Pouzolzia zeylanica was extracted with different solvents (acetone, ethyl acetate and petroleum ether), using different protocols (cold-extraction and Soxhlet extraction). To evaluate the antiradical and antioxidant abilities of the extracts, four in vitro test systems were employed, i.e., DPPH, ABTS and hydroxyl radical scavenging assays and a reducing power assay. All extracts exhibited outstanding antioxidant activities that were superior to that of butylated hydroxytoluene. The ethyl acetate extracts exhibited the most significant antioxidant activities, and cold-extraction under stirring seemed to be the more efficacious method for acquiring the predominant antioxidants. Furthermore, the antioxidant activities and total phenolic (TP) content of different extracts followed the same order, i.e., there is a good correlation between antioxidant activities and TP content. The results showed that these extracts, especially the ethyl acetate extracts, could be considered as natural antioxidants and may be useful for curing diseases arising from oxidative deterioration.
Recruitment and retention are of paramount importance to medical radiation science (MRS) as a profession. There is a strong demand for MRS practitioners which is expected to continue as the population ages. This study aimed to examine demographic data, factors relating to the career choice of MRS, and future work or study plans of first year MRS students. Questionnaires were distributed to 83 first year students, currently enrolled in MRS at the University of Sydney. A total of 73 completed questionnaires were received. This sample included 30 diagnostic radiography students, 24 nuclear medicine students and 19 radiation therapy students. The top three factors that influenced students' career choices was wanting to help others, followed by wanting to work in a healthcare field, then wanting to work with technology. The most common source of students' career information was family members, friends and health professionals. Among the students, 68.5% were aware of advancement opportunities in their stream of MRS. About half of the students planned to specialise or undertake postgraduate study in MRS, and 39.7% planned to study another degree after graduating. The results of this study indicated that many students chose MRS as a career with the goal of helping others. The most frequently reported source through which the students first heard about the profession was personal contacts. Furthermore, about half of the students were interested in further study. This information can be used by professional organisations, educational programs, or employers to assist in recruitment and retention strategies of MRS students.
Most existing federated learning methods assume that clients have fully labeled data to train on, while in reality, it is hard for the clients to get task-specific labels due to users' privacy concerns, high labeling costs, or lack of expertise. This work considers the server with a small labeled dataset and intends to use unlabeled data in multiple clients for semi-supervised learning. We propose a new framework with a generalized model, Federated Incremental Learning (FedIL), to address the problem of how to utilize labeled data in the server and unlabeled data in clients separately in the scenario of Federated Learning (FL). FedIL uses the Iterative Similarity Fusion to enforce the server-client consistency on the predictions of unlabeled data and uses incremental confidence to establish a credible pseudolabel set in each client. We show that FedIL will accelerate model convergence by Cosine Similarity with normalization, proved by Banach Fixed Point Theorem. The code is available at https://anonymous.4open.science/r/fedil.
The primary goal of online change detection (OCD) is to promptly identify changes in the data stream. OCD problem find a wide variety of applications in diverse areas, e.g., security detection in smart grids and intrusion detection in communication networks. Prior research usually assumes precise knowledge of the system parameters. Nevertheless, this presumption often proves unattainable in practical scenarios due to factors such as estimation errors, system updates, etc. This paper aims to take the first attempt to develop a triadic-OCD framework with certifiable robustness, provable optimality, and guaranteed convergence. In addition, the proposed triadic-OCD algorithm can be realized in a fully asynchronous distributed manner, easing the necessity of transmitting the data to a single server. This asynchronous mechanism could also mitigate the straggler issue that faced by traditional synchronous algorithm. Moreover, the non-asymptotic convergence property of Triadic-OCD is theoretically analyzed, and its iteration complexity to achieve an ǫ-optimal point is derived. Extensive experiments have been conducted to elucidate the effectiveness of the proposed method.
Semi-supervised learning (SSL) is a popular research area in machine learning which utilizes both labeled and unlabeled data. As an important method for the generation of artificial hard labels for unlabeled data, the pseudo-labeling method is introduced by applying a high and fixed threshold in most state-of-the-art SSL models. However, early models prefer certain classes that are easy to learn, which results in a high-skewed class imbalance in the generated hard labels. The class imbalance will lead to less effective learning of other minority classes and slower convergence for the training model. The aim of this paper is to mitigate the performance degradation caused by class imbalance and gradually reduce the class imbalance in the unsupervised part. To achieve this objective, we propose FocalMatch, a novel SSL method that combines FixMatch and focal loss. Our contribution of FocalMatch adjusts the loss weight of various data depending on how well their predictions match up with their pseudo labels, which can accelerate system learning and model convergence and achieve state-of-the-art performance on several semi-supervised learning benchmarks. Particularly, its effectiveness is demonstrated with the dataset that has extremely limited labeled data.
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