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This research presents a comparative performance analysis of three prominent hypervisor architectures—KVM, Xen, and VMware Workstation—to evaluate their scalability and resource efficiency under increasing virtual machine (VM) densities. Utilizing a standardized benchmarking framework, this study quantifies performance regressing across CPU throughput (GFLOPS), memory bandwidth, disk I/O, and initialization latency. The experiment results reveal that architectural design significantly dictates scalability limits: KVM’s kernel-integrated Type-1 model achieved the highest efficiency, maintaining a near-native CPU baseline and superior I/O resilience at peak density. In contrast, Xen’s microkernel architecture demonstrated stable but lower throughput due to Domain 0 (Dom0) management overhead, while VMware’s Type-2 hosted model exhibited the most acute performance degradation. These results demonstrate that while hosted hypervisors are suitable for low-density personal use, kernel-level integration is a mechanical necessity for maintaining performance in high-concurrency server environments. This work provides a reproducible methodology and critical data for system architects and researchers seeking to optimize resource orchestration in distributed and parallel systems.
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This study evaluates the viability of deep learningbased super-resolution (SR) for enhancing real-time video streaming. We compare a traditional HEVC-compressed streaming pipeline against an AI-assisted framework that transmits lowresolution video and reconstructs high-resolution output at the client using the Efficient Sub-Pixel Convolutional Neural Network (ESPCN) algorithm. The evaluation spans multiple resolutions (1080p to 4K), motion types, and processing methodologies (subprocess vs. frame-by-frame). Experimental results indicate that while AI-assisted streaming significantly reduces transmission bandwidth, it introduces substantial computational overhead, with completion times increasing by over 300% compared to traditional methods. Furthermore, quality assessments using PSNR and VMAF metrics reveal that most AI-assisted streams failed to meet the target quality range (30–50 dB PSNR), with high-resolution 4K streams experiencing up to 24.58% frame loss during real-time inference. These findings highlight critical performance barriers in utilizing current SR algorithms for standard consumer-grade real-time video delivery.
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Cardiovascular disease (CVD) risk stratification often relies on anthropometric measures that do not fully reflect central adiposity or differences across populations. The Body Roundness Index (BRI) is a body-shape indicator that may improve assessment of cardiometabolic risk, but evidence from large national cohorts remains limited. This study analyzed harmonized data from the China Health and Retirement Longitudinal Study (CHARLS, 2015) and the U.S. National Health and Nutrition Examination Survey (NHANES, 2005–2016) to examine the relationship between BRI and prevalent CVD. Participants were grouped into quartiles of BRI, with the highest quartile serving as the reference. Weighted multivariable logistic regression models were used to estimate odds ratios (ORs) and 95% confidence intervals (CIs), with sequential adjustment for demographic and clinical covariates. Subgroup analyses were performed to evaluate heterogeneity across population strata, and an extreme gradient boosting (XGBoost) model was applied to assess the relative importance of BRI and other predictors. In both cohorts, individuals with CVD were older and had higher prevalences of hypertension and diabetes, whereas BRI distributions differed between cohorts. Lower BRI quartiles were associated with greater odds of CVD. Compared with Q4, Q1 was associated with increased CVD risk in CHARLS (OR 1.73, 95% CI 1.14-2.68; p=0.012) and NHANES (OR 1.82, 95% CI 1.44-2.30; p<0.001). These findings suggest that lower BRI is associated with higher CVD risk and may complement conventional anthropometric measures in population-based cardiovascular risk assessment.
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