This paper presents a series of protective relay applications that use peer-to-peer communications to transmit data among protective relays and other intelligent electronic devices (IEDs). Applications are selected from various categories such as transmission line, transformer, breaker, bus, substation, and distribution feeder.
Recent years have been witnessing a substantial improvement in the accuracy of skin cancer classification using convolutional neural networks (CNNs). CNNs perform on par with or better than dermatologists with respect to the classification tasks of single images. However, in clinical practice, dermatologists also use other patient data beyond the visual aspects present in a digitized image, further increasing their diagnostic accuracy. Several pilot studies have recently investigated the effects of integrating different subtypes of patient data into CNN-based skin cancer classifiers.
Power line carrier (PLC) is a popular method of communicating protective relaying information between line terminals of a protected power transmission line. This paper describes the results of a 1990 survey of utility engineers on their PLC equipment selection and applications, and PLC equipment maintenance practices and problems.< >
A parallel optical link, PAROLI(R), with over 15 Gbit/s throughput for both asynchronous and synchronous data transmission is described. The link consists of discrete transmitter and receiver modules and a low skew cable system. The PAROLI(R) system operates with 12 optical channels at data rates of up to 1.25 Gbit/s per channel. The oxide-confined VCSEL array, the pin diode array and the ICs are designed to operate at 3.3 Volts. Methods of system performance evaluation by eye diagram analysis and BER scan techniques are discussed. Improvements of key component features are presented, pointing towards 2.5 Gbit/s transmission per optical channel in the near future.
The aim of this study was to systematically review the literature to assess static fracture strength tests applied for FDPs and analyze the impact of periodontal ligament (PDL) simulation on the fracture strength. Original scientific papers published in MEDLINE (PubMed) database between 01/01/1981 and 01/06/2010 were included in this systematic review. Data were analyzed considering the test method (static loading), material type (metal-ceramic-MC, oxide all-ceramic-AC, fiber reinforced composite resin-FRC, composite resin-C), PDL (without or with) and restoration type (single crowns, 3-unit, 4-unit, inlay-retained and cantilever FDPs). The selection process resulted in the 72 studies. In total, 377 subgroups revealed results from static load-bearing capacity of different materials. Fourteen metal-ceramic, 190 AC, 121 FRC, 45 C resin groups were identified as subgroups. Slightly decreased results were observed with the presence of PDL for single crowns (without PDL=1117±215 N; with PDL=876±69 N), 3-unit FDPs (without PDL=791±116 N; with PDL=675±91 N) made of AC, 3-unit FDP (without PDL=1244±270 N; with PDL=930±76 N) and inlay-retained FDP (without PDL=848±104 N; with PDL=820±91 N) made of FRC and 4-unit FDPs (without PDL=548±26 N; with PDL=393±67 N) made of C. Overall, for single crowns, fracture strength of FRC was higher than that of AC and MC; for 3-unit FDPs FRC=C>AC=MC; for 4-unit FDPs AC>FRC>C and for inlay-retained FDPs, FRC=AC. An inclination for decreased static fracture strength could be observed with the simulation of PDL but due to insufficient data this could not be generalized for all materials used for FDPs.
Abstract Proteins are the main drivers of cell function and disease, making their analysis a powerful technique to characterize determinants of cell identity and to identify biomarkers. Current proteomic technology has the breadth to profile thousands of proteins and even the sensitivity to access single cells, however limitations in throughput restrict its application, e.g. not allowing classification of samples according to biological or clinical status in large sample cohorts. Therefore, we developed a deep learning-based approach for the analysis of mass spectrometric (MS) data, assigning proteomic profiles to sample identity. Specifically, we designed an architecture referred to as Proformer, and show that it is superior to convolutional neural network-driven architectures, is explainable, and demonstrates robustness towards batch-effects. Based on its tabular approach, we highlight the integration of all four dimensions of proteomic measurements (retention time, mass-to-charge, intensity and ion mobility), and demonstrate enhanced sample discrimination involving a treatment with IFN-γ, despite its subtle effect on the cell’s proteome. In addition, the Proformer is not restricted to proteomic depth, and can classify cells by cell type and their differentiation status even using single-cell proteomic data. Collectively, this work presents a novel deep learning-based model for rapid classification of proteomic data, with important future implications to enhance patient stratification, early detection and single-cell analysis.
Clinicians and pathologists traditionally use patient data in addition to clinical examination to support their diagnoses.We investigated whether a combination of histologic whole slides image (WSI) analysis based on convolutional neural networks (CNNs) and commonly available patient data (age, sex and anatomical site of the lesion) in a binary melanoma/nevus classification task could increase the performance compared with CNNs alone.We used 431 WSIs from two different laboratories and analysed the performance of classifiers that used the image or patient data individually or three common fusion techniques. Furthermore, we tested a naive combination of patient data and an image classifier: for cases interpreted as 'uncertain' (CNN output score <0.7), the decision of the CNN was replaced by the decision of the patient data classifier.The CNN on its own achieved the best performance (mean ± standard deviation of five individual runs) with AUROC of 92.30% ± 0.23% and balanced accuracy of 83.17% ± 0.38%. While the classification performance was not significantly improved in general by any of the tested fusions, naive strategy of replacing the image classifier with the patient data classifier on slides with low output scores improved balanced accuracy to 86.72% ± 0.36%.In most cases, the CNN on its own was so accurate that patient data integration did not provide any benefit. However, incorporating patient data for lesions that were classified by the CNN with low 'confidence' improved balanced accuracy.
