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Miniature loudspeakers exhibit pronounced nonlinear characteristics due to physical size limitations and the demand for sufficient sound pressure. The primary nonlinear properties, including the force factor, mechanical stiffness, and mechanical resistance, depend on the displacement or velocity of the voice coil. The creep effect in the loudspeaker’s suspension system, which significantly affects low-frequency displacement, is often neglected in existing studies on nonlinear dynamics. This study enhances the modeling of miniature loudspeakers by incorporating the creep effect into an extended nonlinear model. The voice coil displacement and total harmonic distortion (THD) predicted by the extended model were validated against experimental data. The displacement and THD values predicted by the nonlinear model in consideration of the creep effect corresponded closely to the actual measurement values, substantiating the effectiveness and precision of the model.

Ferrochelatase (FECH) is the terminal enzyme in human heme biosynthesis, catalyzing the insertion of ferrous iron into protoporphyrin IX (PPIX) to form protoheme IX (Heme). Phosphorylation increases the activity of FECH, and it has been confirmed that the activity of FECH phosphorylated at T116 increases. However, it remains unclear whether the T116 site and other potential phosphorylation modification sites collaboratively regulate the activity of FECH. In this study, we identified a new phosphorylation site, T218, and explored the allosteric effects of unphosphorylated (UP), PT116, PT218, and PT116 + PT218 states on FECH in the presence and absence of substrates (PPIX and Heme) using molecular dynamics (MD) simulations. Binding free energies were evaluated with the MM/PBSA method. Our findings indicate that the PT116 + PT218 state exhibits the lowest binding free energy with PPIX, suggesting the strongest binding affinity. Additionally, this state showed a higher binding free energy with Heme compared to UP, which facilitates Heme release. Moreover, employing multiple analysis methods, including free energy landscape (FEL), principal component analysis (PCA), dynamic cross-correlation matrix (DCCM), and hydrogen bond interaction analysis, we demonstrated that phosphorylation significantly affects the dynamic behavior and binding patterns of substrates to FECH. Insights from this study provide valuable theoretical guidance for treating conditions related to disrupted heme metabolism, such as various porphyrias and iron-related disorders.

One of the limitations of the dung beetle optimization (DBO) is its susceptibility to local optima and its relatively low search accuracy. Several strategies have been utilized to improve the diversity, search precision, and outcomes of the DBO. However, the equilibrium between exploration and exploitation has not been achieved optimally. This paper presents a novel algorithm called the ODBO, which incorporates cat map and an opposition-based learning strategy, which is based on symmetry theory. In addition, in order to enhance the performance of the dung ball rolling phase, this paper combines the global search strategy of the osprey optimization algorithm with the position update strategy of the DBO. Additionally, we enhance the population’s diversity during the foraging phase of the DBO by incorporating vertical and horizontal crossover of individuals. This introduction of asymmetry in the crossover operation increases the exploration capability of the algorithm, allowing it to effectively escape local optima and facilitate global search.

Apples are deeply loved by people because of their rich nutritional value, but they are susceptible to rotting. The use of antagonistic yeast is a promising method for controlling postharvest fruit diseases, but biocontrol efficacy of yeast will be weakened in environmental stress. In this study, the effects of glycine (Gly) on the oxidative stress tolerance and the biocontrol efficacy of Sporidiobolus pararoseus (S. pararoseus) against Aspergillus niger (A. niger) are discussed. Under the stimulation of H2O2, the yeast cells treated with Gly (1 mM) showed lower ROS content, less mitochondrial impairment and cellular oxidative damage, and the cell survival rate was significantly higher than Gly-untreated yeast. The yeast cells exposed to Gly significantly increased the activities of antioxidant enzymes superoxide dismutase (SOD), catalase (CAT), and the content of glutathione (GSH). Notably, Gly-treated yeast cells had better biocontrol efficacy against A. niger in postharvest apples. The lesion diameter and decay incidence were reduced by 17.67 mm and 79.63% compared to the control, respectively, when S. pararoseus was treated with 1 mM Gly. Moreover, Gly-treated yeast increased the antioxidant enzymes activities and their gene expression were up-regulated in apples. These results indicated that 1 mM Gly not only reduced the oxidative damage of yeast, but also induced resistance-related enzymes of apples under oxidative stress, which contributed to enhancing the biocontrol efficacy of S. pararoseus against A. niger in apples.

