Medication errors associated with the Medication Use Process present significant risks and require effective management. However, current practices do not address these risks. There is a lack of awareness that medicat...
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ISBN:
(纸本)9798350319439
Medication errors associated with the Medication Use Process present significant risks and require effective management. However, current practices do not address these risks. There is a lack of awareness that medication errors are unintended risks, and a lack of dedicated systems to manage them. To overcome these limitations, a digital system exploiting massive medical data (big data) is proposed. By integrating various data sources, this system aims to provide adaptable medication errors' management and continuous improvement. This article presents an overview of the system requirements and highlights the potential of data Mining in healthcare. Implementing this system could revolutionize medication error management and improve patient safety.
In this paper, we first introduce the transmission model of MIMO-OFDM. A semi blind estimation method based on noise subspace is proposed to overcome some limitations of blind estimation methods. Then a new transmissi...
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ISBN:
(纸本)9798350386288;9798350386271
In this paper, we first introduce the transmission model of MIMO-OFDM. A semi blind estimation method based on noise subspace is proposed to overcome some limitations of blind estimation methods. Then a new transmission scheme is proposed, in which the channel information obtained by blind or semi blind estimation will be used for the precoder. At the end of this paper, the performance of this data transmission scheme is analyzed and discussed, and the parameters, especially the size of the data frame, can be adjusted according to the needs, which can affect the transmission efficiency and delay. Adjusting parameters according to channel conditions and communication requirements is crucial for optimizing the efficiency and robustness of transmission systems.
In economics, the study of macroeconomic laws often encounters challenges such as difficulties in data collection and low data quality. A complex economic system (CES) simulation model based on Multi-Agent Simulation ...
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This paper studies to what extent an adversary (without the original graph data) can recover the original raw graph data from graph embeddings. To quantify the original graph data information leakage from graph embedd...
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ISBN:
(纸本)9781665488679
This paper studies to what extent an adversary (without the original graph data) can recover the original raw graph data from graph embeddings. To quantify the original graph data information leakage from graph embeddings, we develop a deep neural network model InferNet that can be used by adversaries to infer the original graph data information from an adversary-accessible graph embedding database. Specifically, we propose the data-free reversed knowledge distillation (KD) technique to support InferNet training even if the original graph dataset is absent. To improve the performance of InferNet, we design two cycle-consistency loss functions to have an interactive training of InferNet over three series of datasets. Our intensive experiments demonstrate that InferNet can infer the original graph data information from the graph embedding dataset with high accuracy.
Cultural heritage artifacts are invaluable to understanding our shared history and cultural identity. However, these artifacts are increasingly at risk due to aging, environmental factors, activities and humans. This ...
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Mobile edge computing (MEC) is an emerging computing paradigm where application vendors deploy applications at the edge of the network by configuring computing and storage resources, to achieve low latency, high bandw...
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作者：
Gu, YifanQu, ZhiShenzhen Univ
State Key Lab Radio Frequency Heterogeneous Integ Shenzhen 518060 Guangdong Peoples R China Shenzhen Univ
Coll Elect & Informat Engn Shenzhen 518060 Guangdong Peoples R China
In this paper, we study the sampling rate design problem of status update systems under unknown and non-stationary delay statistics, where the delay statistics include both the distribution of transmission delay and t...
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ISBN:
(纸本)9798350377675;9798350377682
In this paper, we study the sampling rate design problem of status update systems under unknown and non-stationary delay statistics, where the delay statistics include both the distribution of transmission delay and the packet loss probability. When the delay statistics are non-stationary, short-term average Age-of-Information (AoI) is much more critical than the long-term average AoI. Motivated by this fact, we formulate a sampling rate control problem by minimizing the Mean Squared Error (MSE) of the average AoI in each control cycle to a predefined AoI target, such that both the short-term and long-term average AoI approach to the target. The formulated problem is different from the existing works on the minimization of long-term average AoI under known or stationary delay statistics. Based on the average AoI feedback at the end of each control cycle, we first present a Delta Sampling (DS) policy with a fixed updating step. Because the DS policy may converge slowly, we then propose a novel Adaptive Sampling Control (ASC) policy to adjust the sampling rate dynamically. The main challenge in developing ASC is the lack of an explicit model to reflect the relationship between the sampling rate and average AoI. To overcome this issue, we design an equivalent dynamic linearization data model with a Pseudo-Partial Derivative (PPD), where the unknown features of delay statistics can be captured by the time-varying PPD. By estimating the PPD in each control cycle, ASC can determine a suitable sampling rate based on the tractable data model. Numerical results show that the proposed ASC policy outperforms the DS, zero waiting, and fixed sampling policies significantly in terms of MSE by more than 100% under unknown and non-stationary delay statistics.
In the era of big data, the escalating volume and velocity of data generation pose significant challenges in data processing. Traditional systems like Spark [1] and Hadoop [2] manage the increasing amount and velocity...
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The advancement in technology leads to provide an efficient communication among vehicles to offload resource-intensive tasks for transportation-based services. However, it may cause issue related to efficient secure r...
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