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Remote sensing image (RSI) scene classification aims to identify the semantic categories of remote sensing images based on their contents. Owing to the strong learning capability of deep convolutional neural networks (CNNs), RSI scene classification methods based on CNNs have drawn much attention and achieved remarkable performance. However, such outstanding deep neural networks are usually computationally expensive and time-consuming, making them impossible to apply on resource-constrained edge devices, such as the embedded systems used on drones. To tackle this problem, we introduce a novel pair-wise similarity knowledge distillation method, which could reduce the model complexity while maintaining satisfactory accuracy, to obtain a compact and efficient deep neural network for RSI scene classification. Different from the existing knowledge distillation methods, we design a novel distillation loss to transfer the valuable discriminative information, which could reduce the within-class variations and restrain the between-class similarity, from the cumbersome model to the compact model. This method could obtain the compact student model with higher performance compared with existing knowledge distillation methods in RSI scene classification. To be specific, we distill the probability outputs between sample pairs with the same label and match the probability outputs between the teacher and student models. Experiments on three public benchmark datasets for RSI scene classification, i.e., AID, UCMerced, and NWPU-RESISC datasets, verify that the proposed method could effectively distill the knowledge and result in a higher performance.

Summary: In microextrusion-based 3D bioprinting, shaping gel fibers online, i.e., in narrow tubes, benefits the structural maintenance after extrusion, but it is challenging for materials possessing slow sol-gel transition dynamics. Gelatin, for example, transforms into thermostable fibers via transglutaminase (TG) reaction in as much as 10 min. It causes dramatic flow resistance accumulation and shear stress increase in fluids moving along narrow tubes, resulting in channel clogging and cell detriments. In this study, we overcome the limitations by adopting cascade pumping and performing in a single peristaltic pump that comprises multi-channel pumping units. The pressure and shear stress reduction by over 1-fold are verified by finite element simulation; continuous gelatin fiber production and patterning in a substrate-free manner are achieved via slow online enzymatic cross-linking. The online fiber shaping can be scaled up by numbering up the pumping units and provides another paradigm for biomanufacturing.

Summary: Implanting cell-laden microgels repairs soft tissue injuries, with the co-occurrence of tissue remodeling and angiogenesis in the engrafts. Engineering microgels using native-tissue derived materials, such as collagen, remains a challenge because they are mostly slow gelling and not angiogenic. Herein, we report a fast-gelling and angiogenic collagen scaffold interlocked by ultra-long DNA (>100,000 nt) programmed with vascular endothelial growth factor (VEGF) aptamers. The DNA interlocking shortens the collagen gelation time by >30-fold, to 40 s and modulated by the microfluidic flows, but the viscoelasticity, biodegradability, and biochemical properties of native collagen remain unaltered. When encapsulated with cells, the angiogenic microgels improve wound healing and liver regeneration, compared with the non-angiogenic or acellular microgels. After cryopreservation, the cell-laden microgels are retrieved with >80% of the cells viable, and they retain their therapeutic potential. These angiogenic, fast-gelling, and ultra-soft cell-laden microgels may be promising candidates as injectable therapeutics in regenerative medicine.

A multicomponent reaction is established toward quinazolimine derivatives, but it requires intensive human interventions in conventional reaction systems to suppress the side reactions. Herein, an automated microfluidics‐based reaction system that uses a program to command the fluid conductance to perform a cascade of reactions and separation in a defined sequence is innovated. The system is composed of chained reaction modules. Within the individual reaction module, side reactions are suppressed by creating an extremely high concentration bias between the reagents via selective reagent diffusion through the porous channel wall. The reaction sequence is commanded by transporting reagents from one module to the following ones, obtaining quinazolimine derivatives with high yield and purity. The system is low cost, easy to compose, program commanded, and requires minimal human intervention once the reaction modules are assembled and reagents are loaded.

