Multihop transmission is one of the important techniques to overcome the transmission coverage of each node in wireless sensor networks (WSNs). However, multihop transmission has a security issue due to the nature of a wireless medium. Additionally, the eavesdropper also attempts to interrupt the legitimate users' transmission. Thus, in this paper, we study the secrecy performance of a multihop transmission under various eavesdropping attacks for WSNs. To improve the secrecy performance, we propose two node selection schemes in each cluster, namely, minimum node selection (MNS) and optimal node selection (ONS) schemes. To exploit the impact of the network parameters on the secrecy performance, we derive the closed-form expression of the secrecy outage probability (SOP) under different eavesdropping attacks. From the numerical results, the ONS scheme shows the most robust secrecy performance compared with the other schemes. However, the ONS scheme requires a lot of channel information to select the node in each cluster and transmit information. On the other side, the MNS scheme can reduce the amount of channel information compared with the ONS scheme, while the MNS scheme still provides secure transmission. In addition, the impact of the network parameters on the secrecy performance is also insightfully discussed in this paper. Moreover, we evaluate the trade-off of the proposed schemes between secrecy performance and computational complexity.
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http://dx.doi.org/10.3390/s23177653 | DOI Listing |
Med Biol Eng Comput
January 2025
Anhui BioX-Vision Biological Technology Co., Ltd, Hefei, 230031, Anhui, China.
The identification and categorization of circulating tumor cells (CTCs) in peripheral blood are imperative for advancing cancer diagnostics and prognostics. The intricacy of various CTCs subtypes, coupled with the difficulty in developing exhaustive datasets, has impeded progress in this specialized domain. To date, no methods have been dedicated exclusively to overcoming the classification challenges of CTCs.
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December 2024
Biomedical Sciences, University of Chicago, Chicago, USA.
Pediatric-type follicular lymphoma (PTFL) is an extremely rare B-cell lymphoma that primarily affects children and young adults, typically in individuals under 25 years old, with a median age of 15 years. Here, we report a rare case of PTFL in a 27-year-old adult male who presented with a slow-growing mass near his left ear. Initial CT scans of the neck revealed two oval-shaped, smooth, well-defined, homogeneously enhancing soft tissue density lesions in the superficial lobe of the left parotid gland.
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December 2024
Paediatrics, Maternity and Children Hospital, AlAhsa, SAU.
Background Maternal diabetes mellitus (DM) is a known risk factor for congenital heart diseases (CHDs), which are of significant concern to infants born to diabetic mothers. Compared to newborns born to non-diabetic mothers, infants born to diabetic mothers had a higher overall risk of developing congenital malformations. This association has a complex pathophysiology that includes genetic predispositions, metabolic abnormalities, and environmental factors during key stages of fetal development.
View Article and Find Full Text PDFBackground: Critical care nurses are vulnerable to depression, which not only lead to poor well-being and increased turnover intention, but also affect their working performances and organizational productivity as well. Work related factors are important drivers of depressive symptoms. However, the non-liner and multi-directional relationships between job demands-resources and depressive symptoms in critical care nurses has not been adequately analyzed.
View Article and Find Full Text PDFBMC Nurs
January 2025
Department of Pain Medicine, the 1st affiliated hospital, Jiangxi Medical College, Nanchang University, 17 Yongwai Street, Nanchang, China.
Background: Mild cognitive impairment (MCI) is prevalent in older adults with chronic pain, making early detection crucial for dementia prevention and healthy aging. This study aimed to determine MCI risk factors in older patients with chronic pain and to develop 9 machine learning models to identify MCI risk.
Methods: A total of 612 older patients with chronic pain were recruited between October 2023 and July 2024.
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