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  1. During International Ocean Discovery Program (IODP) Expedition 359, Site U1471 was cored in the Maldives Inner Sea, where the sediments consist of hemipelagic carbonate drifts containing a mixture of components exported from the atolls and pelagic origin (periplatform ooze). The cores from this site provide a complete and uninterrupted record of the sedimentary and paleoceanographic changes in the Maldives Inner Sea from the Miocene through the Pleistocene. Here, we present the bulk sediment total organic carbon, total nitrogen, and calcium carbonate contents for the uppermost 21 m of the composite splice of Site U1471.
    Free, publicly-accessible full text available August 31, 2023
  2. Irfan Awan ; Muhammad Younas ; Jamal Bentahar ; Salima Benbernou (Ed.)
    Multi-site clinical trial systems face security challenges when streamlining information sharing while protecting patient privacy. In addition, patient enrollment, transparency, traceability, data integrity, and reporting in clinical trial systems are all critical aspects of maintaining data compliance. A Blockchain-based clinical trial framework has been proposed by lots of researchers and industrial companies recently, but its limitations of lack of data governance, limited confidentiality, and high communication overhead made data-sharing systems insecure and not efficient. We propose π–²π—ˆπ—π–Ύπ—‹π—‚π–Ί, a privacy-preserving smart contracts framework, to manage, share and analyze clinical trial data on fabric private chaincode (FPC). Compared to public Blockchain, fabric has fewer participants with an efficient consensus protocol. π–²π—ˆπ—π–Ύπ—‹π—‚π–Ί consists of several modules: patient consent and clinical trial approval management chaincode, secure execution for confidential data sharing, API Gateway, and decentralized data governance with adaptive threshold signature (ATS). We implemented two versions of π–²π—ˆπ—π–Ύπ—‹π—‚π–Ί with non-SGX deploys on AWS blockchain and SGX-based on a local data center. We evaluated the response time for all of the access endpoints on AWS Managed Blockchain, and demonstrated the utilization of SGX-based smart contracts for data sharing and analysis.
    Free, publicly-accessible full text available September 1, 2023
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  8. Vapor loss and molecular absorption make the transmission distance in sub-Terahertz bands a challenge, especially in mobile statues such as UAVs communication. The molecular absorption element is an essential part of the path loss in THz communication channel modeling that cannot be neglected. Along this direction, we investigated the UAV trajectories in sub-THz band. To maximize the secrecy rate of the UAVs communication, an optimization problem has been proposed to jointly optimize the trajectory and transmit power. To enhance the obtained average secrecy rate, MIMO communication and a cooperative UAV jammer strategy were used in this paper. Also, analysis and simulations results were presented to show the performance of UAV-ground communication at THz communications. Finally, Secrecy Outage Probability was obtained for each UAV trajectories in different flight periods to examine the performance of physical layer security added to the UAVground communication at sub-THz communication.
    Free, publicly-accessible full text available January 1, 2023
  9. Recently, using credit cards has been considered one of the essential things of our life due to its pros of being easy to use and flexible to pay. The critical impact of the increment of using credit cards is the occurrence of fraudulent transactions, which allow the illegal user to get money and free goods via unauthorized usage. Artificial Intelligence (AI) and Machine Learning (ML) have become effective techniques used in different applications to ensure cybersecurity. This paper proposes our fraud detection system called Man-Ensemble CCFD using an ensemble-learning model with two stages of classification and detection. Stage one, called ML-CCFD, utilizes ten machine learning (ML) algorithms to classify credit card transactions to class 1 as a fraudulent transaction or class 0 as a legitimate transaction. As a result, we compared their classification reports together, precisely precision, recall (sensitivity), and f1-score. Then, we selected the most accurate ML algorithms based on their classification performance and prediction accuracy. The second stage, known Ensemble-learning CCFD, is an ensemble model that applies the Man-Ensemble method on the most effective ML algorithms from stage one. The output of the second stage is to get the final prediction instead of using common types of ensemblemore »learning, such as voting, stacking, boosting, and others. Our framework’s results showed the effectiveness and efficiency of our fraud detection system compared to using ML algorithms individually due to their weakness issues, such as errors, overfitting, bias, prediction accuracy, and even their robustness level.« less
    Free, publicly-accessible full text available January 1, 2023