The binary typhoon systems of Lekima and Krosa (2019) have been simulated using the Weather Research and Forecast (WRF) model with 27 km, 9 km, and 3 km resolutions and their behaviors of moist potential vorticity (MPV) in these simulations have been analyzed. All the simulations clearly show that the main sources of instability energy include two parts of MPV—the convective energy from the environmental field (MPV1) and the internal transferring energy between the binary typhoons (MPV2). While typhoons absorb the convective energy from the upper ocean through vortex effects on their periphery as the main external MPV sources, there is an MPV conveying belt at the middle-low layer of the atmosphere in the binary typhoon system, transferring energy between binary typhoons and causing atmospheric baroclinicity changes. While all three-resolution simulations show the fundamental features of MPV and its transferring in the binary typhoon system, the 3 km resolution cloud-resolving simulation yield more detailed and accurate MPV structures inside the individual typhoon, which may have non-negligible feedbacks on the background. Based on the comprehensive characteristics of MPV in binary typhoon interactions, further understanding of the interactions of internal detailed structures inside typhoons and their feedbacks on the background may provide a theoretical basis for improving the forecast of typhoon intensity and track.

Unmanned Aerial Vehicles (UAV) are widely used in disaster relief and road exploration in recent years. This paper mainly studied the emergency response of UAVs after disasters. The UAV response system is mainly suitable for the distribution of necessities and road exploration after geological disasters and tsunamis in coastal areas. By analyzing the problem and making reasonable assumptions, the optimization model was established with the traffic planning theory, and MATLAB software was used to program and solve the problem. An optimal scheduling scheme was presented to solve these problems. The normalization method was used to select a highly capable UAV. Taking the minimum volume of idle space buffer material as the objective function and taking into account the constraints, such as payload of unmanned aerial vehicle, a single objective programming model was established. The results are as follows: Each International Standards Organization (ISO) cargo container has five UAVs B, one UAV C, one UAV F and one UAV H. It provides 188 days of relief requirements with ISO cargo containers’ space utilization of 71.4%. The research shows that the UAV response system has the functions of necessities distribution and road exploration after disasters, and can be used to deal with the emergency response after disasters in coastal areas, and has a wide range of applicability.

The Madin–Darby bovine kidney (MDBK) cell line is currently used for the production of bovine alphaherpesvirus-1 (BoHV-1) vaccine. For the purpose of vaccine manufacturing, suspension cells are preferred over adherent ones due to simplified sub-cultivation and an easier scale-up process, both of which could significantly reduce production cost. This study aimed to establish a procedure for the culture of BoHV-1 in the suspended MDBK cell line in serum-free medium. We screened several commercially available serum-free media and chose ST503 for subsequent experiments. We successfully adapted the adherent MDBK cells to suspended growth in ST503 in the absence of serum. The maximum density of suspension-adapted MDBK cells could reach 2.5 × 107 cells/mL in ST503 medium with optimal conditions. The average size of suspension-adapted cells increased to 18 ± 1 µm from 16 ± 1 µm. Moreover, we examined tumorigenicity of the suspended cells and found no sign of tumorigenicity post adaptation. Next, we developed a protocol for the culture of BoHV-1 in the cell line described above and found that ultrasonic treatment could facilitate virus release and enhance virus yield by 11-fold, with the virus titer reaching 8.0 ± 0.2 log10TCID50/mL. Most importantly, the prototype inactivated BoHV-1 vaccine we generated using the suspension cultures of MDBK cells induced neutralizing antibodies to a titer comparable to that of the commercial inactivated BoHV-1 vaccine. Overall, we established and optimized a protocol for the production of inactivated BoHV-1 vaccine in MDBK cells adapted for suspension culture, which provides insights for future large-scale manufacturing of BoHV-1 vaccine.

The accurate prediction of electricity-heat-cooling-gas loads on the demand side in the integrated energy system (IES) can provide significant reference for multiple energy planning and stable operation of the IES. This paper combines the multi-task learning (MTL) method, the Bootstrap method, the improved Salp Swarm Algorithm (ISSA) and the multi-kernel extreme learning machine (MKELM) method to establish the uncertain interval prediction model of electricity-heat-cooling-gas loads. The ISSA introduces the dynamic inertia weight and chaotic local searching mechanism into the basic SSA to improve the searching speed and avoid falling into local optimum. The MKELM model is established by combining the RBF kernel function and the Poly kernel function to integrate the superior learning ability and generalization ability of the two functions. Based on the established model, weather, calendar information, social–economic factors, and historical load are selected as the input variables. Through empirical analysis and comparison discussion, we can obtain: (1) the prediction results of workday are better than those on holiday. (2) The Bootstrap-ISSA-MKELM based on the MTL method has superior performance than that based on the STL method. (3) Through comparing discussion, we discover the established uncertain interval prediction model has the superior performance in combined electricity-heat-cooling-gas loads prediction.

