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Concurrent operation of round beam and flat beam in a low-emittance storage ring
No full textIn 4th-generation storage rings, whether to operate the beam as round or flat is a critical question. A round beam has equal horizontal and vertical emittances, and is an efficient solution to reduce strong intra-beam scattering effects and lengthen the Touschek lifetimes, but a flat beam produces a brighter photon beam than a round beam. To provide both beams concurrently rather than bifurcating the beam time, this paper presents the exploitation of beam dynamics and the cutting-edge fast pulser that supports concurrent operation of round beam and flat beam
Versatile Cu-Based Layered Hydroxide Precursors for Efficient Electrochemical Nitrate Reduction Reaction Catalysts
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Bioelectrochemical and BioFET Sensor Readout Interface for Portable Self-Diagnostic Devices
No full textDeveloping a data-driven modeling framework for simulating a chemical accident in freshwater
No full textChemical accidents in freshwater pose threats to public health and aquatic ecosystems. Process-based models (PBMs) have been used to identify spatiotemporal chemical distributions in natural water. However, their computationally expensive simulations can hinder timely incident responses, which are crucial for minimizing negative impacts. Therefore, this study proposes a site-specific data-driven model (DDM) to supplement PBM-based chemical accident simulations. A convolutional neural network (CNN) was employed as the DDM because of its outstanding performance in capturing spatial patterns. Our model was developed to facilitate chemical accident simulations in the Namhan River, South Korea. The model datasets were generated using the PBM simulation outputs from toluene accident scenarios. Our DDM showed a Nash-Sutcliffe-efficiency of 0.94 and a root-mean-square-error of 0.023 mu g/L for the validation set. Its computational time was approximately 64 times faster than that of PBMs. In addition, this study interpreted the DDM results using SHapley Additive exPlanations (SHAP). The SHAP findings highlighted the influential role of distance from the accident site in this study. Overall, this study demonstrated the applicability of our modeling approach in freshwater chemical ac-cidents by providing rapid spatial distribution results complementing PBM simulations
Experimental systems for the analysis of mutational signatures: no 'one-size-fits-all' solution
No full textCells constantly accumulate mutations, which are caused by replication errors, as well as through the action of endogenous and exogenous DNA-damaging agents. Mutational patterns reflect the status of DNA repair machinery and the history of genotoxin exposure of a given cellular clone. Computationally derived mutational signatures can shed light on the origins of cancer. However, to understand the etiology of cancer signatures, they need to be compared with experimental signatures, which are obtained from the isogenic cell lines or organisms under controlled conditions. Experimental mutational patterns were instrumental in understanding the nature of signatures caused by mismatch repair and BRCA deficiencies. Here, we describe how different cell lines and model organisms were used in recent years to decipher mutational signatures observed in cancer genomes and provide examples of how data from different experimental systems complement and support each other
Autoencoders with exponential deviation loss for weakly supervised anomaly detection
No full textWeakly supervised anomaly detection aims to detect anomalies using a small number of labeled anomalies and a large amount of unlabeled data. However, existing methods have limitations: unsuitable setting of the anomaly threshold, using an inefficient loss function, and being vulnerable to contaminated data. To address these limitations, this paper proposes a novel framework called the Exponential Deviation Autoencoder (EDAE), which consists of two stages. In the first stage, EDAE pre-trains an autoencoder (AE) to learn a compressed representation of the input data and estimates the anomaly score distribution of the training data to determine an appropriate anomaly threshold. In the second stage, EDAE fine-tunes the AE with a novel Exponential Deviation Loss (EDL) function that provides continuous and nonlinear penalties according to anomaly scores and enables more effective training using labeled anomalies. EDAE also uses batch sampling based on empirical distribution to create batches of data that are more robust to contaminated data. We conduct extensive experiments on various datasets and show that EDAE outperforms state-of-the-art weakly supervised methods with up to a 26% improvement in accuracy. (c) 2023 Elsevier B.V. All rights reserved
