To realize a liberalized peer-to-peer (P2P) electricity market in distribution systems with network security, this paper develops a general framework for P2P trading in distribution systems with the utility's operation. The model is formulated as a bi-level programming. The utility's operation is an upper level problem, where a calculation method of network usage charges for P2P trading is also proposed. Peers' P2P trading is a lower level problem. An iterative algorithm based on analytical target cascading (ATC) is proposed to solve the model, where the interactions between utility and peers are presented. Numerical results on the IEEE 33-bus system demonstrate that the proposed method realizes a liberalized P2P market and ensures network security in distribution systems.
This study aimed to gather healthcare professionals’expectations and reluctance toward peer support in a cancer center.Semistructured interviews were conducted among 12 professionals,recruited in different professions.The interviews were fully transcribed,and a thematic analysis was then conducted.Of the data analysis,three main themes about professionals’expectations emerged:the need for the strongest support of the patients,to break the isolation in the sickness,and to enhance the care system.Three main themes also emerged from the data analysis about professionals’reluctances:the limitations related to the intervention of the peer-workers,the psychological issues of the relationship,and institutional barriers to the implementation of peer-support interventions.Our study shows that peer support could be a response to the expectations of healthcare professionals’,but its implementation should consider their reluctance.
Scalability and information personal privacy are vital for training and deploying large-scale deep learning models.Federated learning trains models on exclusive information by aggregating weights from various devices and taking advantage of the device-agnostic environment of web browsers.Nevertheless,relying on a main central server for internet browser-based federated systems can prohibit scalability and interfere with the training process as a result of growing client numbers.Additionally,information relating to the training dataset can possibly be extracted from the distributed weights,potentially reducing the privacy of the local data used for training.In this research paper,we aim to investigate the challenges of scalability and data privacy to increase the efficiency of distributed training models.As a result,we propose a web-federated learning exchange(WebFLex)framework,which intends to improve the decentralization of the federated learning process.WebFLex is additionally developed to secure distributed and scalable federated learning systems that operate in web browsers across heterogeneous devices.Furthermore,WebFLex utilizes peer-to-peer interactions and secure weight exchanges utilizing browser-to-browser web real-time communication(WebRTC),efficiently preventing the need for a main central server.WebFLex has actually been measured in various setups using the MNIST dataset.Experimental results show WebFLex’s ability to improve the scalability of federated learning systems,allowing a smooth increase in the number of participating devices without central data aggregation.In addition,WebFLex can maintain a durable federated learning procedure even when faced with device disconnections and network variability.Additionally,it improves data privacy by utilizing artificial noise,which accomplishes an appropriate balance between accuracy and privacy preservation.
Mai AlzamelHamza Ali RizviNajwa AltwaijryIsra Al-Turaiki