Floating offshore wind platform (FOWP) has become the economically favored option for supporting wind turbines in deep waters. It is urgent to propose new concept designs for FOWPs that can be effectively deployed. Additionally, the extensive use of steel in such platforms significantly escalates costs, necessitating the optimization of steel utilization. Motivated by these challenges, a V-shaped floating semi-submersible platform equipped with NREL 5 MW wind turbine is designed and analyzed based on the potential flow theory and the blade element momentum theory. Fully coupled time-domain simulations are conducted using the F2A program, which couples NREL FAST and ANSYS AQWA via a Dynamic Link Library (DLL), to compare the hydrodynamic performance and stability of the V-shaped floating platform with the original triangle-shaped model of “Fuyao”. Various sea conditions have been considered, including combined wind-wave action and wind-wave-current action at different incidence angles. The results show that the V-shaped floating platform has better economic and hydrodynamic performance (e.g., a reduction of 40.4% and 12.9%, respectively, in pitch and yaw motions, and a 17.4% reduction in maximum mooring tension), but lower stability than its triangle-shaped counterpart.

In this study, we utilized a simple and efficient microwave heating method with polyethyleneimine (PEI) and phosphate as raw materials to synthesize room temperature persistent luminescence (RTPL) materials that emit phosphorescent light for up to 10 s. Our investigation revealed that the optimal synthesis conditions were a microwave radiation power of 560 W and a heating time of 5 min. The synthesized RTPL materials had an average particle size of 2 nm and exhibited excellent RTPL performance, with optimal excitation and emission wavelengths of 360 nm and 544 nm, respectively. Additionally, these materials displayed good water solubility. We conducted mapping experiments and in situ phosphorescent imaging of plants to showcase the potential applications of RTPL materials in the fields of biological imaging and anti-counterfeiting. Overall, our findings demonstrate the promising potential of these RTPL materials as versatile tools for various practical applications.

From the perspective of the differential phase delay experienced by the two counterpropagating optical fields, the self-starting of the mode-locked fiber laser with a non-linear amplifying loop mirror (NALM) is theoretically studied. Although it is generally believed that NALM shows a saturable absorption effect on both continuous wave (CW) light and pulses, we find a counter-intuitive fact that cross-phase modulation (XPM) leads to opposite signs of differential non-linear phase shifts (NPSs) in these two cases, resulting in inverse saturable absorption (ISA) during the pulse formation process. The ISA is not helpful for the self-starting of laser mode-locking and can be alleviated by introducing a non-reciprocal phase shifter into the fiber loop. These results are helpful for optimizing the design of NALM and lowering the self-starting threshold of the high-repetition-rate mode-locked fiber laser.

Hydrazine borane (HB) is a chemical hydrogen storage material with high gravimetric hydrogen density of 15.4 wt%, containing both protic and hydridic hydrogen. However, its limitation is the formation of unfavorable gaseous by-products, such as hydrazine (N2H4) and ammonia (NH3), which are poisons to fuel cell catalyst, upon pyrolysis. Previous studies proved that confinement of ammonia borane (AB) greatly improved the dehydrogenation kinetics and thermodynamics. They function by reducing the particle size of AB and establishing bonds between silica functional groups and AB molecules. In current study, we employed the same strategy using MCM-41 and silica aerogel to investigate the effect of nanosizing towards the hydrogen storage properties of HB. Different loading of HB to the porous supports were investigated and optimized. The optimized loading of HB in MCM-41 and silica aerogel was 1:1 and 0.25:1, respectively. Both confined samples demonstrated great suppression of melting induced sample foaming. However, by-products formation was enhanced over dehydrogenation in an open system decomposition owing to the presence of extensive Si-O···BH3(HB) coordination that further promote the B-N bond cleavage to release N2H4. The Si-OH···N(N2H4) hydrogen bonding may further promote N-N bond cleavage in the resulting N2H4, facilitating the formation of NH3. As temperature increases, the remaining N-N-B oligomeric chains in the porous silica, which are lacking the long-range structure may further undergo intramolecular B-N or N-N cleavage to release substantial amount of N2H4 or NH3. Besides open system decomposition, we also reported a closed system decomposition where complete utilization of the N-H from the released N2H4 and NH3 in the secondary reaction can be achieved, releasing mainly hydrogen upon being heated up to high temperatures. Nanosizing of HB particles via PMMA encapsulation was also attempted. Despite the ester functional group that may favor multiple coordination with HB molecules, these interactions did not impart significant change towards the decomposition of HB selectively towards dehydrogenation.

