RESUMEN
There are an increasing number of manufacturing service resources appeared on the cloud manufacturing (CMfg) service platform recently, which leads to a serious information overloading problem to the enterprises that need these resources. To tackle this problem, a graph neural network-based recommendation method for CMfg service resources is proposed, which effectively overcomes some limitations of the traditional recommendation methods. Specifically, we first use different similarity calculation methods (e.g., Cosine similarity, Pearson correlation coefficient, etc.) to calculate the similarities between different resources based on the feature information of CMfg service resources. A resource graph dataset is accordingly established. A graph neural network is then used to perform representation learning of nodes in these graphs, obtaining the vector representations of these nodes. Finally, new links that may appear in a graph are predicted by performing dot product calculations on these nodes' vector representations. And these links can be used to recommend suitable resources. Experiments mainly show that (i) the proposed method obtains better link prediction accuracy compared with that of other link prediction algorithms; (ii) when the network density used for training is relatively high, the predictive performance of the proposed method is improved significantly. Our method can shed light on how to choose suitable CMfg service resources from the CMfg service platform.
Asunto(s)
Algoritmos , Redes Neurales de la Computación , Nube Computacional , Comercio , Correlación de DatosRESUMEN
BACKGROUND: Coronavirus disease 2019 (COVID-19) is a global pandemic caused by the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). There is no specific cure for this disease, and the clinical management mainly depends on supportive treatment. This disease may affect SARS-CoV-2 conjunctivitis. Yuxingcao eye drops is used in treating COVID-19 conjunctivitis in China. METHODS: A comprehensive literature search will be conducted. Two methodological trained researchers will read the title, abstract, and full texts and independently select the qualified literature according to inclusion and exclusion criteria. After assessment of the risk of bias and data extraction, we will conduct meta-analyses for outcomes related to COVID-19 conjunctivitis. The heterogeneity of data will be investigated by Cochrane X and I tests. Then publication bias assessment will be conducted by funnel plot analysis and Egger test. RESULTS: The results of our research will be published in a peer-reviewed journal. CONCLUSION: Our study aims to systematically present the clinical evidence of Yuxingcao eye drops in treating COVID-19 conjunctivitis, which will be of significant meaning for further research and clinical practice. PROSPERO REGISTRATION NUMBER: PROSPERO CRD42020209059.
Asunto(s)
COVID-19/complicaciones , Conjuntivitis/tratamiento farmacológico , Conjuntivitis/etiología , Medicamentos Herbarios Chinos/uso terapéutico , Ensayos Clínicos como Asunto , Conjuntivitis/virología , Medicamentos Herbarios Chinos/administración & dosificación , Medicamentos Herbarios Chinos/efectos adversos , Humanos , Soluciones Oftálmicas , Pandemias , Proyectos de Investigación , SARS-CoV-2RESUMEN
The technology of Internet of Things (IoT) has appealed to both professionals and the general public to its convenience and flexibility. As a crucial application of IoT, telecare medicine information system (TMIS) provides people a high quality of life and advanced level of medical service. In TMIS, smart card-based authenticated key agreement schemes for multi-server architectures have gathered momentum and positive impetus due to the conventional bound of a single server. However, we demonstrate that most of the protocols in the literatures can not implement strong security features in TMIS, such as Lee et al.'s and Shu's scheme. They store the identity information directly, which fail to provide strong anonymity and suffer from password guessing attack. Then we propose an extended authenticated key agreement scheme (short for AKAS) with strong anonymity for multi-server environment in TMIS, by enhancing the security of the correlation parameters stored in the smart cards and calculating patients' dynamic identities. Furthermore, the proposed chaotic map-based scheme provides privacy protection and is formally proved under Burrows-Abadi-Needham (BAN) logic. At the same, the informal security analysis attests that the AKAS scheme not only could resist the multifarious security attacks but also improve efficiency by 21% compared with Lee et al.'s and Shu's scheme.