Leveraging History to Predict Infrequent Abnormal Transfers in Distributed Workflows.

Scientific computing heavily relies on data shared by the community, especially in distributed data-intensive applications. This research focuses on predicting slow connections that create bottlenecks in distributed workflows. In this study, we analyze network traffic logs collected between January 2021 and August 2022 at the National Energy Research Scientific Computing Center (NERSC). Based on the observed patterns, we define a set of features primarily based on history for identifying low-performing data transfers. Typically, there are far fewer slow connections on well-maintained networks, which creates difficulty in learning to identify these abnormally slow connections from the normal ones. We devise several stratified sampling techniques to address the class-imbalance challenge and study how they affect the machine learning approaches. Our tests show that a relatively simple technique that undersamples the normal cases to balance the number of samples in two classes (normal and slow) is very effective for model training. This model predicts slow connections with an F1 score of 0.926.

Locating Partial Discharges in Power Transformers with Convolutional Iterative Filtering.

The most common source of transformer failure is in the insulation, and the most prevalent warning signal for insulation weakness is partial discharge (PD). Locating the positions of these partial discharges would help repair the transformer to prevent failures. This work investigates algorithms that could be deployed to locate the position of a PD event using data from ultra-high frequency (UHF) sensors inside the transformer. These algorithms typically proceed in two steps: first determining the signal arrival time, and then locating the position based on time differences. This paper reviews available methods for each task and then propose new algorithms: a convolutional iterative filter with thresholding (CIFT) to determine the signal arrival time and a reference table of travel times to resolve the source location. The effectiveness of these algorithms are tested with a set of laboratory-triggered PD events and two sets of simulated PD events inside transformers in production use. Tests show the new approach provides more accurate locations than the best-known data analysis algorithms, and the difference is particularly large, 3.7X, when the signal sources are far from sensors.

Bilateral Sequential Spontaneous Anterior Dislocated Intraocular Lens in a Patient With Retinitis Pigmentosa.

Retinitis pigmentosa is one of the risk factors for intraocular lens dislocation post cataract surgery which can lead to many complications. A 64-year-old Chinese female with bilateral pseudophakia and retinitis pigmentosa was referred for the continuation of care in 2009 with baseline visual acuity of hand movement bilaterally due to the retinitis pigmentosa. The cataract surgeries with posterior chamber intraocular lens (PCIOL) implantation in her early 50s were uneventful. In 2011, her right eye PCIOL dislocated anteriorly into the anterior chamber spontaneously and touched the cornea. It was complicated with bullous keratopathy and corneal decompensation. Intraocular pressure (IOP) was normal. PCIOL explantation, anterior vitrectomy and surgical peripheral iridotomy were performed. However, the cornea remained decompensated postoperatively. Her left eye was stable until 2019 when she developed acute angle closure secondary to complete anterior dislocation of PCIOL with pupillary block glaucoma. She underwent left eye PCIOL explantation, anterior vitrectomy and surgical peripheral iridotomy when IOP was optimised medically. Finally, both eyes were left aphakic due to poor prognosis with light perception (PL) vision, IOP was stable on single topical antiglaucoma and bilateral decompensated corneas were maintained with topical hypertonic saline. This case highlights the different serious sequelae of bilateral eyes in an unfortunate retinitis pigmentosa patient.