Yi Chen’s research while affiliated with National Cheng Kung University and other places

Background: Inconsistencies with guidelines or standards regarding nurses’ practice of vasoactive agent infusion have been documented. Adequate knowledge and positive attitudes are critical for compliance. However, there are currently no validated tools specifically designed to measure the knowledge and attitudes related to vasoactive agent infusions among nurses. Objective: The aim of this study was to develop and test the validity and reliability of the Chinese mainland version of Knowledge, Attitude, and Practice of Vasoactive Agents Infusions Questionnaire among nurses. Methods: The initial questionnaire items were developed through a comprehensive literature review, expert consultation, and pilot study. From February to June 2024, cross-sectional data were collected using convenience sampling from 538 nurses across 9 hospitals in Sichuan Province, China. The reliability and validity of the scale were evaluated through internal consistency reliability, inter-rater reliability, exploratory factor analysis, and confirmatory factor analysis. Results: The final version of the questionnaire included 33 items across 3 dimensions, explaining 78.04% of the variance, with item loadings ranging from 0.56 to 0.89. The content validity index ranged from 0.91 to 1.00, and the scale-level content validity index was 0.98.The overall Cronbach’s α for the questionnaire was 0.96, with Cronbach’s α for each dimension ranging from 0.96 to 0.98. The test-retest reliability for the entire questionnaire was 0.90, and for each dimension, it ranged from 0.90 to 0.94 (p < 0.05). The confirmatory factor analysis demonstrated acceptable fit indices for the three-dimensional model: χ²/(df) = 1.135, p = 0.200, RMSEA = 0.024, CFI = 0.986, TLI = 0.985, GFI = 0.884, and NFI = 0.895. Conclusion: The Knowledge, Attitude, and Practice of Vasoactive Agents Infusions Questionnaire demonstrates good reliability and validity, making it a reliable measurement tool for assessing nurses’ attitudes and knowledge related to vasoactive agents’ infusions. This version will facilitate further research and advancements in this specific field of study.

The abundant data in meteorological science has facilitated applying big data techniques. The data collection was achieved by researchers using different atmospheric sounding methods in the past few decades. Compared with traditional methods, such as statistical forecast approaches and numerical weather prediction, intelligent Internet of Things (IoT) technologies have attracted extensive attention in meteorological science due to their significant advantages in data processing and analysis. In addition, extreme weather events and meteorological disasters have occurred frequently around the world in recent years. Against this background, this article aims to introduce the application of intelligent IoT technologies in meteorological science and elaborate the encountered open problems as well as the challenges in the future. Along with the introduction of intelligent IoT, a comprehensive review of current studies on meteorological observation, forecast, and services with intelligent IoT is provided. Correspondingly, the impact of intelligent IoT on meteorological businesses is analyzed. Finally, as for the meteorological operations in IoT based on artificial intelligence (AI), some open research issues, countermeasures and future potential research directions are put forward.

The Internet of Things (IoT) network is a complex system interconnected by different types of devices, e.g., sensors, smartphones, computers, etc.. Community detection is a critical component to understand and manage complex IoT networks. Although several community detection algorithms were proposed, they in general suffer several issues, such as lack of optimal solutions and scalability, and difficulty to be applied to a dynamic IoT environment. In this work, we propose a framework that uses Distributed Community Detection (DCD) algorithms based on Gromov-Wasserstein (GW) metric, namely GW-DCD, to support scalable community detection and address the issues with the existing community detection algorithms. The proposed GW-DCD applies Gromov-Wasserstein metric to detect communities of IoT devices embedded in a Euclidean space or in a graph space. GW-DCD is able to handle community detection problems in a dynamic IoT environment, utilizing translation/rotation invariance properties of the GW metric. In addition, distributed community detection approach and parallel matrix computations can be integrated into GW-DCD to shorten the execution time of GW-DCD. Finally, a new metric, i.e., Gromov-Wasserstein driven mutual information (GWMI), is derived to measure the performance of community detection by considering internal structure within each community. Numerical experiments for the proposed GW-DCD were conducted with simulated and real-world datasets. Compared to the existing community detection algorithms, the proposed GW-DCD can achieve a much better performance in terms of GWMI and the runtime.

