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The virtual synchronous generator (VSG) has been widely used to improve the system inertia and damping in the renewable energy generation system. However, the in-depth understanding of VSG’s stability under disturbances on different control parameters is lacked. In order to solve the problem, the small-signal model of single-VSG is established at first. The influences of key control parameters on the stability of system are analyzed by using the eigenvalue analysis method in detail. On this basis, a novel optimization strategy for control parameters is proposed based on the Particle Swarm Optimization (PSO) algorithm. The control parameters are optimized to realize excellent damping and stability of VSG system. Finally, the simulation and experimental results verify the effectiveness of stability analysis and parameter optimization strategy.

“On‐the‐fly” molecule enrichment by micro/nanomotors obviously improves heterogeneous catalysis, trace detection, and environmental monitoring, yet faces challenges of the trade‐off between collection range and interaction time. Inspired by the versatile foraging process of predators, this work demonstrates that micromotors doing spontaneous multipattern motion with microvortex can greatly enhance “on‐the‐fly” enrichment, demonstrated by highly sensitive surface‐enhanced Raman scattering detection. It leverages an axis‐asymmetric bowl‐shaped structure and the nonlinear Ag–AgCl reaction, realizing alternating low‐velocity swinging forward and accelerated steering motions for prolonged interaction and large work area. Moreover, the bowl‐shaped microstructure bestows a micro‐vortex above the Ag side due to the competition of electric potential and pressure gradient, also extending interaction time during the acceleration. Consequently, it exhibits at least an order of magnitude larger enhancement of detection signals than the counterparts. This proof‐of‐concept study highlights the significance of motion mode and structure design in guiding flow field, offering substantial benefits for applications.

Serotonin-based nanomaterials have been positioned as promising contenders for constructing multifunctional biomedical nanoplatforms due to notable biocompatibility, advantageous charge properties, and chemical adaptability. The elaborately designed structure and morphology are significant for their applications as functional carriers. In this study, we fabricated anisotropic bowl-like mesoporous polyserotonin (PST) nanoparticles with a diameter of approximately 170 nm through nano-emulsion polymerization, employing P123/F127 as a dual-soft template and 1,3,5-trimethylbenzene (TMB) as both pore expander and emulsion template. Their formation can be attributed to the synchronized assembly of P123/F127/TMB, along with the concurrent manifestation of anisotropic nucleation and growth on the TMB emulsion droplet surface. Meanwhile, the morphology of PST nanoparticles can be regulated from sphere- to bowl-like, with a particle size distribution ranging from 432 nm to 100 nm, experiencing a transformation from a dendritic, cylindrical open mesoporous structure to an approximately non-porous structure by altering the reaction parameters. The well-defined mesopores, intrinsic asymmetry, and pH-dependent charge reversal characteristics enable the as-prepared mesoporous bowl-like PST nanoparticles’ potential for constructing responsive biomedical nanomotors through incorporating some catalytic functional materials, 3.5 nm CeO2 nanoenzymes, as a demonstration. The constructed nanomotors demonstrate remarkable autonomous movement capabilities under physiological H2O2 concentrations, even at an extremely low concentration of 0.05 mM, showcasing the 51.58 body length/s velocity. Furthermore, they can also respond to physiological pH values ranging from 4.4 to 7.4, exhibiting reduced mobility with increasing pH. This charge reversal-based responsive nanomotor design utilizing PST nanoparticles holds great promise for advancing the application of nanomotors within complex biological systems.

Software rejuvenation is a proactive fault management technique widely used for preventing performance degradation and failures due to software aging. It works by proactively terminating the system, cleaning the internal state of the system, and restarting the system. Since rejuvenation incurs extra system overhead and downtime cost, it is important to determine the optimal time to trigger rejuvenation. This paper proposes a two-layer model to characterize the system failure behavior and rejuvenation process under aging condition, planning a time-based rejuvenation policy maximizing the system availability and minimizing downtime cost. The lower layer is a failure model that uses an analytical model and runtime measurements to build the failure distribution of the aging system. The upper layer is a rejuvenation model that takes the failure distribution from the lower layer as input to formulate the availability function and downtime cost function. Taking these two functions as optimization targets, we can obtain the optimal rejuvenation time. Compared with the traditional analytical model, the two-layer model modeling software failure considers runtime measurements, which can describe the aging behavior more accurately. In the experimental part, we comprehensively evaluate the two-layer model by studying the aging of the web search system. The results show that the two-layer model reduces unavailability by 18.18% and reduces downtime cost by 31.22% in comparison with the traditional analytical model.

