This paper presents a new direct active and reactive power control (DPC) of grid-connected doubly fed induction generator (DFIG)-based wind turbine systems. The proposed DPC strategy employs a nonlinear sliding-mode control scheme to directly calculate the required rotor control voltage so as to eliminate the instantaneous errors of active and reactive powers without involving any synchronous coordinate transformations. Thus, no extra current control loops are required, thereby simplifying the system design and enhancing the transient performance. Constant converter switching frequency is achieved by using space vector modulation, which eases the designs of the power converter and the ac harmonic filter. Simulation results on a 2-MW grid-connected DFIG system are provided and compared with those of classic voltage-oriented vector control (VC) and conventional lookup table (LUT) DPC. The proposed DPC provides enhanced transient performance similar to the LUT DPC and keeps the steady-state harmonic spectra at the same level as the VC strategy. Index Terms-Constant switching frequency, direct power control (DPC), doubly fed induction generators (DFIGs), sliding-mode control (SMC), wind power. NOMENCLATURE I s , I r Stator, rotor current vectors. L m Mutual inductance. L s , L rStator, rotor self-inductances. L σ s , L σ r Stator, rotor leakage inductances. P s , Q s Stator output active and reactive powers. R s , R r Stator, rotor resistances. U s , U r Stator, rotor voltage vectors. θ r Rotor angle. ψ s , ψ r Stator, rotor flux linkage vectors. ω 1 , ω r , ω slip Stator, rotor, and slip angular frequencies. Subscripts α s , β sStationary α s β s axis. α r , β r Rotor α r β r axis.

Summary: Rapid advances in pharmaceutical sciences have brought ever-increasing interests in combined therapies for better clinical efficacy and safety, especially in cases of complicated and refractory diseases. Innovative experimental technologies and theoretical frameworks are being actively developed for multicomponent drug research. In this work, we present the Drug Combination Database, with aims to facilitate analyses of known drug combinations, to summarize patterns of beneficial drug interactions, and to provide a basis for theoretical modeling and simulation of such drug interactions. Its current version (1.0) collected 499 approved or investigational drug combinations, including 40 unsuccessful drug combinations, involving 485 individual drugs, from >6000 references.

The virtual synchronous generator (VSG) was proposed to emulate a synchronous machine's dynamics when integrating power electronic converter-based distributed energy resources to the power grid. However, the VSG's synchronization stability during grid faults is not fully explored. The underlying mechanism of loss of synchronization (LOS) still needs to be further revealed due to VSG's nonlinear characteristics. In this paper, a step-by-step analytical method based on combining the linear and nonlinear models is proposed to analyze VSG's dynamic behaviors during a large disturbance. The relationship between the linear and nonlinear models is first brought to light, showing that the linear model is suitable for qualitative analysis to give an intuitive physical insight. Simultaneously, the latter is adopted for quantitative analysis to assess stability after a grid fault. Moreover, to avoid the conflict of the synchronization stability and the inertia response, a transient damping method is added in the active power control loop, which can simultaneously improve the synchronization stability and frequency stability. Design guidelines are also proposed to identify the parameter of the transient damping with different inertia requirements. Finally, the experimental results verify the analytical method and the theoretical analysis.

Diverse forms of resistance to anticancer drugs can lead to the failure of chemotherapy. Drug resistance is one of the most intractable issues for successfully treating cancer in current clinical practice. Effective clinical approaches that could counter drug resistance by restoring the sensitivity of tumors to the targeted agents are urgently needed. As numerous experimental results on resistance mechanisms have been obtained and a mass of high-throughput data has been accumulated, mathematical modeling and computational predictions using systematic and quantitative approaches have become increasingly important, as they can potentially provide deeper insights into resistance mechanisms, generate novel hypotheses or suggest promising treatment strategies for future testing. In this review, we first briefly summarize the current progress of experimentally revealed resistance mechanisms of targeted therapy, including genetic mechanisms, epigenetic mechanisms, posttranslational mechanisms, cellular mechanisms, microenvironmental mechanisms and pharmacokinetic mechanisms. Subsequently, we list several currently available databases and Web-based tools related to drug sensitivity and resistance. Then, we focus primarily on introducing some state-of-the-art computational methods used in drug resistance studies, including mechanism-based mathematical modeling approaches (e.g. molecular dynamics simulation, kinetic model of molecular networks, ordinary differential equation model of cellular dynamics, stochastic model, partial differential equation model, agent-based model, pharmacokinetic-pharmacodynamic model, etc.) and data-driven prediction methods (e.g. omics data-based conventional screening approach for node biomarkers, static network approach for edge biomarkers and module biomarkers, dynamic network approach for dynamic network biomarkers and dynamic module network biomarkers, etc.). Finally, we discuss several further questions and future directions for the use of computational methods for studying drug resistance, including inferring drug-induced signaling networks, multiscale modeling, drug combinations and precision medicine.

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Herein, we have developed a novel approach to investigate the mechanism of bone regeneration in a porous biodegradable calcium phosphate (CaP) scaffold by a combination of a multi-scale agent-based model, experimental optimization of key parameters and experimental data validation of the predictive power of the model. The advantages of this study are that the impact of mechanical stimulation on bone regeneration in a porous biodegradable CaP scaffold is considered, experimental design is used to investigate the optimal combination of growth factors loaded on the porous biodegradable CaP scaffold to promote bone regeneration and the training, testing and analysis of the model are carried out by using experimental data, a data-mining algorithm and related sensitivity analysis. The results reveal that mechanical stimulation has a great impact on bone regeneration in a porous biodegradable CaP scaffold and the optimal combination of growth factors that are encapsulated in nanospheres and loaded into porous biodegradable CaP scaffolds layer-by-layer can effectively promote bone regeneration. Furthermore, the model is robust and able to predict the development of bone regeneration under specified conditions.

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