Background - The pursuit of automated methods to assess the extent of resection (EOR) in glioblastomas is challenging, requiring precise measurement of residual tumor volume. Many algorithms focus on preoperative scans, making them unsuitable for postoperative studies. Our objective was to develop a deep learning-based model for postoperative segmentation using magnetic resonance imaging (MRI). We ...

Drill cores from the Antarctic continental shelf are essential for directly constraining changes in past Antarctic Ice Sheet extent. Here, we provide a sedimentary facies analysis of drill cores from International Ocean Discovery Program (IODP) Site U1521 in the Ross Sea, which reveals a unique, detailed snapshot of Antarctic Ice Sheet evolution between ca. 18 Ma and 13 Ma. We identify distinct ...

Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence research and its translation into practice. However, increasing evidence shows that, particularly in image analysis, metrics are often chosen inadequately. Although taking into account the individual strengths, weaknesses and limitations of validation metrics is a critical ...

Diffusion models have obtained photo-realistic results on various super-resolution tasks. However, existing approaches typically require the availability of high-resolution and paired training data, which often is not readily available in many remote sensing scenarios. To enhance multi-spectral Sentinel 2 (S2) satellite images – at a ground sampling distance (GSD) ranging from 10m to 60m – without ...

Most integrated evanescent-field photonic sensors measure changes in either the real part or the imaginary part of the complex refractive index of the sample, i.e., refraction or absorption. Here we propose and experimentally demonstrate a near-infrared sensor based on a silicon nitride Mach-Zehnder interferometer which provides a direct measurement of the complex refractive index. Our architecture ...

Modern artificial intelligence (AI) technologies, led by machine learning (ML), have gained unprecedented momentum over the past decade. Following this wave of "AI summer," the network research community has also embraced AI/ML algorithms to address many problems related to network operations and management. However, compared to their counterparts in other domains, most ML-based solutions have yet ...

Extreme precipitation events can cause severe floods that pose significant risks to human lives, properties, and ecosystems. Therefore, understanding how climate change may affect the characteristics of these events is crucial for developing effective adaptation and mitigation strategies. In this study, we investigated the effect of climate change on the extreme characteristics through the concept ...

Nitroxides are a class of organic radicals that exhibit remarkable stability under ambient conditions due to kinetic, thermodynamic, and electronic factors. Their unpaired electron and stability render nitroxides valuable as probes and polarisation transfer agents in spectroscopy and imaging techniques. In particular, electron paramagnetic resonance (EPR) spectroscopy extensively utilizes nitroxides ...

This paper examines the equilibrium between user transaction fees and miner profitability within proof-of-work-based blockchains, specifically focusing on Bitcoin. We analyze the dependency of mining profit on factors such as transaction fee adjustments and operational costs, particularly electricity. By applying a multidimensional profitability model and performing a sensitivity analysis, we evaluate ...

Seabed gas and oil emissions appear as bubble plumes ascending through the water column in various environments. Understanding bubble characteristics—size, rise speed—is important for estimating escape rates of fluids like methane, oil, and carbon dioxide. However, measuring underwater gas bubbles is challenging, often requiring expensive specialized equipment. This study presents a novel methodology ...