Search

The whale optimization algorithm has several advantages, such as simple operation, few control parameters, and a strong ability to jump out of the local optimum, and has been used to solve various practical optimization problems. In order to improve its convergence speed and solution quality, a reinforced whale optimization algorithm (RWOA) was designed. Firstly, an opposition-based learning strategy is used to generate other optima based on the best optimal solution found during the algorithm's iteration, which can increase the diversity of the optimal solution and accelerate the convergence speed. Secondly, a dynamic adaptive coefficient is introduced in the two stages of prey and bubble net, which can balance exploration and exploitation. Finally, a kind of individual information-reinforced mechanism is utilized during the encircling prey stage to improve the solution quality. The performance of the RWOA is validated using 23 benchmark test functions, 29 CEC-2017 test functions, and 12 CEC-2022 test functions. Experiment results demonstrate that the RWOA exhibits better convergence accuracy and algorithm stability than the WOA on 20 benchmark test functions, 21 CEC-2017 test functions, and 8 CEC-2022 test functions, separately. Wilcoxon's rank sum test shows that there are significant statistical differences between the RWOA and other algorithms [ABSTRACT FROM AUTHOR]

Cognitive engagement is a crucial factor that shapes successful learning outcomes, but our understanding of the factors that influence such engagement in the smart classroom context remains limited. This study aims to narrow this research gap by exploring the relationships among college students' perceptions of the smart learning environment, perceived usefulness of mobile technology, achievement emotions, and cognitive engagement. A total of 1293 college students completed an online questionnaire survey, and 1076 valid responses were received. Structural equation modeling was used to analyze the interrelationships among these factors. The results revealed that students' perceptions of the smart classroom environment and perceived usefulness of mobile technology as well as two achievement emotions (pride and anxiety) significantly impact cognitive engagement. Both pride and anxiety act as mediators in the relationships among perceptions of smart classroom environments, the perceived usefulness of mobile technology, and cognitive engagement, in which context the mediating effect of pride is stronger than that of anxiety. These findings have practical implications for instructors, who should focus on implementing strategies that promote positive achievement emotions when students use mobile technology in smart classrooms. Additionally, these findings can inform the design and construction of smart classroom environments. Moreover, our study has limitations due to reliance on online data collection and self-reported data, which may introduce biases and measurement errors. Future research should incorporate multimodal data and advanced technologies for a comprehensive assessment to better understand students' engagement in smart learning environments, while also considering individual factors and the educational context to enhance the effectiveness of mobile technology in supporting students' emotions and achievement. [ABSTRACT FROM AUTHOR]

A simple equivalent circuit model (ECM) for a reflective polarization converter (RPC) comprising anisotropic metasurfaces is presented in this paper. The impedance matrix of an anisotropic metasurface in x and y axis is derived by the impedance matrix in a diagonal axis multiply with a rotated matrix, while the diagonal impedance matrix can be calculated by a simple L C network, which contains a series of capacitors and inductors. The presence of the dielectric substrates is considered using the transmission line theory. Some typical RPCs with different metasurface shapes are used to verify the effectiveness of the ECM. The methodology provides a simple approach for the design and optimization of RPC. Finally, we designed a polarization converter based on the proposed method, and the measured results are in good agreement with the theoretical results. [ABSTRACT FROM AUTHOR]

How to reduce a boiler's NOx emission concentration is an urgent problem for thermal power plants. Therefore, in this paper, we combine an evolution teaching-learning-based optimization algorithm with extreme learning machine to optimize a boiler's combustion parameters for reducing NOx emission concentration. Evolution teaching-learning-based optimization algorithm (ETLBO) is a variant of conventional teaching-learning-based optimization algorithm, which uses a chaotic mapping function to initialize individuals' positions and employs the idea of genetic evolution into the learner phase. To verify the effectiveness of ETLBO, 20 IEEE congress on Evolutionary Computation benchmark test functions are applied to test its convergence speed and convergence accuracy. Experimental results reveal that ETLBO shows the best convergence accuracy on most functions compared to other state-of-the-art optimization algorithms. In addition, the ETLBO is used to reduce boilers' NOx emissions by optimizing combustion parameters, such as coal supply amount and the air valve. Result shows that ETLBO is well-suited to solve the boiler combustion optimization problem. [ABSTRACT FROM AUTHOR]

At present, establishing a multidimensional characteristic model of a boiler combustion system plays an important role in realizing its dynamic optimization and real-time control, so as to achieve the purpose of reducing environmental pollution and saving coal resources. However, the complexity of the boiler combustion process makes it difficult to model it using traditional mathematical methods. In this paper, a kind of hyper-parameter self-optimized broad learning system by a sparrow search algorithm is proposed to model the NOx, SO2 emissions concentration and thermal efficiency of a circulation fluidized bed boiler (CFBB). A broad learning system (BLS) is a novel neural network algorithm, which shows good performance in multidimensional feature learning. However, the BLS has several hyper-parameters to be set in a wide range, so that the optimal combination between hyper-parameters is difficult to determine. This paper uses a sparrow search algorithm (SSA) to select the optimal hyper-parameters combination of the broad learning system, namely as SSA-BLS. To verify the effectiveness of SSA-BLS, ten benchmark regression datasets are applied. Experimental results show that SSA-BLS obtains good regression accuracy and model stability. Additionally, the proposed SSA-BLS is applied to model the combustion process parameters of a 330 MW circulating fluidized bed boiler. Experimental results reveal that SSA-BLS can establish the accurate prediction models for thermal efficiency, NOx emission concentration and SO2 emission concentration, separately. Altogether, SSA-BLS is an effective modelling method. [ABSTRACT FROM AUTHOR]

The majority–minority cellular automaton algorithm (MmCAA) is a novel optimization algorithm that has demonstrated its competitiveness compared to other algorithms. However, one of the main issues with MmCAA is the global exploration capability. It affects the convergence speed and the ability of the algorithm to solve problems in higher dimensions. To tackle this issue, a modified average-roulette cellular automaton algorithm (ARCAA) is proposed in this paper. ARCAA comprises two core rules: the average rule and the roulette rule. The combination of the two rules contributes to the well-rounded evolution of cellular states. In the individual cell exploitation phase, the cell state undergoes random and targeted disturbances and evolves in different directions. In the single neighbor exploration phase, the cell state is guided by reference values of randomly selected neighbors and adjusted in the appropriate directions. The performance of the proposed ARCAA is evaluated by 26 benchmark functions and three real-world engineering problems. It is compared with state-of-the-art optimization algorithms. Meanwhile, the significance of the comparisons is verified through the Wilcoxon signed-rank test. These results indicate that ARCAA effectively balances exploration and exploitation, showing strong potential for broader application in various optimization problems. [ABSTRACT FROM AUTHOR]