Abstract Immuno-inflammation has been shown to play a pivotal role in the pathogenesis of moyamoya disease (MMD). However, how did circulating Treg/Th17 cells involve in MMD patients remains unclear. 26 MMD, 21 atherothrombotic stroke, and 32 healthy controls were enrolled in this study. MMD patients have a significantly higher percentage of circulating Treg and Th17 cells as well as their dominantly secreting cytokines than other groups (P

We consider the problem of sampling pulse streams with known shapes. The recent finite rate of innovation (FRI) framework has shown that such signals can be sampled with perfect reconstruction at their rate of innovation, which is usually much lower than the Nyquist rate. Although FRI sampling of pulse streams was treated in various works, either the work was unstable for high rate of innovation, or the sampling stage was complex and redundant. In this paper, we propose an FRI sampling and recovery method for pulse streams, which is based on the real parts of the Fourier coefficients. The proposed method is simple and efficient, and leads to stable recovery even when the rate of innovation is very high. This is achieved through modulating the input signal in each channel with a properly chosen cosine signal, followed by filtering with a low-pass filter. Since the modulating process will lead to the signal spectrum aliasing, we propose a spectrum de-aliasing algorithm to solve this problem, resulting in the real parts of a band of Fourier coefficients from each two channels. Combining with the multi-channel sampling structure, we propose a more efficient way to obtain arbitrary frequency bands from the aliased spectrum, which improves the utility of the signal spectrum. By using a sparsity-based recovery algorithm, the time delays and amplitudes of the pulse streams can be recovered from the obtained real parts of the Fourier coefficients. Finally, simulation results have shown that the proposed scheme is flexible and exhibits better noise robustness than previous approaches.

Bovine viral diarrhea virus (BVDV) is an important animal pathogen that affects cattle. Infections caused by the virus have resulted in substantial economic losses and outbreaks of BVDV are reported globally. Virus-like particles (VLPs) are promising vaccine technology largely due to their safety and strong ability to elicit robust immune responses. In this study, we developed a strategy to generate BVDV-VLPs using a baculovirus expression vector system (BEVS). We were able to assemble BVDV-VLPs composed of dimerized viral proteins E2 and Erns, and the VLPs were spherical particles with the diameters of about 50 nm. Mice immunized with 15 μg of VLPs adjuvanted with ISA201 elicited higher levels of E2-specific IgG, IgG1, and IgG2a antibodies as well as higher BVDV-neutralizing activity in comparison with controls. Re-stimulation of the splenocytes collected from mice immunized with VLPs led to significantly increased levels of CD3+CD4+T cells and CD3+CD8+T cells. In addition, the splenocytes showed dramatically enhanced proliferation and the secretion of Th1-associated IFN-γ and Th2-associated IL-4 compared to that of the unstimulated control group. Taken together, our data indicate that BVDV-VLPs efficiently induced BVDV-specific humoral and cellular immune responses in mice, showing a promising potential of developing BVDV-VLP-based vaccines for the prevention of BVDV infections.

The NH3-SCR activities and hydrothermal stabilities of five xCu/MCM-22 zeolites with different Cu loadings (x = 2–10 wt%) prepared by incipient wetness impregnation method were systematically investigated. The physicochemical properties of xCu/MCM-22 zeolites were analyzed by XRD, nitrogen physisorption, ICP-AES, SEM, NH3-TPD, UV-vis, H2-TPR and XPS experiments. The Cu species existing in xCu/MCM-22 are mainly isolated Cu2+, CuOx and unreducible copper species. The concentrations of both isolated Cu2+ and CuOx species in xCu/MCM-22 increase with Cu contents, but the increment of CuOx species is more distinct, especially in high Cu loadings (>4 wt%). NH3-SCR experimental results demonstrated that the activity of xCu/MCM-22 is sensitive to Cu content at low Cu loadings (≤4 wt%). When the Cu loading exceeds 4 wt%, the NH3-SCR activity of xCu/MCM-22 is irrelevant to Cu content due to the severe pore blockage effects caused by aggregated CuOx species. Among the five xCu/MCM-22 zeolites, 4Cu/MCM-22 with moderate Cu content has the best NH3-SCR performance, which displays higher than 80% NOx conversions in a wide temperature window (160–430 °C). Furthermore, the hydrothermal aging experiments (xCu/MCM-22 was treated at 750 °C for 10 h under 10% water vapor atmosphere) illustrated that all the xCu/MCM-22 zeolites exhibit high hydrothermal stability in NH3-SCR reactions.

