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Drawing on case study evidence, this article explores the motivations a small liberal arts university internationalize and the challenges in facilitating internationalization. It employs qualitative methodology with qualitative data were collected from organizational documents, interviews, and field observations. The findings suggest that though a strong missionary is the most significant motivation for expansion, other internal and external factors impact the international decision-making process.

Due to high computation complexity, traditional design methods for LLC converter usually consider limited kinds of performance, and the process for searching the optimal parameter scheme is discrete, which might cause the missing of real optimal solution. In order to solve these problems, a multi‐objectives optimization design method for the LLC converter is proposed in this paper. According to the numerical calculation method, a closed parameter space for resonant parameters is established under the constraints of operation mode, switching frequency, zero voltage switching (ZVS), and voltage stress of resonant capacitor. Besides, the root‐mean‐square (RMS) value of resonant current and secondary current, as well as the core loss of the transformer, are selected as the optimization objectives. Non‐dominated sorting genetic algorithm (NSGA‐II) is utilized to deal with these multi‐objective formulations. In order to reduce the computation complexity, a data‐driven method, called adaptive polynomial approximation (APA), is selected to obtain the explicit expressions of the parameter space and the optimization objectives. Then, by substituting this simplified surrogate model into the NSGA‐II algorithm, the optimal parameter scheme is obtained. The comprehensive comparison analysis is performed, and a 400 W/48 V experimental prototype was built to verify the theoretical analysis, which shows that the proposed method features the highest efficiency of 95%. [ABSTRACT FROM AUTHOR]

The rational design of efficient catalysts for uric acid (UA) electrooxidation, as well as the establishment of structure‐activity relationships, remains a critical bottleneck in the field of electrochemical sensing. To address these challenges, herein, a hybrid catalyst that integrates carbon‐supported Pt nanoparticles and nitrogen‐coordinated Mn single atoms (PtNPs/MnNC) is developed. The metal‐metal interaction during annealing affords the construction of metallic‐bonded Pt−Mn pairs between PtNPs and Mn single atoms, facilitating the electron transfer from PtNPs to the support and thereby optimizing the electronic structure of catalysts. More importantly, experiments and theoretical calculations provide visual proof for the 'incipient hydrous oxide adatom mediator' mechanism for UA oxidation. The Pt−Mn pairs first adsorb OH* to construct the bridged Pt−OH−Mn mediators to serve as a highly active intermediate for N−H bond dissociation and proton transfer. Benefiting from the unique electronic and geometric structure of the catalytic center and reactive intermediates, PtNPs/MnNC exhibits superior electrooxidation performance. The electrochemical sensor based on PtNPs/MnNC enables sensitive detection and discrimination of UA and dopamine in serum samples. This work offers new insights into the construction of novel electrocatalysts for sensitive sensing platforms. [ABSTRACT FROM AUTHOR]

Mask-free multi-photon lithography enables the fabrication of arbitrary nanostructures low cost and more accessible than conventional lithography. A major challenge for multi-photon lithography is to achieve ultra-high precision and desirable lateral resolution due to the inevitable optical diffraction barrier and proximity effect. Here, we show a strategy, light and matter co-confined multi-photon lithography, to overcome the issues via combining photo-inhibition and chemical quenchers. We deeply explore the quenching mechanism and photoinhibition mechanism for light and matter co-confined multiphoton lithography. Besides, mathematical modeling helps us better understand that the synergy of quencher and photo-inhibition can gain a narrowest distribution of free radicals. By using light and matter co-confined multiphoton lithography, we gain a 30 nm critical dimension and 100 nm lateral resolution, which further decrease the gap with conventional lithography. Mask-free multi-photon lithography allows the straightforward fabrication of nanostructures, but high precision and good resolution can be challenging to achieve. Here, the authors report a combination of photo-inhibition and chemical quenchers for improved lithography performance. [ABSTRACT FROM AUTHOR]

Engineering isolated metal sites resembling the primary coordination sphere of metallocofactors enables atomically dispersed materials as promising nanozymes. However, most existing nanozymes primarily focus on replicating specific metallocofactors while neglecting other supporting cofactors within active pockets, leading to reduced electron transfer (ET) efficiency and thus inferior catalytic performances. Herein, we report a metal–organic framework UiO‐67 nanozyme with atomically dispersed iron sites, which involves multiple tailored enzyme‐like nanocofactors that synergistically drive the ET process for enhanced peroxidase‐like catalysis. Among them, the linker‐coupled atomic iron site plays a critical role in substrate activation, while bare linkers and zirconia nodes facilitate the ET efficiency of intermediates. The synergy of three nanocofactors results in a 4.29‐fold enhancement compared with the single effort of isolated metal site‐based nanocofactor, holding promise in immunoassay for sensitive detection of chlorpyrifos. This finding opens a new way for designing high‐performance nanozymes by harmonizing various nanocofactors at the atomic and molecular scale. [ABSTRACT FROM AUTHOR]