This paper deals with the robust pinning synchronization issue of uncertain fractional-order neural networks with discontinuous activations (FNNDAs) by means of the linear matrix inequalities (LMIs). In this paper, a class of FNNDAs model is presented. Moreover, an appropriate pinning controller is designed to ensure the error dynamical system gets robust Mittag–Leffler stability via Lyapunov function approach, non-smooth analysis theory and inequality analysis technique. In addition, the robust pinning synchronization conditions of FNNDAs drive system and FNNDAs response system are obtained in terms of the LMIs. Finally, a typical numerical simulation is provided to show the effectiveness of the obtained results. [ABSTRACT FROM AUTHOR]

This article studies the moving path following (MPF) problem where a desired path attached to a moving object must be followed. Focusing on the problem that the classical guidance vector field is not suitable for an MPF, we present a time-varying vector field guidance law, which is qualified for following a path expressed with respect to a moving target frame. Moreover, a prescribed performance method is embedded to the time-varying vector field law to further enhance the control performance. By combining the prescribed performance, the tracking errors are ensured not to exceed the predefined arbitrary small residual sets. Subsequently, the proposed guidance laws are applied to a typical MPF mission—the automatic carrier landing. An active disturbance rejection attitude controller is designed as the "low-level" controller with backstepping as the main frame. To eliminate the potential problem of external disturbances and parameter uncertainties, extended state observers are adopted in the controller. Besides, tracking differentiators are integrated with the controller to avoid complex derivative calculations in backstepping. Finally, comparative simulations are conducted, the results clarify and verify the proposed methods. [ABSTRACT FROM AUTHOR]

The existing size measurement systems cannot meet the requirements of non-contact measurement tasks, and the main challenge is how to detect various objects and improve measurement accuracy. In order to solve these problems, a novel measurement system was proposed in this paper. In the system, a three-camera model with variable baselines was designed based on the trinocular vision. Three binocular vision subsystems were composed of the three cameras, which were used to obtain the depth information from different shooting angles, and the baselines between the cameras could be adjusted according to the different objects. In the measurement process, the target object was detected automatically based on the visual saliency features and spatial information. Finally, the size of the target object was computed by the cooperative analysis of the three binocular vision subsystems. The experimental results demonstrated that the proposed system is accurate and stable in various objects’ detection and measurement tasks. [ABSTRACT FROM AUTHOR]

As the era of Internet of Things is approaching, huge number of devices need to be included in the network, and the long standby time of devices should also be satisfied. To solve these problems, in this paper, a relay-aided multiple access (RAMA) transmission scheme based on network coding is considered in joint transmission with two base stations (BSs). In the scheme, the near user acts as a two-way relay node between the BS and the far user based on machine-to-machine communications for both downlink and uplink transmissions. At first for two users, non-orthogonal multiple access (NOMA) is employed to compare with the proposed RAMA scheme in terms of the achievable sum rates. From the analysis, we can show that for NOMA and RAMA, one outperforms the other under different conditions. Then, we consider the case of multiple user pairs, for which an adaptive RAMA-NOMA transmission scenario is developed to make full use of their respective advantages. In the scenario, an optimal power allocation scheme based on the branch and bound (BB) algorithm and a low-complexity user pairing scheme based on a greedy algorithm are proposed. Numerical results show that our proposed adaptive RAMA-NOMA transmission scenario can achieve a higher sum rate compared with RAMA or NOMA only scheme, and under the optimal power allocation based on BB, the performance of our proposed user pairing scheme is close to that of the optimal pairing method using an exhaustive search, and significantly outperforms the random selection method. [ABSTRACT FROM AUTHOR]

This paper is focused on using plasma as element of a frequency selective surface (FSS). FSSs have been used for filtering electromagnetic waves for many years. Conventional FSSs use metal patch pattern as periodic element. This paper takes the plasma tube as a substitution for metal patch. The 3-D finite-difference time-domain method with periodic boundary condition is utilized to simulate the interaction of incident wave and plasma FSS. Numerical calculation results in that electron number density of plasma can dominate the resonance frequency obviously. The resonance frequency increases as the increasing of electron number density of plasma to the limit of that of perfectly electric conductor. Thus, the FSS can be designed to be tunable by changing the ionized electron number density. Both of the noncollision and collisional plasma model are introduced to study the FSS characteristics. The numerical calculation results show that the collision frequency only influences the reflectivity while has no effect on the resonant frequency. The resonant frequency and transmitted power ratio can be tuned by assigning the plasma’s electron number density and collision frequency. Thus, plasma elements offer the possibility of improved shielding effect along with reconfigurability. Plasma FSS can also be made transparent by tuning the plasma off which makes the use of FSS more versatile. [ABSTRACT FROM AUTHOR]

Purpose A numerical study on the aerodynamic noise generation of a high efficiency propeller is carried out.Design/methodology/approach Three-dimensional numerical simulation based on Reynolds averaged N-S model is performed to obtain the aerodynamic performance of the propeller. Then, the result of the aerodynamic analysis is given as input of the acoustic calculation. The sound is calculated using the Farassat 1A which was derived from Ffowcs Williams–Hawkings equation and is compared with the measurements.Findings Moreover, the fan is modified for noise reduction by changing its geometrical parameters such as span, chord length and torsion angle.Originality/value The variation trend of aerodynamic and acoustic are compared and discussed for different modification tasks. Some meaningful conclusions are drawn on the noise reduction of propeller. [ABSTRACT FROM AUTHOR]

Harris Hawks Optimization (HHO) is a novel meta-heuristic optimization algorithm. The algorithm is inspired by the behavior of Harris Hawks collaborating with each other to pursue prey in nature. The algorithm has the advantages of simple structure, fewer parameters, easy implementation, and excellent performance on high-dimensional problems. However, the algorithm also suffers from the inability to strike a good balance between exploration and exploitation, low convergence accuracy, and slow convergence speed in the early stage. In response to these defects, this paper will introduce three strategies to the HHO: a non-negative stochastic shrinkage exponential energy function, a Cauchy-Gaussian-based dynamic variance reduction selection strategy, and a greedy-difference-based selection strategy. The improved algorithm TSHHO is analyzed on the well-established 28 benchmark test functions, and four industrial engineering design problems. The experimental results show that the TSHHO algorithm proposed in this paper can achieve a better balance in the exploration and development stages,the strategies significantly improve the search efficiency, convergence accuracy, and robustness of the algorithm. [ABSTRACT FROM AUTHOR]