The synthesis of cyclic carbonates from carbon dioxide (CO2) and epoxides is a 100% atom economical reaction and an attractive pathway for CO2 utilisation. Because CO2 is a thermodynamically stable molecule, the use of catalysts is mandatory in reducing the activation energy of the CO2 conversion. Considering environmental compatibility and the high-efficiency catalytic conversion of CO2, there is the strong need to develop green catalysts. Biomass-based catalysts, a type of renewable resource, have attracted considerable attention due to their unique properties—non-toxic, low-cost, pollution-free, etc. In this review, recent advances in the development of biomass-based catalysts for the synthesis of cyclic carbonates by CO2 and epoxides coupling are summarized and discussed in detail. The effect of biomass-based catalysts, functional groups, reaction conditions, and co-catalysts on the catalytic efficiency and selectivity of synthesizing cyclic carbonates process is discussed. We intend to provide a comprehensive understanding of recent experimental and theoretical progress of CO2 and epoxides coupling reaction and pave the way for both CO2 conversion and biomass unitization.

This study introduces a coordinated low-voltage ride through (LVRT) control method for permanent magnet synchronous generator (PMSG) wind turbines (WT) interconnected with an energy storage system (ESS). In the proposed method, both the WT pitch and power converters are controlled to enhance the LVRT response. Moreover, the ESS helps in regulating the dc link voltage during a grid fault. Previous LVRT methods can be categorized into strategies with or without an additional device for the LVRT. The latter scheme is advantageous from the perspective of no additional installation cost; in this case, pitch and converter controllers are used. Meanwhile, the former method uses an additional device for LVRT operation and hence, involves additional expense. However, it can effectively enhance the LVRT response by reducing the LVRT burden on the WT. Moreover, the additional device can be used for various WT power control applications and it is common that the ESS is interconnected to the WT for multiple objectives. Previous studies focused on these two aspects separately; hence, a method of coordinated control for an ESS and a WT is needed as more ESSs are required to connect to WTs for flexible wind power operation. The proposed method introduces a control method with different LVRT modes considering the ESS state of charge (SoC). When the WT does not have a sufficient inertial response operation range, the ESS reserve energy capacity is required for LVRT operation. This coordinated LVRT method employs both the WT and ESS controls when it is hard to handle the LVRT using the WT control alone at high wind speeds. In this case, power curve analysis is used to obtain the appropriate power reference during the fault period. In addition, a power reference is also used to ensure a safe operation. Using the proposed method, an ESS can be operated in a manner that is appropriate for WT operation, especially at high wind speeds. To validate the effectiveness of the proposed method, we considered two case studies. One study compares the LVRT response between the WT itself and the proposed method. The other research work compares the response of the conventional LVRT method that uses a WT and an ESS and with that of the proposed method. From these case studies, we concluded that the proposed method achieved a better performance while operating within the constraints of the WT rotor speed and ESS SoC limits.

Microgrid is a good option to integrate renewable energy sources (RES) into power systems. In order to deal with the intermittent characteristics of the renewable energy based distributed generation (DG) units, a fuzzy-logic based coordinated control strategy of a battery energy storage system (BESS) and dispatchable DG units is proposed for the microgrid management system (MMS). In the proposed coordinated control strategy, the BESS is used to minimize active power exchange at the point of common coupling of the microgrid for grid-connected operation, and is used for frequency control for island operation. The efficiency of the proposed control strategy was tested by case studies using DIgSILENT/ PowerFactroy.

