Relaxation dynamics of Fe55Cr10Mo14C15B6 metallic glass explored by mechanical spectroscopy and calorimetry measurements

In this work, the mechanical relaxation dynamics of Fe55Cr10Mo14C15B6 metallic glass is explored by mechanical spectroscopy. The temperature-dependent loss modulus E″(T) shows the features of β relaxation well below glass transition temperature Tg. This β relaxation can be well described in the framework of anelastic theory by a thermal activated process with activation energy of 165 kJ mol−1. Structural relaxation, also known as physical aging, has a large effect on the glass properties. The activation energy spectrum of structural relaxation is characterized by differential scanning calorimetry measuring the heat flow difference between as-quenched and relaxed states. The obtained energy spectrum is well described by a lognormal distribution with maximum probability activation energy of 176 kJ mol−1. The obtained activation energy of structural relaxation is similar to that of β relaxation observed from mechanical spectroscopy. Both values are also close to the Johari–Goldstein β relaxation estimated by the empirical rule Eβ = 26RTg.Peer ReviewedPostprint (author's final draft

Unsupervised Anomaly Detection in High-Dimensional Flight Data Using Convolutional Variational Auto-Encoder

The modern National Airspace System (NAS) is an extremely safe system and the aviation industry has experienced a steady decrease in fatalities over the years. This can be attributed to both improved flight critical systems with redundant hardware and software protections, as well as an increased focus on active monitoring and response to real time and historically identified vulnerabilities by implementing more resilient procedures and protocols. The main approach for identifying vulnerabilities in operations leverages domain expertise using knowledge about how the system should behave within the expected tolerances to known safety margins. This approach works well when the system has a well-defined operating condition. However, the operations in the NAS can be highly complex with various nuances that render it difficult to clearly pre-define all known safety vulnerabilities. With the advancement of data science and machine learning techniques, the potential to automatically identify emerging vulnerabilities in the observed operations has become more practical in recent years. The state-of-the-art anomaly detection approaches in aerospace data usually rely on supervised or semi-supervised learning. However, in many real-world problems such as flight safety, creating labels for the data requires huge amount of effort and is largely impractical. To address this challenge, we developed a Convolutional Variational Auto-Encoder (CVAE), which is an unsupervised learning approach for anomaly detection in high-dimensional heterogeneous time-series data. We validate performance of CVAE compared to the state-of-the-art supervised learning approach as well as unsupervised clustering-based approach using KMeans++ and kernel-based approach using One-Class Support Vector Machine (OC-SVM) on Yahoo!'s benchmark time series anomaly detection data. Finally, we showcase performance of CVAE on a case study of identifying anomalies in the first 60 seconds of commercial flights' take-offs using Flight Operational Quality Assurance (FOQA) data

Nucleolar targeting in an early-branching eukaryote suggests a general mechanism for ribosome protein sorting

The compartmentalised eukaryotic cell demands accurate targeting of proteins to the organelles in which they function, whether membrane-bound (like the nucleus) or non-membrane-bound (like the nucleolus). Nucleolar targeting relies on positively charged localisation signals and has received rejuvenated interest since the widespread recognition of liquid–liquid phase separation (LLPS) as a mechanism contributing to nucleolus formation. Here, we exploit a new genome-wide analysis of protein localisation in the early-branching eukaryote Trypanosoma brucei to analyse general nucleolar protein properties. T. brucei nucleolar proteins have similar properties to those in common model eukaryotes, specifically basic amino acids. Using protein truncations and addition of candidate targeting sequences to proteins, we show both homopolymer runs and distributed basic amino acids give nucleolar partition, further aided by a nuclear localisation signal (NLS). These findings are consistent with phase separation models of nucleolar formation and physical protein properties being a major contributing mechanism for eukaryotic nucleolar targeting, conserved from the last eukaryotic common ancestor. Importantly, cytoplasmic ribosome proteins, unlike mitochondrial ribosome proteins, have more basic residues – pointing to adaptation of physicochemical properties to assist segregation

RetinaVR: Democratizing Vitreoretinal Surgery Training with a Portable and Affordable Virtual Reality Simulator in the Metaverse

We developed and validated RetinaVR, an affordable and immersive virtual reality simulator for vitreoretinal surgery training, using the Meta Quest 2 VR headset. We focused on four core fundamental skills: core vitrectomy, peripheral shaving, membrane peeling, and endolaser application. The validation study involved 10 novice ophthalmology residents and 10 expert vitreoretinal surgeons. We demonstrated construct validity, as shown by the varying user performance in a way that correlates with experimental runs, age, sex, and expertise. RetinaVR shows promise as a portable and affordable simulator, with potential to democratize surgical simulation access, especially in developing countries