For the edge computing network, whether the end-to-end delay satisfies the delay constraint of the task is critical, especially for delay-sensitive tasks. Virtual machine (VM) migration improves the robustness of the network, whereas it also causes service downtime and increases the end-to-end delay. To study the influence of failure, migration, and recovery of VMs, we define three states for the VMs in an edge server and build a continuous-time Markov chain (CTMC). Then, we develop a matrix-geometric method and a first passage time method to obtain the VMs timely reliability (VTR) and the end-to-end timely reliability (ETR). The numerical results are verified by simulation based on OMNeT++. Results show that VTR is a monotonic function of the migration rate and the number of VMs. However, in some cases, the increase in task VMs (TVMs) may conversely decrease VTR, since more TVMs also brings about more failures in a given time. Moreover, we find that there is a trade-off between TVMs and backup VMs (BVMs) when the total number of VMs is limited. Our findings may shed light on understanding the impact of VM migration on end-to-end delay and designing a more reliable edge computing network for delay-sensitive applications.
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http://dx.doi.org/10.3390/s21010093 | DOI Listing |
J Med Internet Res
January 2025
Division of Clinical Pathology, Department of Pathology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan.
Background: Sepsis, a critical global health challenge, accounted for approximately 20% of worldwide deaths in 2017. Although the Sequential Organ Failure Assessment (SOFA) score standardizes the diagnosis of organ dysfunction, early sepsis detection remains challenging due to its insidious symptoms. Current diagnostic methods, including clinical assessments and laboratory tests, frequently lack the speed and specificity needed for timely intervention, particularly in vulnerable populations such as older adults, intensive care unit (ICU) patients, and those with compromised immune systems.
View Article and Find Full Text PDFIntensive Care Med Exp
January 2025
Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL, 32224, USA.
Background: The discharge practices from the intensive care unit exhibit heterogeneity and the recognition of eligible patients for discharge is often delayed. Recognizing the importance of safe discharge, which aims to minimize readmission and mortality, we developed a dynamic machine-learning model. The model aims to accurately identify patients ready for discharge, offering a comparison of its effectiveness with physician decisions in terms of safety and discrepancies in discharge readiness assessment.
View Article and Find Full Text PDFBiosensors (Basel)
January 2025
INFN-Laboratori Nazionali di Frascati, Via E. Fermi 54, 00044 Frascati, Italy.
The COVID-19 pandemic has highlighted the urgent need for rapid, sensitive, and reliable diagnostic tools for detecting SARS-CoV-2. In this study, we developed and optimized a surface plasmon resonance (SPR) biosensor incorporating advanced materials to enhance its sensitivity and specificity. Key parameters, including the thickness of the silver layer, silicon nitride dielectric layer, molybdenum disulfide (MoS) layers, and ssDNA recognition layer, were systematically optimized to achieve the best balance between sensitivity, resolution, and attenuation.
View Article and Find Full Text PDFBrain Sci
December 2024
Department of Psychiatry, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul 03181, Republic of Korea.
Background/objectives: Stressors occurring across the life course are considered to have a cumulative impact on health, but there is no instrument for assessing lifetime stressor exposure in Korea. Therefore, we validated the Stress and Adversity Inventory (Adult STRAIN) in Korean.
Methods: We translated the Adult STRAIN into Korean and examined its concurrent, predictive, and comparative predictive validity in 218 Korean adults (79 men, 139 women; = 29.
Bioengineering (Basel)
December 2024
Faculty of Medicine, Dentistry and Health Sciences, The University of Melbourne, 720, Swanston Street, Carlton, VIC 3053, Australia.
Artificial intelligence (AI) has gained significant traction in medical image analysis, including dentistry, aiding clinicians in making timely and accurate diagnoses. Radiographs, such as orthopantomograms (OPGs) and intraoral radiographs, along with clinical photographs, are the primary imaging modalities employed for AI-powered analysis in the dental field. In this review, we discuss the most recent research and product developments concerning the clinical application of AI as a visual aid in dentistry and introduce the concept of Observational Diagnostics (ODs) as a structured method to standardise image analysis.
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