Adaptive CPU Resource Allocation for Emulator in Kernel-based Virtual Machine

The technologies of heterogeneous multi-core architectures, co-location, and virtualization can be used to reduce server power consumption and improve system utilization, which are three important technologies for data centers. This article explores the scheduling strategy of Emulator threads within virtual machine processes in a scenario of co-location of multiple virtual machines on heterogeneous multi-core architectures. In this co-location scenario, the scheduling strategy for Emulator threads significantly affects the performance of virtual machines. This article focuses on this thread for the first time in the relevant field. This article found that the scheduling latency metric can well indicate the running status of the vCPU threads and Emulator threads in the virtualization environment, and applied this metric to the design of the scheduling strategy. This article designed an Emulator thread scheduler based on heuristic rules, which, in coordination with the host operating system's scheduler, dynamically adjusts the scheduling scope of Emulator threads to improve the overall performance of virtual machines. The article found that in real application scenarios, the scheduler effectively improved the performance of applications within virtual machines, with a maximum performance improvement of 40.7%

Construction of stable Ta3N5/g-C3N4 metal/non-metal nitride hybrids with enhanced visible-light photocatalysis

In this paper, a novel Ta3N5/g-C3N4 metal/non-metal nitride hybrid was successfully synthesized by a facile impregnation method. The photocatalytic activity of Ta3N5/g-C3N4 hybrid nitrides was evaluated by the degradation of organic dye rhodamine B (RhB) under visible light irradiation, and the result indicated that all Ta3N5/g-C3N4 samples exhibited distinctly enhanced photocatalytic activities for the degradation of RhB than pure g-C3N4. The optimal Ta3N5/g-C3N4 composite sample, with Ta3N5 mass ratio of 2%, demonstrated the highest photocatalytic activity, and its degradation rate constant was 2.71 times as high as that of pure g-C3N4. The enhanced photocatalytic activity of this Ta3N5/g-C3N4 metal/metal-free nitride was predominantly attributed to the synergistic effect which increased visible-light absorption and facilitated the efficient separation of photoinduced electrons and holes. The Ta3N5/g-C3N4 hybrid nitride exhibited excellent photostability and reusability. The possible mechanism for improved photocatalytic performance was proposed. Overall, this work may provide a facile way to synthesize the highly efficient metal/metal-free hybrid nitride photocatalysts with promising applications in environmental purification and energy conversion

Open X-Embodiment:Robotic learning datasets and RT-X models

Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backbones serving as a starting point for many applications. Can such a consolidation happen in robotics? Conventionally, robotic learning methods train a separate model for every application, every robot, and even every environment. Can we instead train "generalist" X-robot policy that can be adapted efficiently to new robots, tasks, and environments? In this paper, we provide datasets in standardized data formats and models to make it possible to explore this possibility in the context of robotic manipulation, alongside experimental results that provide an example of effective X-robot policies. We assemble a dataset from 22 different robots collected through a collaboration between 21 institutions, demonstrating 527 skills (160266 tasks). We show that a high-capacity model trained on this data, which we call RT-X, exhibits positive transfer and improves the capabilities of multiple robots by leveraging experience from other platforms. The project website is robotics-transformer-x.github.io

Expansion Mechanism and Properties of Magnesium Oxide Expansive Hydraulic Cement for Engineering Applications

The expansion mechanism of magnesium oxide expansive hydraulic cement as a novel expansive hydraulic cement was reviewed. Anisotropic crystallization results in crystal growth pressure, causing volume expansion while also increasing the porosity of the whole system. The theoretical relationship between porosity and expansion was analyzed. A basic method is given for predicting the expansion rate considering the expansive agent content in MgO expansive hydraulic cement. A concise equation is proposed for calculating the ultimate expansion. A theoretical relationship between porosity and expansion is presented. The compressive strength and durability of magnesium oxide expansive hydraulic cement were analyzed considering porosity changes and compared with hydraulic cement. If the expansion rate exceeds 0.8%, the mechanical properties and durability changes caused by porosity should be considered. If magnesium oxide expansive concrete is used with restraining in real structure, extra compressive stress is generated and the porosity decreases, compared with that during free expansion. In particular, for strain-hardening cementitious composites, expansion confined with the fibers present in the composite is beneficial for refining cracks and improving the self-healing ability of these materials whenever exposed to humid environments. This paper describes the expansion mechanism and properties of magnesium oxide expansive hydraulic cement for engineering applications

Pneumocystis jirovecii Pneumonia in Patients with Nephrotic Syndrome: Application of Lymphocyte Subset Analysis in Predicting Clinical Outcomes

Purpose. With immunosuppressants being widely used, Pneumocystis jirovecii pneumonia (PCP) has been increasing and could be life-threatening among HIV-negative patients. This study aimed at identifying prognostic factors of PCP in patients with nephrotic syndrome. Methods. We retrospectively investigated patients with nephrotic syndrome who were diagnosed with PCP. The diagnosis of PCP was based on clinical manifestations, radiological findings, and microbiological confirmatory tests. Predictors of outcome were determined with multivariate logistic regression analysis. Results. A total of 57 patients were included in this study. The PCP mortality was 33.3%, which increased to 48.6% if ICU admission was required and to 60% when mechanical ventilation was needed. The T lymphocyte count and CD4/CD8 ratio independently predicted the outcome of PCP, so did the CD4+ T lymphocyte count (OR, 0.981; 95% CI, 0.967–0.996; p=0.001). The cut-off value of 71 cells/μl for the CD4+ T lymphocyte count was determined to identify patients with poor prognosis. No association was found between PCP mortality and the type of immunosuppressant used. Conclusions. PCP is a fatal complication among nephrotic syndrome patients receiving immunosuppressive therapy. The CD4+ T lymphocyte count is suggested as an independent predictor of prognosis, which can be used clinically to identify patients with high risk of unfavorable outcomes

Comparative Study of Parameter Extraction from a Solar Cell or a Photovoltaic Module by Combining Metaheuristic Algorithms with Different Simulation Current Calculation Methods

In this paper, single-diode model (SDM) and double-diode model (DDM) parameters of the French RTC solar cell and the Photowatt PWP 201 photovoltaic (PV) module were extracted by combining five metaheuristic algorithms with three simulation current calculation methods (i.e., approximation method, Lambert W method and Newton–Raphson method), respectively. It was found that the parameter-extraction accuracies of the Lambert W (LW) method and the Newton–Raphson (NR) method are always approximately equal and higher than that of the approximation method. The best RMSEs (root mean square error) obtained by using the LW or the NR method on the solar cell and the PV module are 7.72986 × 10−4 and 2.05296 × 10−3 for SDM parameter extraction and 6.93709 × 10−4 and 1.99051 × 10−3 for DDM parameter extraction, respectively. The latter may be the highest parameter-extraction accuracy reported on the solar cell and the PV module so far, which is due to the adoption of more reasonable DDM parameter boundaries. Furthermore, the convergence curves of the LW and the NR method basically coincide, with a convergence speed faster than that of the approximation method. The robustness of a parameter-extraction method is mainly determined by the metaheuristic algorithm, but it is also affected by the simulation current calculation method and the parameter-extraction object. In a word, the approximation method is not suitable for application in PV-model parameter extraction because of incorrect estimation of the simulation current and the RMSE, while the LW and NR methods are suitable for the application for accurately calculating the simulation current and RMSE. In terms of saving computation resources and time, the NR method is superior to the LW method