In this paper, a class of variable-order fractional convection diffusion equations have been solved with assistance of the second kind Chebyshev wavelets operational matrix. The operational matrix of variable-order fractional derivative is derived for the second kind Chebyshev wavelets. By implementing the second kind Chebyshev wavelets functions and also the associated operational matrix, the considered equations will be reduced to the corresponding Sylvester equation, which can be solved by some appropriate iterative solvers. Also, the convergence analysis of the proposed numerical method to the exact solutions and error estimation are given. A variety of numerical examples are considered to show the efficiency and accuracy of the presented technique. [ABSTRACT FROM AUTHOR]

In the last decade, the online sequential extreme learning machine (OS-ELM) has become an effective online modeling tool for the regression problem and time series prediction areas. However, the random initialization input-weights remain unchanged when the new large testing data arrive, which maybe reduce its training accuracy and generalization ability gradually. In this paper, based on the conventional OS-ELM, a kind of input data sample increment online sequential extreme learning machine is proposed, namely SIOS-ELM. As its name suggests, the sample increment is the actual error value between the present training input data sample and the new arriving input data sample. In SIOS-ELM, the parameters of hidden layer nodes (the input-weights and threshold values of hidden layer) are calculated in real time based on the sample increment by twice least square method when the new input data arrives one by one or chunk by chunk. In addition, the output weights are adjusted online as the OS-ELM. Compared with OS-ELM and its variants on benchmark problems, the proposed SIOS-ELM possesses better model accuracy and generalization ability. Additionally, the SIOS-ELM is applied to build the NOx emissions model of one 330 MW circulating fluidized bed boiler. The experiment result reveals that the SIOS-ELM is an effective online machine learning tool. [ABSTRACT FROM AUTHOR]

In this paper, reflection, absorption and transmission characteristics of nonuniform plasma layer with varying electron number density are analyzed. The plasma number density profile is parabolic. Finite-difference time-domain (FDTD) and subslabs approximation methods are utilized and the results are validated. In FDTD, we use auxiliary differential equation (ADE) to simulate the plasma dispersive characteristics. In subslabs method, the plasma layer is divided into several thin subslabs with constant electron number density in each subslab. Partial reflection coefficients at each subslab boundary with different electromagnetic parameters are calculated. The total reflected and transmitted coefficients are then deduced using transmission line method. The reflected, transmitted and absorption power ratio with respect to the incident power is acquired. Their functional dependence on the number density, collision frequency and the distribution of number density, collision frequency is studied. [ABSTRACT FROM AUTHOR]

Extreme learning machine (ELM) is a novel single hidden layer feed-forward network, which has become a research hotspot in various domains. Through in-depth analysis on ELM, there are four factors mainly affect its model performance, such as the input data, the input weights, the number of hidden layer nodes and the hidden layer activation function. In order to enhance the performance of ELM, an ameliorated extreme learning machine, namely AELM, is proposed based on the aforementioned four factors. The proposed method owns new way to generate input weights and bias of hidden layer and has a new-type hidden layer activation function. Simulations on many UCI benchmark regression problems have demonstrated that the AELM generally outperforms the original ELM as well as several variants of ELM. Simultaneously, the AELM is adopted to build thermal efficiency model and NOx emission model of a 330MW circulating fluidized bed boiler. The results demonstrate the AELM is a useful machine learning tool. [ABSTRACT FROM AUTHOR]

This paper is devoted to introduce a novel method of the operational matrix of integration for Legendre wavelets in order to predict the thermal behavior of stratospheric balloons on float at high altitude in the stratosphere. Radiative and convective heat transfer models are also developed to calculate absorption and emission heat of the balloon film and lifting gas within the balloon. Thermal equilibrium equations (TEE) for the balloon system at daytime and nighttime are shown to predict the thermal behavior of stratospheric balloons. The properties of Legendre wavelets are used to reduce the TEE to a nonlinear system of algebraic equations which is solved by using a suitable numerical method. The approximations of the thermal behavior of the balloon film and lifting gas within the balloon are derived. The diurnal variations of the film and lifting gas temperature at float conditions are investigated, and the efficiency of the proposed method is also confirmed. [ABSTRACT FROM AUTHOR]

A novel artificial neural network called fast learning network (FLN) was proposed by Li et al. in 2014, which showed better performance than some famous traditional artificial neural network algorithms. Inspired by the conventional FLN and quantum mechanics, this work proposes a kind of quantum-inspired double parallel feed-forward neural network (QIDPFNN). Compared with the FLN, the proposed algorithm presents two obvious differentials. Firstly, the input weights between hidden layer and input layer are generated by quantum computing. Secondly, a new hidden layer activation function is introduced. In order to verify the QIDPFNN validity, it is applied to 13 regression applications. The experimental results show that the QIDPFNN owns better generalization ability and stronger stability than FLN on most applications. Simultaneously, the QIDPFNN is applied to build NOx emission concentration model and thermal efficiency model of a 330 MW circulating fluidized bed boiler. The experiment results demonstrate that the proposed method has high regression precision, strong stability and generalization ability. [ABSTRACT FROM AUTHOR]

Detecting the threats of the external obstacles to the power lines can ensure the stability of the power system. Inspired by the attention mechanism and binocular vision of human visual system, an intelligent power line inspection system is presented in this paper. Human visual attention mechanism in this intelligent inspection system is used to detect and track power lines in image sequences according to the shape information of power lines, and the binocular visual model is used to calculate the 3D coordinate information of obstacles and power lines. In order to improve the real time and accuracy of the system, we propose a new matching strategy based on the traditional SURF algorithm. The experimental results show that the system is able to accurately locate the position of the obstacles around power lines automatically, and the designed power line inspection system is effective in complex backgrounds, and there are no missing detection instances under different conditions. [ABSTRACT FROM AUTHOR]

