Pre-nuclear level of I-129 in Chinese loess-paleosol sections: A search for the natural I-129 level for dating in terrestrial environments

Due to its long half-life (15.7 Myr), radioactive I-129 has great potential for dating geologic materials as old as 100 Myr. Thus, knowing the natural level of I-129 is crucial to dating applications. The initial ratio of I-129/I-127 in the ocean has been quantified by a number of researchers who have reached a consensus value. However, the applicability of I-129 dating in the terrestrial environment remains problematic because the lack of an initial I-129/I-127 value. In this work, samples of loess-paleosol sections from the Chinese Loess Plateau (CLP) were analyzed for I-129/I-127, aiming to provide an Initial I-129/I-127 ratio that can be adopted for dating purposes in terrestrial environments. A value of (2.0 +/- 1.0) x 10(-11) for the I-129/I-127 ratio was found in two investigated loess-paleosol sections from Xifeng and Luochuan, China. This ratio is one order of magnitude higher than the initial value reported for the marine environment. Alteration of the natural initial I-129 In the investigated samples due to the downward migration of anthropogenic I-129 and by excess fissiogenic I-129 from uranium was not supported. Consequently, the I-129/I-127 ratio measured is considered to be a pristine value, and the difference from that In the marine systems is attributed to an Isotopic dilution effect. (C) 2018 Elsevier Ltd. All rights reserved

Meta-analysis of effectiveness of traditional Chinese medicine or its combination with Western medicine in the treatment of triple negative breast cancer

Purpose: To assess the efficacy and side effects of Traditional Chinese Medicine (TCM) in the management of triple negative breast cancer (TNBC). Methods: Full text data on randomized controlled trial (RCT) of TNBC treated with TCM or its combination with Western Medicine (WM) were retrieved from the Chinese biomedical literature database, Chinese periodicals, Chinese Science-Technology periodicals and VP and PubMed. The qualities of the RCTs were evaluated. Revman 5.3 was used to conduct the meta-analysis. Results: A total of 16 RCTs involving 1186 patients were included. Analysis of these RCTs showed significant differences in total effectiveness between WM and TCM or combination of TCM with WM {(OR = 2.63, 95 % CI = 1.37, 5.03), test of the combined effect (Z = 2.91, p ˂ 0.005)}. Conclusion: The results show that TCM is effective in the treatment TNB

Impact of North Korean nuclear weapons test on 3 September, 2017 on inland China traced by C-14 and I-129

Environmental impact of North Korea nuclear weapons testing on 3 Sept, 2017, is of key concern. In order to investigate whether there is radioactive leakage and whether it can be transported to inland China, C-14 and I-129 are determined in aerosol samples collected in a Chinese inland city before and after the test. Aerosol Delta C-14 values before and after the test do not show any significant difference. In contrast, a four-fold increase of I-129/I-127 ratios was found after the test. The possible sources of I-129 in these atmospheric samples and the impact of the North Korea nuclear test are discussed

Fossil Image Identification using Deep Learning Ensembles of Data Augmented Multiviews

Identification of fossil species is crucial to evolutionary studies. Recent advances from deep learning have shown promising prospects in fossil image identification. However, the quantity and quality of labeled fossil images are often limited due to fossil preservation, conditioned sampling, and expensive and inconsistent label annotation by domain experts, which pose great challenges to the training of deep learning based image classification models. To address these challenges, we follow the idea of the wisdom of crowds and propose a novel multiview ensemble framework, which collects multiple views of each fossil specimen image reflecting its different characteristics to train multiple base deep learning models and then makes final decisions via soft voting. We further develop OGS method that integrates original, gray, and skeleton views under this framework to demonstrate the effectiveness. Experimental results on the fusulinid fossil dataset over five deep learning based milestone models show that OGS using three base models consistently outperforms the baseline using a single base model, and the ablation study verifies the usefulness of each selected view. Besides, OGS obtains the superior or comparable performance compared to the method under well-known bagging framework. Moreover, as the available training data decreases, the proposed framework achieves more performance gains compared to the baseline. Furthermore, a consistency test with two human experts shows that OGS obtains the highest agreement with both the labels of dataset and the two experts. Notably, this methodology is designed for general fossil identification and it is expected to see applications on other fossil datasets. The results suggest the potential application when the quantity and quality of labeled data are particularly restricted, e.g., to identify rare fossil images.Comment: preprint submitted to Methods in Ecology and Evolutio

DLPFA: Deep Learning based Persistent Fault Analysis against Block Ciphers

Deep learning techniques have been widely applied to side-channel analysis (SCA) in recent years and shown better performance compared with traditional methods. However, there has been little research dealing with deep learning techniques in fault analysis to date. This article undertakes the first study to introduce deep learning techniques into fault analysis to perform key recovery. We investigate the application of multi-layer perceptron (MLP) and convolutional neural network (CNN) in persistent fault analysis (PFA) and propose deep learning-based persistent fault analysis (DLPFA). DLPFA is first applied to advanced encryption standard (AES) to verify its availability. Then, to push the study further, we extend DLPFA to PRESENT, which is a lightweight substitution–permutation network (SPN)-based block cipher. The experimental results show that DLPFA can handle random faults and provide outstanding performance with a suitable selection of hyper-parameters

Decentralized Funding of Public Goods in Blockchain System:Leveraging Expert Advice

Public goods projects, such as open-source technology, are essential for the blockchain ecosystem's growth. However, funding these projects effectively remains a critical issue within the ecosystem. Currently, the funding protocols for blockchain public goods lack professionalism and fail to learn from past experiences. To address this challenge, our research introduces a human oracle protocol involving public goods projects, experts, and funders. In our approach, funders contribute investments to a funding pool, while experts offer investment advice based on their expertise in public goods projects. The oracle's decisions on funding support are influenced by the reputations of the experts. Experts earn or lose reputation based on how well their project implementations align with their advice, with successful investments leading to higher reputations. Our oracle is designed to adapt to changing circumstances, such as experts exiting or entering the decision-making process. We also introduce a regret bound to gauge the oracle's effectiveness. Theoretically, we establish an upper regret bound for both static and dynamic models and demonstrate its closeness to an asymptotically equal lower bound. Empirically, we implement our protocol on a test chain and show that our oracle's investment decisions closely mirror optimal investments in hindsight

Decentralized Funding of Public Goods in Blockchain System:Leveraging Expert Advice

Public goods projects, such as open-source technology, are essential for the blockchain ecosystem's growth. However, funding these projects effectively remains a critical issue within the ecosystem. Currently, the funding protocols for blockchain public goods lack professionalism and fail to learn from past experiences. To address this challenge, our research introduces a human oracle protocol involving public goods projects, experts, and funders. In our approach, funders contribute investments to a funding pool, while experts offer investment advice based on their expertise in public goods projects. The oracle's decisions on funding support are influenced by the reputations of the experts. Experts earn or lose reputation based on how well their project implementations align with their advice, with successful investments leading to higher reputations. Our oracle is designed to adapt to changing circumstances, such as experts exiting or entering the decision-making process. We also introduce a regret bound to gauge the oracle's effectiveness. Theoretically, we establish an upper regret bound for both static and dynamic models and demonstrate its closeness to an asymptotically equal lower bound. Empirically, we implement our protocol on a test chain and show that our oracle's investment decisions closely mirror optimal investments in hindsight