As the most fundamental unit of photonic integration, optical waveguides offer the possibility of developing efficient and practical photonic chips by facilitating the on‐chip integration of devices with different functions. Femtosecond direct laser writing (FsDLW), as a burgeoning 3D microfabrication technology, can realize the rapid formation of arbitrary optical waveguides without any mask inside versatile materials, such as crystalline dielectric crystals, glasses, polymers, and photoresists. This significant material‐independence allows FsDLW to combine different materials and manufacturing processes to implement a cross‐platform solution. This review first describes the different optical loss types and suitable measurement methods for different applicable scenes, then summarizes the two fabrication mechanisms and points out the general direction for adjusting the waveguide properties by fabrication processes. Finally, the application fields and development prospects of different materials are discussed by overviewing the characteristics of different materials and the material selection preferences of different devices. With a panoramic view of the development, it is concluded with insights and perspectives of the future development of optical waveguides and processing technology via FsDLW. [ABSTRACT FROM AUTHOR]

Constructing a renewable energy-based power system has become an important development path for the power industry's low-carbon transformation. However, as the proportion of renewable energy generation (REG) increases, the power grid gradually changes to uncertainty. Technologies to address this issue have been introduced. However, the majority of existing reviews focus on specific uncertainty modeling approaches and applications, lacking the consideration of temporal and spatial interdependence. Therefore, this paper provides a comprehensive review of the uncertainty modeling of temporal and spatial interdependence. It includes the discrete and continuous stochastic process-based methods to address temporal interdependence, the correlation coefficient and copula functions in modeling spatial interdependence, and the Itô process and random fields theory to describe temporal and spatial interdependence. Finally, their applications in power system stability, control, and economic scheduling are summarized. [ABSTRACT FROM AUTHOR]

Reducing carbon emissions is a crucial way to achieve the goal of green and sustainable development. To accomplish this goal, electric vehicles (EVs) are considered system‐schedulable energy storage devices, suppressing the negative impact of the randomness and fluctuation of renewable energy on the system's operation. In this paper, a coordination control strategy aimed at minimising the carbon emissions of a distribution network between EVs, energy storage devices, and static var compensators (SVCs) is proposed. A model‐free deep reinforcement learning (DRL)‐based approach is developed to learn the optimal control strategy with the constraint of avoiding system overload caused by random EV access. The twin‐delayed deep deterministic policy gradient (TD3) framework is applied to design the learning method. After the model learning is completed, the neural network can quickly generate a real‐time low‐carbon scheduling strategy according to the system operating situation. Finally, simulation on the IEEE 33‐bus system verifies the effectiveness and robustness of this method. On the premise of meeting the charging demand of electric vehicles, this method can optimise the system operation by controlling the charge‐discharge process of EVs, effectively absorbing the renewable energy in the system and reducing the carbon emissions of the system operation. [ABSTRACT FROM AUTHOR]

Future power systems will face more extreme operating condition scenarios, and system emergency dispatch will face more severe challenges. The use of distributed control is a well-designed way to handle this. It enables multi-energy complementation by means of autonomous communication, which greatly improves the flexibility of the grid. First, in the context of global energy conservation and emission reduction, this paper adopts the energy usage method of "renewable energy is the main source of energy, supplemented by thermal power and energy storage" to reduce the system abandoned wind (light) rate while supplementing the energy storage capacity. Second, a consensus algorithm is added to the system while considering the coordination between thermal units and energy storage. An "interface" for autonomous communication between thermal units and energy storage is created using the incremental cost of each agent. To address the recurring issue of power imbalance during emergency dispatch of the system, the consensus algorithm is enhanced so that the communication interval varies with the unit rate. This is based on the climbing characteristics of each thermal power unit. Finally, the effectiveness of the proposed method is verified in an IEEE-30 bus system. [ABSTRACT FROM AUTHOR]