A detailed aerodynamic performance of a ducted tail rotor in hover has been numerically studied using CFD technique. The general governing equations of turbulent flow around ducted tail rotor are given and directly solved by using finite volume discretization and Runge-Kutta time integration. The calculations of the lift characteristics of the ducted tail rotor can be obtained. In order to predict the aerodynamic noise, a hybrid method combining computational aeroacoustic with boundary element method (BEM) has been proposed. The computational steps include the following: firstly, the unsteady flow around rotor is calculated using the CFD method to get the noise source information; secondly, the radiate sound pressure is calculated using the acoustic analogy Curle equation in the frequency domain; lastly, the scattering effect of the duct wall on the propagation of the sound wave is presented using an acoustic thin-body BEM. The aerodynamic results and the calculated sound pressure levels are compared with the known technique for validation. The sound pressure directivity and scattering effect are shown to demonstrate the validity and applicability of the method. [ABSTRACT FROM AUTHOR]

The traditional space near the aerostat is to achieve a high-altitude aerostat platform in near station-keeping, which requires large energy consumption, and the need to carry large amounts of energy battery. Based on stratospheric quasi-zero wind layer theory, when the flying is height-adjustable long space, then the flight near hover aerostat program is in a designated area. Zero wind layer by aligning the theoretical analysis, we design one kind of aerostat balloon with a deputy, by inflating and deflating the balloon deputy achieve aerostat height adjustment, so aerostat always stay in the quasi-zero wind layer or aerostat realize what the direction of movement; propulsion system to achieve the use of portable aerostat north-south direction of motion control. After this paper shows that the method is simple height adjustment by Vice aerostat balloon is not required to withstand strong capsule, small power consumption, energy systems and propulsion systems do not ask, can achieve aerostat designated area hovering. [ABSTRACT FROM AUTHOR]

In recent years, optimization problems have received extensive attention from researchers, and metaheuristic algorithms have been proposed and applied to solve complex optimization problems. The wild horse optimizer (WHO) is a new metaheuristic algorithm based on the social behavior of wild horses. Compared with the popular metaheuristic algorithms, it has excellent performance in solving engineering problems. However, it still suffers from the problem of insufficient convergence accuracy and low exploration ability. This article presents an improved wild horse optimizer (I-WHO) with early warning and competition mechanisms to enhance the performance of the algorithm, which incorporates three strategies. First, the random operator is introduced to improve the adaptive parameters and the search accuracy of the algorithm. Second, an early warning strategy is proposed to improve the position update formula and increase the population diversity during grazing. Third, a competition selection mechanism is added, and the search agent position formula is updated to enhance the search accuracy of the multimodal search at the exploitation stage of the algorithm. In this article, 25 benchmark functions (Dim = 30, 60, 90, and 500) are tested, and the complexity of the I-WHO algorithm is analyzed. Meanwhile, it is compared with six popular metaheuristic algorithms, and it is verified by the Wilcoxon signed-rank test and four real-world engineering problems. The experimental results show that I-WHO has significantly improved search accuracy, showing preferable superiority and stability. [ABSTRACT FROM AUTHOR]

The Squirrel Search Algorithm (SSA) is widely used due to its simple structure and efficient search ability. However, SSA exhibits relatively slow convergence speed and imbalanced exploration and exploitation. To address these limitations, this paper proposes a fuzzy squirrel search algorithm based on a wide-area search mechanism named FSSSA. The fuzzy inference system and sine cosine mutation are employed to enhance the convergence speed. The wide-area search mechanism is introduced to achieve a better balance between exploration and exploitation, as well as improve the convergence accuracy. To evaluate the effectiveness of the proposed strategies, FSSSA is compared with SSA on 24 diverse benchmark functions, using four evaluation indexes: convergence speed, convergence accuracy, balance and diversity, and non-parametric test. The experimental results demonstrate that FSSSA outperforms SSA in all four indexes. Furthermore, a comparison with eight metaheuristic algorithms is conducted to illustrate the optimization performance of FSSSA. The results indicate that FSSSA exhibits excellent convergence speed and overall performance. Additionally, FSSSA is applied to four engineering problems, and experimental verification confirms that it maintains superior performance in realistic optimization problems, thus demonstrating its practicality. [ABSTRACT FROM AUTHOR]

The understanding of the target radar cross section (RCS) is significant for target identification and for radar designing and optimization. In this paper, a numerical algorithm for calculating target RCS is presented which is based on Legendre wavelet model-based parameter estimation (LW-MBPE). The Padé rational function fitting model applied for MBPE in the frequency domain is enhanced to include spatial dependence on the numerator and denominator coefficients. This allows the function to interpolate target RCS in both the frequency and spatial domains simultaneously. The combination of Legendre wavelets guarantees the convergence of the algorithm. The method is convergent by increasing the sampling frequency and spatial points. Numerical results are provided to demonstrate the validity and applicability of the new technique. [ABSTRACT FROM AUTHOR]

A numerical method for solving a class of fractional partial differential equations with variable coefficients based on Legendre polynomials is proposed. A fractional order operational matrix of Legendre polynomials is also derived. The initial equations are transformed into the products of several matrixes by using the operational matrix. A system of linear equations is obtained by dispersing the coefficients and the products of matrixes. Only a small number of Legendre polynomials are needed to acquire a satisfactory result. Results obtained using the scheme presented here show that the numerical method is very effective and convenient for solving fractional partial differential equations with variable coefficients. [ABSTRACT FROM AUTHOR]

A transparent and tunable metamaterial absorber with simultaneously low infrared (IR) emissivity is proposed in this Letter. The designed structure consists of an infrared shielding layer (IRSL), a radar absorber layer (RAL), and an air/water dielectric layer with an indium-tin-oxide (ITO) backplane. Both the IRSL and RAL are made of ITO films and polyethylene terephthalate substrates, so the absorber exhibits optical transparency. By changing the thickness of the water layer, the 90% absorption band can be converted from 7.6–12.5 to 12.8–18.2 GHz. The absorber is polarization insensitive and stable at wide incidence angles. The IR emission value of IRSL is 0.24. Both numerical simulations and experimental measurements demonstrated the good performance of the proposed structure, indicating its great potential applications in multi-spectrum compatible stealth. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a wavelet operational method based on Haar wavelet is proposed to solve the fractional partial differential equations in the Caputo derivative sense. We give the Haar wavelet operational matrix of fractional order integration. A truncated Haar wavelet series together with the wavelet operational matrix are utilized to reduce the fractional partial differential equations to Sylvester equations. In addition, some examples are presented to show the efficiency and the accuracy of the approach. [Copyright &y& Elsevier]

