topologies depicted at the neutrino detectors differ, muon neutrino CC interaction is dominated by a slowly ionizing muon, while electron neutrino CC interaction is usually recorded as a wide shower. For the ResNet performance evaluation, we use area under ROC curve (AUC) as the evaluation metric. We observe an improvement while using residual learning compared to general CNN architecture, which is caused by a more stable training with lesser vulnerability to the vanishing gradient of the ResNets. Moreover, stacking other hidden layers within our ResNet model greatly increased the AUC value on the test neutrino dataset without the signs of unstable training or overfitting.</jats:p

Effects of Bias-Corrected Regional Climate Projections and Their Spatial Resolutions on Crop Model Results under Different Climatic and Soil Conditions in Austria

The quality, reliability, and uncertainty of Austrian climate projections (ÖKS15) and their impacts on the results of the crop model DSSAT for three different orographic and climatic agricultural regions in Austria were analyzed. Cultivar-specific grain yields of winter wheat, spring barley, and maize were simulated for different soil classes to address three main objectives. First, the uncertainties of simulated crop yields related to the ÖKS15 projections were analyzed under current climate conditions. The climate projections revealed that the case study regions with higher humidity levels generally had lower yield deviations than the drier regions (yield deviations from −19% to +15%). Regarding the simulated crop types, spring barley was found to be less sensitive to the climate projections than rainfed maize, and the response was greater in regions with a low soil water storage capacity. The second objective was to simulate crop yields for the same cultivars using future climate projections. Winter wheat and spring barley tended to show increased yields by the end of the century due to an assumed CO2-fertilization effect in the range of 3–23%, especially under RCP 8.5. However, rainfed and irrigated maize were associated with up to 17% yield reductions in all three study regions due to a shortened growth period caused by warming. The third objective addressed the effects of crop model weather input data with different spatial resolutions (1 vs. 5, 11, and 21 km) on simulated crop yields using the climate projections. Irrigated grain maize and rainfed spring barley had the lowest simulated yield deviations between the spatial scales applied due to their better water supply conditions. The ranges of uncertainty revealed by the different analyses suggest that impact models should be tested with site representative conditions before being applied to develop site-specific adaptation options for Austrian crop production

Effects of Bias-Corrected Regional Climate Projections and Their Spatial Resolutions on Crop Model Results under Different Climatic and Soil Conditions in Austria

The quality, reliability, and uncertainty of Austrian climate projections (ÖKS15) and their impacts on the results of the crop model DSSAT for three different orographic and climatic agricultural regions in Austria were analyzed. Cultivar-specific grain yields of winter wheat, spring barley, and maize were simulated for different soil classes to address three main objectives. First, the uncertainties of simulated crop yields related to the ÖKS15 projections were analyzed under current climate conditions. The climate projections revealed that the case study regions with higher humidity levels generally had lower yield deviations than the drier regions (yield deviations from −19% to +15%). Regarding the simulated crop types, spring barley was found to be less sensitive to the climate projections than rainfed maize, and the response was greater in regions with a low soil water storage capacity. The second objective was to simulate crop yields for the same cultivars using future climate projections. Winter wheat and spring barley tended to show increased yields by the end of the century due to an assumed CO2-fertilization effect in the range of 3–23%, especially under RCP 8.5. However, rainfed and irrigated maize were associated with up to 17% yield reductions in all three study regions due to a shortened growth period caused by warming. The third objective addressed the effects of crop model weather input data with different spatial resolutions (1 vs. 5, 11, and 21 km) on simulated crop yields using the climate projections. Irrigated grain maize and rainfed spring barley had the lowest simulated yield deviations between the spatial scales applied due to their better water supply conditions. The ranges of uncertainty revealed by the different analyses suggest that impact models should be tested with site representative conditions before being applied to develop site-specific adaptation options for Austrian crop production.</jats:p

Reaktivita vnitřních a vnějších Bronstedových kyselých poloh v nanoporézních MFI: kinetická studie H/D výměny

The strength of Bronsted acid sites (BAS) affects the properties of 2D and hierarchical zeolites, but the relative contribution of internal and external BAS remains unknown. Accordingly, this study aims to assess the acidity of external and internal BAS in nanosponge-like MFI zeolites by comparatively analyzing hydrogen-deuterium exchange kinetics between zeolitic deuteroxyl groups and C2H6 molecules monitored by in-situ FTIR spectroscopy. For this purpose, (i) a sample pre-treatment procedure was specifically developed to deuterate only internal or only external acid sites using 2,6-di-tert-butylpyridine (DTBP) as a masking agent and (ii) DFT modeling of surface BAS was performed. Theoretical models of the thin MFI layer revealed that the external surface of MFI crystals contains three types of BAS: (i) BAS positioned in 5-membered rings, either shielded by silanol nests, rendering the site inaccessible for DTBP or yielding a very low adsorption energy for ethane, (ii) BAS pointing into the pores, due to the presence of aluminol, which hinders DTBP accessibility, or the BAS undergoes transformation to a three-coordinate aluminium site, and (iii) BAS accessible to both DTBP and ethane. The results from our kinetics measurements showed that H/D exchange at external BAS of nanosponge MFI zeolites is faster than at internal BAS (rate constants at 425 degrees C: 3.8 10(-3) vs. 2.4 10(-3) s(-1) for external and internal BAS, respectively), but this cannot be attributed to the effect of diffusion. Therefore, the differences in exchange kinetics between external and internal BAS are given by mutual interplay of subtle differences in the corresponding activation barriers (113 vs. 117 kJ/mol for external and internal BAS, respectively) and pre-exponential terms (1.09 10(6) vs. 1.45 10(6) s(-1) for external and internal BAS, respectively).Síla Bronstedových kyselých poloh ovlivňuje vlastnosti 2D a hierarchických zeolitů. Tato studie byla zaměřen na zhodnocení kyselosti vnitřních a vnějších BAS v nanoporézních MFI zeolitech pomocí komparativní analýzy H/D výměny mezi deuteroxylovou skupinou a C2H6 molekulami. Tato výměna byla monitorována pomocí in-situ FTIR spektroskopie. Výsledky byly porovnány s DFT výpočty modelujícími povrchové BAS