Dynamic power control of wind farms (WFs) is necessary to provide automatic generation control (AGC) services for the power system. However, cooperative WF control for AGC remains a great challenge because of the nonlinear and high-dimensional nature of the wake flow dynamics. To address this challenge, this article proposes a model predictive control (MPC) framework with deep learning-based reduced-order modeling (ROM). Two novel neural network architectures are designed, which successfully formulate a WF ROM capturing the dominant wake steering dynamics in a computationally efficient manner. Compared to physical models, the data-driven ROM reduces the number of model states by orders of magnitude. Then, a novel WF AGC framework embedding the derived WF ROM is proposed. Thrust coefficient and yaw steering are both employed to optimize WF power tracking performance. Compared to prior WF AGC controllers, the dynamic yaw actuation is first optimized for AGC considering the wake steering dynamics. Case studies validate the effectiveness of the deep learning-based WF ROM at capturing the wake traveling dynamics. The WF controllers were stress-tested under time-varying inflow directions. The proposed MPC can react to different wind directions and generates higher-quality control performance than existing alternatives with extended trackable AGC range and better dynamic power tracking performance. [ABSTRACT FROM AUTHOR]

The loss of small signal stability under parameter variation (e.g., fluctuation of loads and wind powers) can be ascribed to local bifurcations, i.e., saddle-node, Hopf, singularity-induced, and limit-induced bifurcations. Classic bifurcation calculation methods like the direct method and continuation method can only provide a bifurcation point or two-parameter bifurcation curve. Based on Galerkin method and the implicit function theorem, this paper proposes accurate polynomial approximations of bifurcation hypersurfaces in the multi-dimensional parameter space of interest. The proposed method can ensure high accuracy in this parameter space, and thus is preferable to the existing Taylor expansion-based local approximation method, given the intrinsic large-variation characteristic of wind powers. Acquired bifurcation hypersurfaces are immediately used to construct the small signal stability region of the continuous system, and then compute the stability margin for the operating point subjected to uncertainty of wind generation. Besides, the validity scope of the proposed method is analyzed, and the handling of limit-triggered equation switching as well as its computational difficulty is discussed. Computational results on the two-parameter 11-bus two-area and six-parameter IEEE 145-bus test systems validate the high accuracy and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Prognostic is a potential tool for improving the durability of solid oxide fuel cells (SOFCs), which usually involves building a degradation model for prediction. However, the existing degradation models based on parallel constant operation datasets are inaccurate for integration with operation optimization and control problems of SOFCs under varying-load operation due to the nonuniform degradation behaviors. To address this issue, a link function degradation model is proposed, and its parameters are identified online with a cyclic batch identification procedure based on the maximum likelihood method, which provides results representing the degradation trend on a timescale of 103 h. The link function takes the form of an empirical function, which describes how operating parameters affect the degradation and is easy to integrate with control designs. The existence of the link function is proven on the varying-load experiment datasets of two flat-chip SOFCs because it statistically improves the prediction accuracy and stability compared with a constant degradation speed model. Furthermore, the effectiveness of the proposed identification procedure for time-varying degradation behaviors on the timescale of 104 h is also validated with 30,000-h simulation datasets. • Accurate degradation models are built, considering degradation nonuniformity. • The model form is preferred by operation control/optimization applications. • 1700-h varying-load degradation experiment datasets of two cells are obtained. • The existence of the link function is proved on the experiment datasets. • The modeling procedure adapts to time-varying degradation characteristics. [ABSTRACT FROM AUTHOR]

Reflecting the uncertainty of renewable energy generations and loads, power system AGC signals are essentially random processes. For the sake of the optimal operation and control of the AGC participant, such as energy storage systems (ESSs) in a performance/mileage-based regulation market, taking the uncertain nature, especially the temporal correlation, of the AGC signals into consideration can be beneficial; hence, random process models of the AGC signals are needed. However, a continuous random process model of the AGC signal that jointly considers the probability distribution and the temporal correlation is still lacking. To fill this gap, this paper first presents a systematic methodology for modeling the continuous random processes of AGC signals based on stochastic differential equations (SDEs). It is shown that AGC signals may have a saturated stationary probability density function and a biexponential temporal correlation, which are very different from the renewable generations. To capture these special characteristics, SDEs are then carefully constructed, which are easy to use in optimization and control. Using the PJM traditional and dynamic regulation (RegD and RegA) signals and the signal received from a battery ESS (BESS) plant in Jiangsu, China for example, simulation shows that the SDE is able to simultaneously capture the probability distribution and temporal correlation accurately. [ABSTRACT FROM AUTHOR]