Harris hawks optimization (HHO) is a new meta-heuristic algorithm that builds a model by imitating the predation process of Harris hawks. In order to solve the problems of poor convergence speed caused by uniform choice position update formula in the exploration stage of basic HHO and falling into local optimization caused by insufficient population richness in the later stage of the algorithm, a Harris hawks optimization based on global cross-variation and tent mapping (CRTHHO) is proposed in this paper. Firstly, the tent mapping is introduced in the exploration stage to optimize random parameter q to speed up the convergence in the early stage. Secondly, the crossover mutation operator is introduced to cross and mutate the global optimal position in each iteration process. The greedy strategy is used to select, which prevents the algorithm from falling into local optimal because of skipping the optimal solution and improves the convergence accuracy of the algorithm. In order to investigate the performance of CRTHHO, experiments are carried out on ten benchmark functions and the CEC2017 test set. Experimental results show that the CRTHHO algorithm performs better than the HHO algorithm and is competitive with five advanced meta-heuristic algorithms. [ABSTRACT FROM AUTHOR]

Previous studies have shown that bispecific antibodies that target both CD20 and CD22 have in vivo lymphomacidal properties. We developed a CD20-CD22 bispecific antibody (Bs20x22) from anti-CD20 and the anti-CD22 monoclonal antibodies (mAb), rituximab and HB22.7, respectively. Bs20x22 was constructed using standard methods and was shown to specifically bind CD20 and CD22. In vitro cytotoxicity assays showed that Bs20x22 was three times more effective than either parent mAb alone and twice as effective as a combination of both parent mAb used at equimolar concentrations. Bs20x22 was also nearly four times more effective at inducing apoptosis than either mAb alone. Examination of the MAPK and SAPK signaling cascades revealed that Bs20x22 induced significantly more p38 phosphorylation than either mAb alone. In an in vivo human NHL xenograft model, treatment with Bs20x22 resulted in significantly greater tumor shrinkage and improved overall survival when compared to either mAb alone or treatment with a combination of HB22.7 and rituximab. The effect of the initial tumor volume was assessed by comparing the efficacy of Bs20x22 administered before xenografts grew versus treatment of established tumors; significantly, greater efficacy was found when treatment was initiated before tumors could become established. [ABSTRACT FROM AUTHOR]

CD22 is a cell-surface adhesion molecule on most B-cell NHL, so it is a promising target for immunotherapy. HB22.7 is an anti-CD22 mAb that binds the two NH2-terminal immunoglobulin domains and specifically blocks the interaction of CD22 with its ligand. CD22-blocking mAbs induce apoptosis in neoplastic B-cells and are functionally distinguishable from other anti-CD22 mAbs. This study assessed the optimal dose, route, schedule, and the targeted CD22 epitope. Raji NHL-bearing nude mice were studied. A non-blocking anti-CD22 mAb (HB22.27) was used as a control. HB22.27 had minimal effect, whereas HB22.7 improved survival and shrank tumors substantially. HB22.7 doses greater than 1.4 mg/week did not further increase efficacy (or toxicity). Tumors less than 200 mm3 had a higher response rate than did larger tumors. Various schedules of HB22.7 administration were tested; one dose every other week was more effective than more or less frequent dosing. Pharmacokinetic studies revealed that the half-life of HB22.7 was 28 days; this correlated with the time needed to re-populate cell-surface CD22 after treatment with HB22.7. Immuno-PET showed that NHL was rapidly and specifically targeted by copper-64-labeled-HB22.7. This study provided data as to an optimal dose, route, schedule and interval between doses of HB22.7. [ABSTRACT FROM AUTHOR]

In this study, {[CoFe 3 O(tza) 6 (H 2 O) 3 ]·(NO 3) 3 ·5 H 2 O} n was applied as a precursor for fabricating CoFe/Fe/C composite materials via explosive and high-temperature calcination techniques. The explosive method was identified as a straightforward approach for achieving remarkable electromagnetic wave absorption properties. At a thickness of 2.03 mm, the CoFe/Fe/C (CoFe-700) composite material exhibited a minimum reflection loss of −77.84 dB. Similarly, it demonstrated the widest effective absorption (≦ −10 dB) bandwidth (4.06 GHz) with a thickness of 2.00 mm. Encompassing frequencies from 2.90 GHz to 18 GHz, the total effective absorption bandwidth extended throughout the thickness range of 1.00−5.50 mm. Under the same mixing ratio of powder and paraffin (1:1), the effective absorption bandwidth value of CoFe/Fe/C (CoFe-600) sample at a thickness of 1.40 mm is 4.95 GHz. The CoFe/Fe/C carbon composite materials emerged as optimal electromagnetic wave absorbers due to their exceptional conductivities, abundant dielectric losses, and magnetic loss mechanisms. [Display omitted] • The nanopowder CoFe-270 (Fe 3 C/Fe 3 N/CoFe 2 O 4 /C) was obtained by explosive method with {[CoFe 3 O(tza) 6 (H 2 O) 3 ]·(NO 3) 3 ·5 H 2 O} n as precursor. • The powder CoFe-700 (CoFe/Fe/C) was obtained by high temperature calcination method with CoFe-270 as precursor. • At 11.36 GHz and 2.03 mm, the minimum reflection loss (RL min) of the sample CoFe-700 (1:1) was −77.84 dB. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) have become important tools for power transmission line inspection. Cameras installed on the platforms can efficiently obtain aerial images containing information about power equipment. However, most of the existing inspection systems cannot perform automatic real-time detection of transmission line components. In this paper, an automatic transmission line inspection system incorporating UAV remote sensing with binocular visual perception technology is developed to accurately detect and locate power equipment in real time. The system consists of a UAV module, embedded industrial computer, binocular visual perception module, and control and observation module. Insulators, which are key components in power transmission lines as well as fault-prone components, are selected as the detection targets. Insulator detection and spatial localization in aerial images with cluttered backgrounds are interesting but challenging tasks for an automatic transmission line inspection system. A two-stage strategy is proposed to achieve precise identification of insulators. First, candidate insulator regions are obtained based on RGB-D saliency detection. Then, the skeleton structure of candidate insulator regions is extracted. We implement a structure search to realize the final accurate detection of insulators. On the basis of insulator detection results, we further propose a real-time object spatial localization method that combines binocular stereo vision and a global positioning system (GPS). The longitude, latitude, and height of insulators are obtained through coordinate conversion based on the UAV's real-time flight data and equipment parameters. Experiment results in the actual inspection environment (220 kV power transmission line) show that the presented system meets the requirement of robustness and accuracy of insulator detection and spatial localization in practical engineering. [ABSTRACT FROM AUTHOR]