The new round of electricity market reform in 2015 completely changed the profit pattern of power grid enterprises (PGEs) in China, and directly affected their investment plans. Under the new electricity market reform (NEMR), the government regulatory authority made higher requirements for the investment efficiency of PGEs, and the investment effectiveness hence became the core criterion for investment plans. Therefore, the PGEs are now attaching great importance to the investment efficiency. According to their geographical differences, this paper divides the Chinese provincial PGEs into three groups, namely eastern, central and western region enterprises. Based on the NEMR, we developed an evaluation system of investment efficiency for the above-mentioned enterprises. Moreover, this paper selects GDP per capita, electricity consumption in industry, and electrification rate as external environment variables, and conducts an empirical research on the investment efficiency of 31 provincial PGEs in China in 2017. The analysis reveals that three external environment variables have considerable impacts on the investment efficiency. Though the increase of GDP per capita and electricity consumption in industry are not conducive to improving investment efficiency, the advancement of electrification plays a positive role in its improvement. And from the real efficiency results, Tianjin, Liaoning, Jiangsu, and Fujian have relatively higher investment efficiency, while Henan, Shandong, and Shanghai exhibit lower investment efficiency. By comparing the investment efficiency of PGEs in the first and third stage, conclusions can be drawn that in the first stage the investment efficiency of PGEs was overestimated, and the inefficient investments prevailed some provincial PGEs, which caused by low scale efficiency.

With the increasing development of renewable resources-based electricity generation and the construction of wind-photovoltaic-energy storage combination exemplary projects, the intermittent and fluctuating nature of renewable resources exert great challenges for the power grid to supply electricity reliably and stably. An energy storage system (ESS) is deemed to be the most valid solution to deal with these challenges. Considering the various types of ESSs, it is necessary to develop a comprehensive assessment framework for selecting appropriate energy storage techniques in establishing exemplary projects combining renewable resources-based electricity generation and an ESS. This paper proposes a multi-criteria decision making (MCDM) model combining a fuzzy-Delphi approach to establish the comprehensive assessment indicator system, the entropy weight determination method, and the best-worst method (BWM) to calculate weights of all sub-criteria, and a Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) comprehensive evaluation model to choose the optimal battery ESS. In accordance with the comprehensive evaluation results, the Li-ion battery is the optimal battery ESS to apply to wind-photovoltaic-energy storage combination exemplary projects. Based on the discussion on the comprehensive evaluation results, policy implications are suggested to improve the applicability of battery ESSs and provide some references for decision makers in related fields.

As useful supplements and effective support for large-scale electric power networks, micro-grid systems are the development tendency of future electric power systems. The planning performance of a micro-grid not only affects its security, reliability and economy, but also has a profound influence on the stable operation of large-scale electric power networks with the increasing penetration of micro-grids. Hence, studies related to micro-grid planning program evaluation are of great significance. This paper established a novel multi-criteria decision making (MCDM) model combining the best-worst method (BWM), the entropy weighting approach, and grey cumulative prospect theory for optimum selection of micro-grid planning programs. Firstly, an evaluation index system containing 18 sub-criteria was built from the perspectives of economy, electricity supply reliability and environmental protection. Secondly, the weights of sub-criteria were calculated integrating the subjective weights judged by the BWM and the objective weights computed by the entropy weighting method. Then, the cumulative prospect theory (CPT) combined with grey theory was employed to select the optimal micro-grid planning program. The empirical result indicates that the program with 100 kWp photovoltaic power generation unit, 200 kW wind power generation unit and 600 kWh NaS battery energy storage system is the optimal micro-grid planning program. To verify the robustness of obtained result, a sensitivity analysis related to values change of parameters under different risk preferences was conducted, and the result indicates that the selected optimal micro-grid planning program will not be influenced by various risk preferences of decision makers (DMs) and investors. The novel MCDM proposed in this paper is applicable and feasible in the micro-grid planning programs evaluation and selection.