To enable power supply in rural areas and to exploit clean energy, fully renewable power systems consisting of cascaded run‐of‐the‐river hydropower and volatile energies such as photovoltaic (pv) and wind are built around the world. In islanded operation, the primary and secondary frequency controls, that are hydro governors and automatic generation control (AGC), are responsible for the frequency stability. However, due to limited water storage capacity of run‐of‐the‐river hydropower and river dynamics constraints, without coordination between the cascaded plants, the traditional AGC cannot fully exploit the adjustability of hydropower. When imbalances between the volatile energy and load occur, load shedding can be inevitable. To address this issue, this paper proposes a coordinated tertiary control approach by jointly considering power system dynamics and the river dynamics that couples the cascaded hydropower plants. To unify the multi‐timescale dynamics of the power system and river dynamics to establish a controller that coordinates the cascaded plants, the relation between AGC parameters and turbine discharge is approximated by a polynomial surrogate model. The cascaded plants are coordinated by optimising AGC participation factors in a receding‐horizon manner. Simulation of a real‐life system shows the proposed method significantly reduces load loss under pv volatility. [ABSTRACT FROM AUTHOR]

Wake effects impose significant aerodynamic interactions among wind turbines. To improve the wind farm operating performance, practical wind farm online control considering wake effects becomes very important. To achieve online optimal wind farm control while responding to grid demands, this paper proposes a novel optimal wind farm supervisory control (SC) model and its explicit solutions. From the controller modelling perspective, the two major wind farm operating modes, the maximum power point tracking mode and the set‐point tracking mode, are first analysed and unified in one optimisation model while considering wake effects. In this way, wind farm power production and rotor kinetic energy reserve can be simultaneously considered to conveniently modify the operation mode in response to different grid demands. Aside from controller modelling, the collocation method is first introduced to address the online application problem of such wake‐effect aware optimal WF control. Although a few optimisation algorithms have been proposed to find the optimum offline, online optimal control is still challenging because of the computational complexity brought by wake model non‐linearity and non‐convexity. The proposed collocation method explicitly approximates the optimal solutions to the proposed supervisory control model, through which only a direct algebraic operation is required for online optimal control instead of repeated optimisations. Case studies are carried out on different wind farms under various wind conditions, showing that the wind farm power production potential and releasable power reserve are improved compared to traditional greedy control in both modes. The accuracy of the collocation method is verified. A detailed analysis of the wind farm production capacity under different wind speeds and directions is also provided. [ABSTRACT FROM AUTHOR]

Transition metal oxides (TMOs) are promising materials for next‐generation lithium‐ion batteries while the lithium storage and cycling stability of TMOs are still limited. Herein, a novel Co3O4/MnO2/C composite using metal‐organic frameworks (MOFs) as sacrificial templates is reported. MnO2 and C shells are layer by layer coated on the MOF‐derived Co3O4/C samples via hydrothermal technique and heat treatment. When employed as anode materials for LIBs, the hierarchically heterostructured Co3O4/MnO2/C composites incorporate the merits of Co3O4, MnO2, and C, showing high Li storage performances at a current density of 0.2 A g−1 with an initial capacity of ∼1245.7 mAh g−1 and a reversible capacity of ∼854.9 mAh g−1 after 100 cycles, remarkable cycling stability, and excellent rate capability (almost 98 % recovery as the current density returns to the initial 0.2 A g−1, and especially, the specific capacity remains ∼587.6 mAh g−1 at a high current density of 2.0 A g−1). [ABSTRACT FROM AUTHOR]

To exploit the massive solar energy available in the region, photovoltaic plants have been built in the mountain areas in Southwest China, coexisting with many small cascaded run-of-the-river hydropower plants, interconnected by short-distance transmission lines. Network constraints inside these systems are often negligible. However, due to the volatile nature of solar power and the weak connection to the external power grid, without proper coordination, solar power curtailment and water spillage are inevitable. To address this issue, this paper proposes an ultra-short-term stochastic generation control method for cascaded hydropower to mitigate solar power volatility. Two technical challenges are specifically addressed: to tackle the time-varying stochastic volatility of solar power, the Itô process model is introduced; to characterize the spatial-temporal hydraulic coupling of the cascaded hydropower plants and river operation constraints, a state-space dynamic river model derived from shallow water equations is included. Followingly, a stochastic control (SC) approach for hydropower generation based on stochastic programming is proposed, where hydropower generation commands are parameterized as affine functions of measured solar power, enabling quick response to solar power volatility. Simulation of a real-life cascaded hydro-solar system and a modified system verifies the proposed control method. [ABSTRACT FROM AUTHOR]