In order to reduce the NOx emissions concentration of a circulation fluidized bed boiler, a modified teaching–learning-based optimization algorithm (MTLBO) is proposed, which introduces a new population group mechanism into the conventional teaching–learning based optimization algorithm. The MTLBO still has two phases: Teaching phase and Learning phase. In teaching phase, all students are divided into two groups based on the mean marks of the class, the two groups present different solution updating strategies, separately. In learning phase, all students are divided into two groups again, where the first group includes the top half of the students and the second group contains the remaining students. The two groups also have different solution updating strategies. Performance of the proposed MTLBO algorithm is evaluated by 14 unconstrained numerical functions. Compared with TLBO and other several state-of-the-art optimization algorithms, the results indicate that the MTLBO shows better solution quality and faster convergence speed. In addition, the tuned extreme learning machine by MTLBO is applied to establish the NOx emission model. Based on the established model, the MTLBO is used to optimize the operation conditions of a 330 MW circulation fluidized bed boiler for reducing the NOx emissions concentration. Experimental results reveal that the MTLBO is an effective tool for reducing the NOx emissions concentration. [ABSTRACT FROM AUTHOR]

In this paper, the reflection and transmission characteristics of frequency selective surface (FSS) embedded within a thick plasma is studied. The plasma layer is lossy and dispersive medium and the embedding of the FSS will change the electromagnetic wave propagation very much. The reflection and transmission coefficients of a square patch FSS layer embedding in plasma is calculated utilizing finite-difference time-domain (FDTD) method. Auxiliary differential equation (ADE) is utilized to simulate the dispersion of the plasma. The influence of the plasma and slab's parameter to the reflection and transmission coefficients is investigated. Various parameters of collision frequency, plasma frequency and thickness of plasma are involved to study the influence of these factors. The reflection and transmission coefficient values of different parameters are plotted to compare their effects. The results show that collision frequency reduces the reflection and transmission coefficient. Plasma frequency changes the FSS from low pass filter to band pass filter. Thick plasma slab makes the transmission coefficient oscillate as the variation of incident frequency. [ABSTRACT FROM AUTHOR]

In this paper, a numerical technique based on two dimensional finite difference method (FDM) is utilized to study the effective permittivity of mixtures with various patterns of inclusions' distributions. The inclusions are assumed to be periodically distributed in the host medium. The influence of the inclusions' shape, permittivity, volume fraction and randomness are investigated in the simulation. Three categories of inclusions' types are considered, which are solid, hollow and randomly distributed inclusion. The shapes of solid inclusion type include square, cylinder, diamond and plus. The shapes of hollow inclusion type include square, cylinder, diamond and grid. The shapes of randomly distributed inclusion type include fine-grained inclusions and coarse-grained inclusions. The results of FDM methods are also compared with that of the analysis. Several analysis formulas are considered, such as Maxwell-Garnett, Bruggeman, Birchak, Looyenga and Lichtenecker. For all shapes of solid patterns except the plus, the simulated effective dielectric properties of mixtures are in good agreement with that obtained by Maxwell-Garnett prediction formula. When the contrast of permittivities between the inclusions and the environment is high, the numerical results for the square solid pattern match well with the analytical results provided by the Maxwell-Garnett formula. For other patterns, no analytical formulas can be implemented appropriately. The FDM results of hollow patterns deviate from the theoretical results significantly. In addition, for mixtures with randomly distributed inclusions, the effective permittivity achieved by the numerical technique varies almost linearly with volume fraction, which does not agree well with any analysis formula. [ABSTRACT FROM AUTHOR]

Grasshopper optimization algorithm (GOA) is a meta-heuristic algorithm for solving optimization problems by modeling the biological habit and social behavior of grasshopper swarms in nature. Compared with other optimization algorithms, GOA still has room to improve its performance on solving complex problems. Therefore, this paper proposes an improved grasshopper optimization algorithm (EMGOA) based on dynamic dual elite learning and sinusoidal mutation. First of all, dynamic elite learning strategy is adopted to improve the influence of elites on the update process, enabling the algorithm to have a faster convergence speed. Then, sinusoidal function is utilized to guide the mutation of the current global optimal individual during each iteration to avoid the algorithm falling into the local optimum and improve the convergence accuracy of the algorithm. In order to investigate the performance of the proposed EMGOA algorithm, experiments are conducted on 26 benchmark functions and CEC2019 in this paper. The experimental results show that the optimization performance of EMGOA is obviously better than GOA, and EMGOA is competitive with six state-of-the-art meta-heuristic optimization algorithms. [ABSTRACT FROM AUTHOR]

2D platinum diselenide (PtSe2), a novel member of the transition metal dichalcogenides (TMDCs) family, possesses many excellent properties, including a layer‐dependent bandgap, high carrier mobility, and broadband response, making it promise for applications in technologies like field‐effect transistors and room‐temperature photodetectors. Doping represents an effective method to modify the electrical properties of 2D TMDCs and to bestow upon them additional functions. However, to date, little research has been conducted on the successful doping of 2D PtSe2 for modification. In this study, sulfur (S) powder is utilized during the chemical vapor deposition growth process of 2D PtSe2 ribbons and successfully integrated into the PtSe2 lattice through substitutional doping. The Au substrate significantly decreases the substitution energy of Se atoms in the lower layer of PtSe2, resulting in the formation of the Janus PtSSe structure. S‐doped PtSe2 ribbons demonstrate significant symmetry breaking and enhanced electrical properties, showcasing a strong nonlinear optical response and certain synaptic plasticity, further simulating some neuromorphological processes. This study not only demonstrates a viable method for controllable doping and modification of 2D PtSe2 but also establishes a platform for exploring the characteristics of Janus TMDCs. [ABSTRACT FROM AUTHOR]