Starmerella bacillaris is often isolated from environments associated with grape and winemaking. S. bacillaris has many beneficial properties, including the ability to improve the flavor of wine, the production of beneficial metabolites, and the ability to biocontrol. S. bacillaris CC-PT4 (CGMCC No. 23573) was isolated from grape and can inhibit methicillin-resistant Staphylococcus aureus and adaptability to harsh environments. In this paper, the whole genome of S. bacillaris CC-PT4 was sequenced and bioinformatics analyses were performed. The S. bacillaris CC-PT4 genome was finally assembled into five scaffolds with a genome size of 9.45 Mb and a GC content of 39.5%. It was predicted that the strain contained 4150 protein-coding genes, of which two genes encoded killer toxin and one gene encoded lysostaphin. It also contains genes encoding F1F0-ATPases, Na(+)/H(+) antiporter, cation/H(+) antiporter, ATP-dependent bile acid permease, major facilitator superfamily (MFS) antiporters, and stress response protein, which help S. bacillaris CC-PT4 adapt to bile, acid, and other stressful environments. Proteins related to flocculation and adhesion have also been identified in the S. bacillaris CC-PT4 genome. Predicted by antiSMASH, two secondary metabolite biosynthesis gene clusters were found, and the synthesized metabolites may have antimicrobial effects. Furthermore, S. bacillaris CC-PT4 carried genes associated with pathogenicity and drug resistance. Overall, the whole genome sequencing and analysis of S. bacillaris CC-PT4 in this study provide valuable information for understanding the biological characteristics and further development of this strain.

The reliability and safety of rotating equipment depend on the performance of bearings. For complex systems with high reliability and safety needs, effectively predicting the fault data in the use stage has important guiding significance for reasonably formulating reliability plans and carrying out reliability maintenance activities. Many methods have been used to solve the problem of reliability prediction. Due to its convenience and efficiency, the data-driven method is increasingly widely used in practical reliability prediction. In order to ensure the reliability of bearing operation, the main objective of the present study is to establish a novel model based on the optimized maximum correlation kurtosis deconvolution (MCKD) and long short-term memory (LSTM) recurrent neural network to realize early bearing fault warnings by predicting bearing fault time series. The proposed model is based on the lifecycle vibration signal of the bearing. In the first step, the cuckoo search (CS) is utilized to optimize the parameter filter length and deconvolution period of MCKD, considering the influence of periodic bearing time series, and to improve the fault impact component of the optimized MCKD deconvolution time series. Then the LSTM learning rate is selected according to the deconvolution time series. Finally, the dataset obtained through various preprocessing approaches is used to train and predict the LSTM model. The analyses performed using the XJTU-SY bearing dataset demonstrate that the prediction results are in good consistency with real fault data, and the average prediction accuracy of the optimized MCKD–LSTM model is 26% higher than that of the original time series.

Presence is the sense of being in a virtual environment when physically situated in another place. It is one of the key components of the overall virtual reality (VR) experience, as well as other immersive audio applications. However, there is no standardized method for measuring presence. In our previous study, we explored the possibility of using electroencephalography (EEG) to measure presence by using questionnaires as a reference. It was found that an increase in the subjective presence level was correlated with an increase in the theta/beta ratio (an index derived from EEG). In the present study, we re-analyzed the original data and found that the peak alpha frequency (PAF), another EEG index, may also have the potential to reflect the change in the subjective presence level. Specifically, an increase in the subjective presence level was found to be correlated with a decrease in PAF. Together with our previous study, these results indicate the potential use of EEG for the objective measurement of presence in the future.

