Underwater wireless sensor networks (UWSNs) have become a new hot research area. However, due to the work dynamics and harsh ocean environment, how to obtain an UWSN with the best systematic performance while deploying as few sensor nodes as possible and setting up self-adaptive networking is an urgent problem that needs to be solved. Consequently, sensor deployment, networking, and performance calculation of UWSNs are challenging issues, hence the study in this paper centers on this topic and three relevant methods and models are put forward. Firstly, the normal body-centered cubic lattice to cross body-centered cubic lattice (CBCL) has been improved, and a deployment process and topology generation method are built. Then most importantly, a cross deployment networking method (CDNM) for UWSNs suitable for the underwater environment is proposed. Furthermore, a systematic quar-performance calculation model (SQPCM) is proposed from an integrated perspective, in which the systematic performance of a UWSN includes coverage, connectivity, durability and rapid-reactivity. Besides, measurement models are established based on the relationship between systematic performance and influencing parameters. Finally, the influencing parameters are divided into three types, namely, constraint parameters, device performance and networking parameters. Based on these, a networking parameters adjustment method (NPAM) for optimized systematic performance of UWSNs has been presented. The simulation results demonstrate that the approach proposed in this paper is feasible and efficient in networking and performance calculation of UWSNs.
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http://dx.doi.org/10.3390/s17071619 | DOI Listing |
Eur J Appl Physiol
January 2025
College of Kinesiology, University of Saskatchewan, Saskatoon, SK, Canada.
Resistance training (RT) load and volume are considered crucial variables to appropriately prescribe and manage for eliciting the targeted acute responses (i.e., minimizing neuromuscular fatigue) and chronic adaptations (i.
View Article and Find Full Text PDFBMC Med Educ
January 2025
School of Nursing, Seirei Christopher University, Hamamatsu, Shizuoka, Japan.
Background: Point-of-care ultrasound (POCUS) can be used in a variety of clinical settings and is a safe and powerful tool for ultrasound-trained healthcare providers, such as physicians and nurses; however, the effectiveness of ultrasound education for nursing students remains unclear. This prospective cohort study aimed to examine the sustained educational impact of bladder ultrasound simulation among nursing students.
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BMC Genomics
January 2025
Henan Collaborative Innovation Center of Modern Biological Breeding, College of Agronomy, Henan Institute of Science and Technology, Xinxiang, 453003, China.
Background: The Sec14 domain is an ancient lipid-binding domain that evolved from yeast Sec14p and performs complex lipid-mediated regulatory functions in subcellular organelles and intracellular traffic. The Sec14 family is characterized by a highly conserved Sec14 domain, and is ubiquitously expressed in all eukaryotic cells and has diverse functions. However, the number and characteristics of Sec14 homologous genes in soybean, as well as their potential roles, remain understudied.
View Article and Find Full Text PDFSci Rep
January 2025
Research Unit of Health Sciences and Technology, University of Oulu, Oulu, Finland.
Optical techniques, such as functional near-infrared spectroscopy (fNIRS), contain high potential for the development of non-invasive wearable systems for evaluating cerebral vascular condition in aging, due to their portability and ability to monitor real-time changes in cerebral hemodynamics. In this study, thirty-six healthy adults were measured by single channel fNIRS to explore differences between two age groups using machine learning (ML). The subjects, measured during functional magnetic resonance imaging (fMRI) at Oulu University Hospital, were divided into young (age ≤ 32) and elderly (age ≥ 57) groups.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Electrical Electronical Engineering, Yaşar University, Bornova, İzmir, Turkey.
We aimed to build a robust classifier for the MGMT methylation status of glioblastoma in multiparametric MRI. We focused on multi-habitat deep image descriptors as our basic focus. A subset of the BRATS 2021 MGMT methylation dataset containing both MGMT class labels and segmentation masks was used.
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