With the continuous progress of renewable energy technology and the large-scale construction of microgrids, the architecture of power systems is becoming increasingly complex and huge. In order to achieve efficient and low-delay data processing and meet the needs of smart grid users, emerging smart energy systems are often deployed at the edge of the power grid, and edge computing modules are integrated into the microgrids system, so as to realize the cost-optimal control decision of the microgrids under the condition of load balancing. Therefore, this paper presents a bilevel optimization control model, which is divided into an upper-level optimal control module and a lower-level optimal control module. The purpose of the two-layer optimization modules is to optimize the cost of the power distribution of microgrids. The function of the upper-level optimal control module is to set decision variables for the lower-level module, while the function of the lower-level module is to find the optimal solution by mathematical methods on the basis of the upper-level and then feed back the optimal solution to the upper-layer. The upper-level and lower-level modules affect system decisions together. Finally, the feasibility of the bilevel optimization model is demonstrated by experiments.

With the wide application of advanced communication and information technology, false data injection attack (FDIA) has become one of the significant potential threats to the security of smart grid. Malicious attack detection is the primary task of defense. Therefore, this paper proposes a method of FDIA detection based on vector auto-regression (VAR), aiming to improve safe operation and reliable power supply in smart grid applications. The proposed method is characterized by incorporating with VAR model and measurement residual analysis based on infinite norm and 2-norm to achieve the FDIA detection under the edge computing architecture, where the VAR model is used to make a short-term prediction of FDIA, and the infinite norm and 2-norm are utilized to generate the classification detector. To assess the performance of the proposed method, we conducted experiments by the IEEE 14-bus system power grid model. The experimental results demonstrate that the method based on VAR model has a better detection of FDIA compared to the method based on auto-regressive (AR) model.

This paper introduces a novel physical layer (PHY-layer) authentication scheme, called Transfer Learning based PHY-layer Authentication (TL-PHA), aiming to achieve fast online user authentication that is highly desired for latency sensitive applications such as edge computing. The proposed TL-PHA scheme is characterized by incorporating with a novel convolutional neural network architecture, namely Triple Pool Network (TP-Net), for achieving lightweight and online classification, as well as effective data augmentation methods for generation of dataset samples for the network model training. To assess the performance of the proposed scheme, we conducted two sets of experiments, including the one using computer-simulated channel data, and the other utilizing real experiment data generated by our wireless testbed. The results demonstrate the superiority of the proposed scheme in terms of authentication accuracy, detection rate, and training complexity compared with all the considered counterparts.

For real-time industrial wireless edge-enabled cyber-physical systems, this paper proposes an adaptive secure transmission mechanism under the large scale path loss to fight against randomly distributed illegal sensing devices. According to changes in the communication environment, the proposed scheme adaptively obtains the corresponding optimal power allocations and wiretap code rates. Specifically, by injecting artificial noise, an edge controller provides confidential control information for a legitimate wireless sensing device and achieves non-confidential data service for a service subscriber, simultaneously. Considering whether the secrecy and non-confidential service rate constraints are met, a dynamic secrecy transmission mechanism is introduced to minimize secrecy outage probability (SOP) based on random channel realizations. The optimal power allocation for minimizing the SOP is derived through the efficient alternating optimization algorithm with numerical methods. Numerical results show that the proposed adaptive secure transmission strategy can efficiently utilize transmit power to achieve non-confidential information traffic without compromising the secrecy performance.

Due to the requirement of real time controlling on the vehicular ad-hoc networks (VANETs), the security measure should be fast, lightweight, and strong. In this paper, we investigate the secure communication between the on-board unit and the roadside unit which has strong computing ability to remedy the insufficient computing power of on-board unit. The novel schemes are developed to guarantee both security and energy harvest. Specifically, a muti-antenna roadside unit (PT) conveys a secret message to a muti-antenna primary onboard unit (PR) intercepted by multiple muti-antenna adversaries (Eves). Simultaneously, a secondary roadside unit (ST) adopts simultaneous wireless information and power transfer (SWIPT) strategy to send secret-free data embeded with energy-carrying artificial noise (AN) to its service subscriber (SR), by which secrecy guarantee is provided for the primary on-board unit and the quality of service (QoS) is satisfied, i.e., data traffic and harvested energy. Technically, considering the secondary link's data transmission rate, harvested energy, and transmit power, we propose joint power allocation designs for the nonconvex secrecy rate maximization (SRM) problems under either perfect channel state information (CSI) or norm-bounded channel uncertainty model. Simulation results show that the proposed secondary linkaided secrecy designs can achieve better performance in terms of secrecy rate and energy efficiency.