Preeclampsia (PE) is a life-threatening pregnancy complication associated with diminished trophoblast migration and invasion. Wnt signalling is one of the most important regulators of placentation. Secreted frizzled-related protein 5 (SFRP5) is an anti-inflammatory adipokine that may inhibit Wnt signalling. In this study, we aimed to investigate the relationship between SFRP5 and PE and its effect on trophoblast function, as well as the underlying signalling pathways. SFRP5 levels in the serum and placental tissues were detected using enzyme-linked immunosorbent assay and immunohistochemistry, respectively. To evaluate the effect of SFRP5 on Wnt signalling, the human trophoblast cell line HTR8/SVneo was treated with recombinant human SFRP5 and Dickkopf-related protein 1 (Dkk-1, canonical Wnt inhibitor) proteins and lithium chloride (LiCl, canonical Wnt agonist). The migration and invasion ability of HTR8/SVneo cells was evaluated using wound-healing and Matrigel Transwell assays. The activities of multiple matrix metalloproteinases (MMP)-2/9 were detected using gelatin zymography. Expression of glycogen synthase kinase-3 beta (GSK3β) and β-catenin proteins was investigated using western blotting. The serum SFRP5 levels were elevated in patients with PE, but SFRP5 expression was not detected in the placental tissues. Furthermore, SFRP5 inhibited the migration and invasion of HTR8/SVneo cells in vitro, increased GSK3β, and decreased β-catenin expression and MMP-2/9 activity in HTR8/SVneo cells. In conclusion, this study suggests that SFRP5 inhibits trophoblast migration and invasion potentially via the inhibition of Wnt/β-catenin signalling, which might be involved in the development of PE. However, the primary cause of the increased SFRP5 levels needs to be investigated. [ABSTRACT FROM AUTHOR]

This paper proposed a single-layer checkerboard metasurface with simultaneous wideband radar cross-section (RCS) reduction characteristics and low infrared (IR) emissivity. The metasurface consists of an indium tin oxide (ITO)-patterned film, a polyethylene terephthalate (PET) substrate and an ITO backplane from the top downwards, with a total ultra-thin thickness of 1.6 mm. This design also allows the metasurface to have good optical transparency and flexibility. Based on phase cancellation and absorption, the metasurface can achieve a wideband RCS reduction of 10 dB from 10.6 to 19.4 GHz under normal incidence. When the metasurface is slightly cylindrically curved, an RCS reduction of approximately 10 dB can still be achieved from 11 to 19 GHz. The polarization and angular stability of the metasurface have also been verified. The filling rate of the top ITO-patterned film is 0.81, which makes the metasurface have a low theoretical IR emissivity of 0.24. Both simulation and experimental results have verified the excellent characteristics of the proposed checkerboard metasurface, demonstrating its great potential application in radar–IR bi-stealth. [ABSTRACT FROM AUTHOR]

In the current global context, digital finance (DF) and sustainable economic development (SED) are important topics. The synergies between DF and SED have already been proven. However, the measurement and quantitative analysis of the coupling coordination degree (CCD) of DF and SED have not received sufficient attention to date. Based on data from 55 cities in the Yellow River Basin (YRB) from 2011 to 2021, this study constructs an evaluation index system of DF and SED and measures their level, respectively. The proposed CCD model is then used to measure the CCD between the two systems. In addition, kernel density estimation, Markov chain, σ-convergence, β-convergence, and the quadratic assignment procedure (QAP) method are used to study the spatial pattern, distribution dynamic evolution trend, convergence, and influencing factors of the regional differences in the CCD. The results show that: (1) From 2011 to 2021, the CCD level showed a stable upward trend and regional heterogeneity, and the time stage characteristics were more obvious. (2) The center position and change interval of the overall distribution curve of the kernel density estimation gradually shifted to the right. The Markov transfer probability matrix shows that the CCD is more stable among different levels, indicating a phenomenon of "club convergence". (3) A convergence analysis shows that there are significant σ-convergence, absolute β-convergence, and conditional β-convergence. (4) The QAP regression shows that factors such as the regional differences in GDP per capita have a significant impact on the regional differences in the CCD. This study offers a comprehensive structure that can be used to examine the synergistic effects between DF and SED; the research findings can also provide perspectives for other areas. [ABSTRACT FROM AUTHOR]

A transparent metamaterial absorber (MMA) with both tunable absorption bandwidth and low infrared (IR) emissivity is proposed in this paper. The MMA is hierarchical, which consists of an infrared shielding layer (IRSL), two radar-absorption layers (RALs), an air/water layer, and an indium–tin–oxide (ITO) backplane from the top downwards. The IRSL and the RALs are made of ITO patterns etched on polyethylene terephthalate (PET) substrates. By changing the thickness of the water, the 90% absorption bandwidth can be tuned from 6.4–11.3 GHz to 12.7–20.6 GHz, while retaining good polarization and angular stability. An equivalent circuit model (ECM) is present, to reveal the physical mechanism of absorption. The proposed MMA has a low theoretical IR emissivity of about 0.24. A sample was fabricated and measured, and the experimental results are consistent with the simulation results, showing its potential applications in stealth glass and multifunctional radome. [ABSTRACT FROM AUTHOR]

This paper focuses on horizontal path following for an underactuated unmanned airship under output constraints and uncertainties. To handle the output constraints, we design a guidance loop based on a novel time-varying tan-type barrier Lyapunov function to generate the desired yaw angle. By using the backstepping technique, the controller is then designed to realize the desired yaw and velocity tracking. For uncertainties and disturbances, an adaptive radial basis function neural network is applied for estimation and compensation. Moreover, to avoid complex derivative calculations for virtual control and guarantee that the desired attitude is bounded, a command filter and an auxiliary system are combined in the system. Theoretical analysis shows that, despite the influence of disturbances, the designed controller ensures that the constraint of airship position is never violated while the closed loop signals are all uniformly ultimately bounded. Comparable simulations illustrate the performance of our controller. [ABSTRACT FROM AUTHOR]

Abstract Dissolved organic matter (DOM) samples from forestry, livestock breeding, urban domestic and agricultural sources around Jiaozhou Bay were incubated in seawater to examine DOM dynamics and bioavailability, which is the availability of DOM to degradation by microbes. The fluorescence signals of protein- and humic-like compounds were used as a proxy for bioavailable and refractory materials, respectively, whereas the evolution of SUVA 254 , dissolved organic nitrogen (DON) and dissolved organic carbon (DOC) concentrations were used to discuss bioavailability. The high bioavailability DON sources were from livestock breeding and urban domestic sources, with no significant difference, followed by agricultural and forestry sources, with respective degradation rate constants of 0.36 ± 0.01 (mean ± SD) d−1, 0.32 ± 0.03 d−1, 0.22 ± 0.04 d−1, and 0.18 ± 0.01 d−1, and with bioavailable DON (BDON%) representing 70% ± 10%, 67% ± 7%, 62% ± 8% and 37% ± 12% of the total DON, respectively. The turnover times of BDON were 3–6 days, far less than the flushing time of Jiaozhou Bay. Approximately 66% of land-based DON may contribute to DIN by microbial re-mineralization in Jiaozhou Bay, with the main sources being urban domestic, agricultural and livestock breeding. DON seemed to exhibit higher bioavailability than DOC. The temporal evolution of fluorophores and optical indices revealed that bioavailable DOM from livestock breeding and urban domestic sources was high. Spectroscopic properties indicated that the degree of humification of the organic molecules changed significantly for DON from livestock breeding and urban domestic sources, followed by agricultural sources, and forestry sources were the lowest. SUVA 254 values and HIX were negatively correlated with DON bioavailability, which can be used as a refractory index and for predicting bioavailable DON. Graphical abstract Image 1 Highlights • Land-based bioavailable DON (BDON) represents from 37% to 70% of the total DON. • Turnover times of land-based BDON were 3–6 days. • Approximately 66% of the land-based DON was converted to DIN in Jiaozhou Bay. • Bioavailability of land-based DON was negatively correlated with humic substances. [ABSTRACT FROM AUTHOR]