In living bodies, pH values, which are precisely regulated and closely associated with diseased cells, can act as an efficient biologically intrinsic indicator for future intelligent biomedicine microsystems. In this work, we have developed flask-like carbonaceous nanomotors (FCNMs), via loading Fe3O4 nanoparticles (NPs) into a cavity, which exhibit a self-adaptive feature to a specific physiological pH by virtue of the pH-dependent dual enzyme-like activities of Fe3O4 NPs. Specifically, the peroxidase-like activity of Fe3O4 NPs in an acidic pH range, and the catalase-like activity in a near neutral and alkaline pH range, determine the products in the motion system (•OH, ions and O2), whose diffusions from the inner to the outside of the flask result in fluid movement providing the driving force for the movement of the FCNMs. Correspondingly, changes of the product concentrations and species in the physiological pH range (4.4–7.4) result, firstly, in velocity decrease and, then, with increase in pH, increase of the FCNMs occurs. Thanks to the non-linear velocity responsiveness, the FCNMs show intriguing pH taxis towards 6.8 (generally corresponding to the physiological pH in tumor microenvironments), where a maximum velocity appears. Furthermore, the superparamagnetic feature of the Fe3O4 NPs simultaneously endows the FCNMs with the abilities to be magnetic-oriented and easily separated. This work could significantly increase the possibility of nanomotors for targeted therapy of tumors and next-generation biotechnological applications.

Mg-based micromotors have emerged as an extremely attractive artificial micro/nanodevice, but suffered from uncontrollable propulsion and limited motion lifetime, restricting the fulfillment of complex tasks. Here, we have demonstrated Mg-based micromotors composed of Mg microspheres asymmetrically coated with Pt and temperature-sensitive poly(N-isopropylacrylamide) (PNIPAM) hydrogel layers in sequence. They can implement different motion behaviors stemming from the driving mechanism transformation when encountering catalyzed substrates such as H2O2 and respond to both H2O2 concentration and temperature in aqueous environment. The as-constructed Mg-based micromotors are self-propelled by Pt-catalyzed H2O2 decomposition following the self-consuming Mg-H2O reaction. In this case, they could further generate bilateral bubbles and thus demonstrate unique self-limitation motion like hovering when the phase transformation of PNIPAM is triggered by decreasing temperature or when the H2O2 concentration after permeating across the PNIPAM hydrogel layer is high enough to facilitate bubble nucleation. Our work for the first time provides a stimuli-induced “hovering” strategy for self-propelled micromotors, which endows Mg-based micromotors with an intelligent response to the surroundings besides the significant extension of their motion lifetime.

Reducing energy consumption can bring various benefits, such as saving power, reducing operating costs and improving system reliability. Data centers afford huge energy costs. Reducing energy consumption and providing efficient quality of service (QoS) are the goals pursued by data centers. This paper aims to develop the scheduling strategy to reduce the energy consumption for latency-critical workload with the Dynamic Voltage Frequency Scaling technique. In this paper, we propose a two-stage strategy that dynamically schedules the CPU to run at the optimal frequency level and provides satisfactory QoS during the latency-critical workload execution. The two-stage strategy includes a static stage and a dynamic stage. In the static stage, a heuristic algorithm is developed to determine an optimal frequency level for different loads, at which the CPU consumes lower energy. However, due to the dynamic characteristics of the load, the determined frequency in the static stage may not guarantee QoS. Therefore, in the dynamic stage, a threshold method is proposed to fine-tune the frequency to ensure the QoS. The two stages are worked together to reduce energy consumption for latency- critical workload which has QoS constraint. A case study on the Web search application shows that our scheduling strategy is effective for energy-saving, with a reduction of more than 13% compared with the baseline strategy. [ABSTRACT FROM AUTHOR]