To solve the problem associated with the huge volume change and low conductivity of Mn3O4, a novel C/Mn3O4 microsphere with Mn3O4 layer and void space sandwiched by nitrogen‐doped carbon (NC) is designed and fabricated. In this composite, the NC is obtained from the metal–organic frameworks (MOFs) and polydopamine (PDA). The preparation process is facile and effective without additional templates or redox solvents introduced. When used as anodes for lithium‐ion batteries (LIBs), the C/Mn3O4 composite incorporates the metrics of N‐doping, yolk‐shell structure, double carbon layer, mesoporous Mn3O4, showing an outstanding lithium storage of ≈1015.9 mAh g−1 at 0.2 A g−1 in the 250th cycle, high cycling stability (without clear capacity decay after 400 cycles), and excellent rate capability (capacity can fully recover as the current density returns back to the initial value of 0.2 A g−1). The yolk‐shell structure derived from MOFs can be expanded to fabricate other materials with improved performance. [ABSTRACT FROM AUTHOR]

The imbalance of generation and load caused by the increasing integration of volatile generations poses challenges on frequency regulation. AGC is required to respond to the generation fluctuations without violating operational and security constraints. Explicit model predictive control (EMPC) provides an approach to reaching such requirements, which calculates the control laws of MPC in an explicit form, allowing for offline validation of the controller and enabling fast online computation. However, the partition number of EMPC's piecewise affine control laws grows exponentially with the number of constraints and prediction/control horizons, which hinders its application in large systems. In this paper, we propose an alternative explicit control approach for AGC by approximating the control laws of EMPC using Legendre polynomial series expansions, thus entirely eliminating the partition issue. The Galerkin method is applied to the KKT conditions of EMPC's multiparametric quadratic programming (mp-QP) problem to compute the approximation. Case studies in an illustrative system and IEEE 118-Bus System verify the performance and efficiency of the proposed controller. [ABSTRACT FROM AUTHOR]

Parametric problems in the transient analysis refer to the investigations of the relationship between parameters and dynamic behavior. To solve parametric problems, we propose a Galerkin method-based polynomial approximation. The proposed method uses a series of polynomials combined with coefficients to approximately represent state variables with parameters, where the Galerkin method is introduced to determine the coefficients. Thereby, the relationship between parameters and dynamic behavior is established through the polynomial expressions. Because models in power system transient analysis are a set of complicated differential-algebraic equations, practical issues related to the non-polynomial model terms, parameterization of initial operation point are discussed in this paper. The proposed method is tested on a three-machine system and the IEEE 145-bus test system. The case studies show that compared to trajectory sensitivities, the proposed method can maintain high accuracy in cases of large parameter variations and strong non-linearity. We also illustrate that the validity of the proposed method for parametric problems like trajectory approximation, finding the critical parameter values and uncertainty quantification. [ABSTRACT FROM AUTHOR]

Power system static voltage stability region boundary (SVSRB) is a complicated hypersurface in parameter space. Due to inequality constraints such as generator reactive power limits, different critical points on the SVSRB may be related to different groups of active constraints and can be associated with different types of loadability limit, which makes it difficult to approximate the whole SVSRB at once or assemble many local approximations of the SVSRB. To overcome the difficulty, this paper proposes an approach for globally approximating the whole SVSRB considering generator reactive power limits at one go. First, a parametric optimization model incorporating all inequality constraints is formulated to describe the SVSRB, where the optimal solutions are the critical points on the SVSRB. The optimality condition, i.e., parametric Karush–Kuhn–Tucker conditions (KKT conditions), is also derived. Then, by applying the Galerkin method to the parametric KKT conditions in an interior-point-method-like manner, the whole SVSRB is approximated and represented using a single polynomial expression. Case studies in a 10-bus system and IEEE 300-bus system verify the effectiveness and accuracy of the proposed method. [ABSTRACT FROM AUTHOR]

A novel method for globally approximating the static voltage stability region boundaries (SVSRBs) of power systems is proposed by applying parametric polynomial approximation to the criterion equation which defines the SVSRB. Known as the Galerkin method, the implicit function portraying the SVSRB is described as a linear combination of basis polynomial functions, and the coefficients of the basis functions are obtained by projecting the SVSRB criterion onto each basis function. The approximation guarantees high precision globally in the whole domain of the parameter of interest rather than only in the neighborhood of a point, and the error can be controlled by the degree of polynomial basis functions. In the meantime, analytical expression of the left or right eigenvector of the system's Jacobian matrix corresponding to the zero eigenvalue is obtained in the form of polynomial, which provides valuable information for online voltage stability control or monitoring. Case studies in a 10-bus test system and IEEE 118-bus test system verifies the validity, accuracy, and flexibility of the proposed method. [ABSTRACT FROM AUTHOR]