Underwater images often suffer from color distortion and low contrast, which dramatically affects the target detection and measurement tasks in the underwater context. In this paper, we present a multistage generative adversarial network for better visual perception of underwater images. Extensive multi-scale context feature learning and high-precision restoration of spatial details are implemented stage by stage. Rich context features are learned based on the encoder and decoder architecture. Spatial details are restored through a pixel restoration module based on original images. Through channel attention module used between multistages, cross-stage feature utilization is realized. More notably, we introduce Gaussian noise into the generator, which enriches the details of images, and the relative discriminator, which promotes the generated image to have more realistic edges and textures. Experimental results demonstrate the superiority of our method over state-of-the-art methods in terms of both quantitative metrics and visual quality. In particular, we applied our method to natural underwater scenes. The results confirm that our method can effectively improve the efficiency of downstream tasks. [ABSTRACT FROM AUTHOR]

Stereo matching is a vital requirement for many applications, such as three-dimensional (3-D) reconstruction, robot navigation, object detection, and industrial measurement. To improve the practicability of stereo matching, a method using census cost over cross window and segmentation-based disparity refinement is proposed. First, a cross window is obtained using distance difference and intensity similarity in binocular images. Census cost over the cross window and color cost are combined as the matching cost, which is aggregated by the guided filter. Then, winner-takes-all strategy is used to calculate the initial disparities. Second, a graph-based segmentation method is combined with color and edge information to achieve moderate under-segmentation. The segmented regions are classified into reliable regions and unreliable regions by consistency checking. Finally, the two regions are optimized by plane fitting and propagation, respectively, to match the ambiguous pixels. The experimental results are on Middlebury Stereo Datasets, which show that the proposed method has good performance in occluded and discontinuous regions, and it obtains smoother disparity maps with a lower average matching error rate compared with other algorithms. © 2018 SPIE and IS&T [DOI: 10.1117/ 1.JEI.27.2.023014] [ABSTRACT FROM AUTHOR]

Non-Hodgkin lymphoma (NHL) affects over 400,000 people in the United States; its incidence increases with age. Treatment options are numerous and expanding, yet efficacy is often limited by toxicity, particularly in the elderly. Nearly 70% patients eventually die of the disease. Many patients explore less toxic alternative therapeutics proposed to boost anti-tumor immunity, despite a paucity of rigorous scientific data. Here we evaluate the lymphomacidal and immunomodulatory activities of a protein fraction isolated from fermented wheat germ. Fermented wheat germ extract was produced by fermenting wheat germ with Saccharomyces cerevisiae. A protein fraction was tested for lymphomacidal activity in vitro using NHL cell lines and in vivo using mouse xenografts. Mechanisms of action were explored in vitro by evaluating apoptosis and cell cycle and in vivo by immunophenotyping and measurement of NK cell activity. Potent lymphomacidal activity was observed in a panel of NHL cell lines and mice bearing NHL xenografts. This activity was not dependent on wheat germ agglutinin or benzoquinones. Fermented wheat germ proteins induced apoptosis in NHL cells, and augmented immune effector mechanisms, as measured by NK cell killing activity, degranulation and production of IFNγ. Fermented wheat germ extract can be easily produced and is efficacious in a human lymphoma xenograft model. The protein fraction is quantifiable and more potent, shows direct pro-apoptotic properties, and enhances immune-mediated tumor eradication. The results presented herein support the novel concept that proteins in fermented wheat germ have direct pro-apoptotic activity on lymphoma cells and augment host immune effector mechanisms. [ABSTRACT FROM AUTHOR]

In this paper, a cancelling scheme for the nonlinear frequency modulated (NLFM) signal (Taylor window and Tangent-based) is proposed with assistance of the N th-order SSC (Spectrum Spread and Compression) algorithm. The explicit expressions of echo cancelling wave are derived for the N th-order SSC algorithm. The received NLFM signal is expanded N times and then compressed N -1 times in frequency domain using its delayed version to generate jamming signal. According to the RCS (Radar Cross Section) characteristic of target, amplitude and phase modulation of the jamming signal are made to acquire the cancelling signal, which has the same amplitude, frequency as the echo signal but with the opposite phase. Simulation results show that the cancelling signal could reduce the target gain when the time delay and the phase error are relatively large. [ABSTRACT FROM AUTHOR]

This paper is concerned with the stability analysis issue of fractional-order impulsive neural networks. Under the one-side Lipschitz condition or the linear growth condition of activation function, the existence of solution is analyzed respectively. In addition, the existence, uniqueness and global Mittag-Leffler stability of equilibrium point of the fractional-order impulsive neural networks with one-side Lipschitz condition are investigated by the means of contraction mapping principle and Lyapunov direct method. Finally, an example with numerical simulation is given to illustrate the validity and feasibility of the proposed results. [ABSTRACT FROM AUTHOR]

The krill herd (KH) is an innovative biologically-inspired algorithm. To improve the solution quality and to quicken the global convergence speed of KH, an ameliorated krill herd algorithm (A-KH) is proposed to solve the aforementioned problems and test it by classical benchmark functions, which is one of the major contributions of this paper. Compared with other several state-of-art optimization algorithms (biogeography-based optimization, particle swarm optimization, artificial bee colony and krill herd algorithm), A-KH shows better search performance. There is, furthermore, another contribution that the A-KH is adopted to adjust the parameters of the fast learning network (FLN) so as to build the turbine heat rate model of a 600MW supercritical steam and obtain a high-precision prediction model. Experimental results show that, compared with other several turbine heat rate models, the tuned FLN model by A-KH has better regression precision and generalization capability. [ABSTRACT FROM AUTHOR]