As one of the most promising kinds of the renewable energy power, wind power has developed rapidly in recent years. However, wind power has the characteristics of intermittency and volatility, so its penetration into electric power systems brings challenges for their safe and stable operation, therefore making accurate wind power forecasting increasingly important, which is also a challenging task. In this paper, a new hybrid wind power forecasting method, named the BND-ALO-RVM forecaster, is proposed. It combines the Beveridge-Nelson decomposition method (BND), relevance vector machine (RVM) and ant lion optimizer (ALO). Considering the nonlinear and non-stationary characteristics of wind power data, the wind power time series were firstly decomposed into deterministic, cyclical and stochastic components using BND. Then, these three decomposed components were respectively forecasted using RVM. Meanwhile, to improve the forecasting performance, the kernel width parameter of RVM was optimally determined by ALO, a new Nature-inspired meta-heuristic algorithm. Finally, the wind power forecasting result was obtained by multiplying the forecasting results of those three components. The proposed BND-ALO-RVM wind power forecaster was tested with real-world hourly wind power data from the Xinjiang Uygur autonomous region in China. To verify the effectiveness and feasibility of the proposed forecaster, it was compared with single RVM without time series decomposition and parameter optimization, RVM with time series decomposition based on BND (BND-RVM), RVM with parameter optimization (ALO-RVM), and Generalized Regression Neural Network with data decomposition based on Wavelet Transform (WT-GRNN) using three forecasting performance criteria, namely MAE (Mean Absolute Error), MAPE (Mean Absolute Percentage Error) and RMSE (Root Mean Square Error). The results indicate the proposed BND-ALO-RVM wind power forecaster has the best forecasting performance of all the tested options, which confirms its validity.

Over the past three decades, China’s economy has witnessed remarkable growth, with an average annual growth rate over 9%. However, China also faces great challenges to balance this spectacular economic growth and continuously increasing energy use like many other economies in the world. With the aim of designing effective energy and environmental policies, policymakers are required to master the relationship between energy consumption and economic growth. Therefore, in the case of North China, a multivariate model employing panel data analysis method based on the Cobb-Douglas production function which introduces electricity consumption as a main factor was established in this paper. The equilibrium relationship and causal relationship between real GDP, electricity consumption, total investment in fixed assets, and the employment were explored using data during the period of 1995–2014 for six provinces in North China, including Beijing City, Tianjin City, Hebei Province, Shanxi Province, Shandong Province and Inner Mongolia. The results of panel co-integration tests clearly state that all variables are co-integrated in the long term. Finally, Granger causality tests were used to examine the causal relationship between economic growth, electricity consumption, labor force and capital. From the Granger causality test results, we can draw the conclusions that: (1) There exist bi-directional causal relationships between electricity consumption and real GDP in six provinces except Hebei; and (2) there is a bi-directional relationship between capital input and economic growth and between labor force input and economic growth except Beijing and Hebei. Therefore, the ways to solve the contradiction of economic growth and energy consumption in North China are to reduce fossil energy consumption, develop renewable and sustainable energy sources, improve energy efficiency, and increase the proportion of the third industry, especially the sectors which hold the characteristics of low energy consumption and high value-added.

Accurate and reliable forecasting on annual electricity consumption will be valuable for social projectors and power grid operators. With the acceleration of electricity market reformation and the development of smart grid and the energy Internet, the modern electric power system is becoming increasingly complex in terms of structure and function. Therefore, electricity consumption forecasting has become a more difficult and challenging task. In this paper, a new hybrid electricity consumption forecasting method, namely grey model (1,1) (GM (1,1)), optimized by moth-flame optimization (MFO) algorithm with rolling mechanism (Rolling-MFO-GM (1,1)), was put forward. The parameters a and b of GM (1,1) were optimized by employing moth-flame optimization algorithm (MFO), which is the latest natured-inspired meta-heuristic algorithm proposed in 2015. Furthermore, the rolling mechanism was also introduced to improve the precision of prediction. The Inner Mongolia case discussion shows the superiority of proposed Rolling-MFO-GM (1,1) for annual electricity consumption prediction when compared with least square regression (LSR), GM (1,1), FOA (fruit fly optimization)-GM (1,1), MFO-GM (1,1), Rolling-LSR, Rolling-GM (1,1) and Rolling-FOA-GM (1,1). The grey forecasting model optimized by MFO with rolling mechanism can improve the forecasting performance of annual electricity consumption significantly.