This paper presents a method for calculating the insertion loss of barriers on the ground. The method combines the classic imaging method for an analysis of sound propagation with the secondary source model for diffraction by barriers in the time domain, which is different from other frequency-domain methods. The validity of the proposed method is verified by comparing the theoretical results with those derived from the MacDonald method, and a valid approximation for the infinite barrier is discussed.

This study investigates the diffraction effects of the IEC 63034 standard micro–baffle (SMB) on the frequency response (FR) measurements of microspeakers based on the extended Biot–Tolstoy–Medwin technique. Two different cases with and without the consideration of the backward diffractions of the SMB were investigated, which correspond to different practical measurement conditions of microspeaker drivers and closed-box microspeaker modules. The experimental results obtained were consistent with the theoretical analysis and numerical calculations. Normalized FR curves characterizing the SMB diffraction effects were presented, which can be used to compensate the FRs measured on the SMB to obtain the results measured on an ideal infinite baffle and eliminate the SMB diffraction effects.

Evolutionary Algorithms (EAs) based Unmanned Aerial Vehicle (UAV) path planners have been extensively studied for their effectiveness and high concurrency. However, when there are many obstacles, the path can easily violate constraints during the evolutionary process. Even if a single waypoint causes a few constraint violations, the algorithm will discard these solutions. In this paper, path planning is constructed as a multi-objective optimization problem with constraints in a three-dimensional terrain scenario. To solve this problem in an effective way, this paper proposes an evolutionary algorithm based on multi-level constraint processing (ANSGA-III-PPS) to plan the shortest collision-free flight path of a gliding UAV. The proposed algorithm uses an adaptive constraint processing mechanism to improve different path constraints in a three-dimensional environment and uses an improved adaptive non-dominated sorting genetic algorithm (third edition—ANSGA-III) to enhance the algorithm’s path planning ability in a complex environment. The experimental results show that compared with the other four algorithms, ANSGA-III-PPS achieves the best solution performance. This not only validates the effect of the proposed algorithm, but also enriches and improves the research results of UAV path planning.

Presence is used to assess the subjective experience of being in one place when physically situated in another. Recently, the research on presence has gained increasing attention due to the wide use of immersive audio technologies. Currently, the most widely-used measurement of presence is based on post-experiment self-report questionnaires. It is reliable but imperfect due to the psychological changes caused by the act of answering the questionnaire when immersed in the virtual environment. Therefore, the present work aims to find an objective way to measure presence, and electroencephalography (EEG) was investigated as a possible tool for this objective measurement. In this study, two listening tests were conducted, where eight loudspeakers were used to reproduce urban soundscapes to stimulate auditory presence. Presence was measured by both questionnaires and EEG. Results showed a significant correlation between T/B (Theta/Beta Ratio) extracted from EEG and subjective presence levels assessed by questionnaires, suggesting the possible use of EEG to measure presence objectively. This study could bring some insight for the research of presence, and related technologies, such as VR, video games and immersive audio production.

For most of differential evolution (DE) algorithm variants, premature convergence is still challenging. The main reason is that the exploration and exploitation are highly coupled in the existing works. To address this problem, we present a novel DE variant that can symmetrically decouple exploration and exploitation during the optimization process in this paper. In the algorithm, the whole population is divided into two symmetrical subpopulations by ascending order of fitness during each iteration; moreover, we divide the algorithm into two symmetrical stages according to the number of evaluations (FEs). On one hand, we introduce a mutation strategy, DE/current/1, which rarely appears in the literature. It can keep sufficient population diversity and fully explore the solution space, but its convergence speed gradually slows as iteration continues. To give full play to its advantages and avoid its disadvantages, we propose a heterogeneous two-stage double-subpopulation (HTSDS) mechanism. Four mutation strategies (including DE/current/1 and its modified version) with distinct search behaviors are assigned to superior and inferior subpopulations in two stages, which helps simultaneously and independently managing exploration and exploitation in different components. On the other hand, an adaptive two-stage partition (ATSP) strategy is proposed, which can adjust the stage partition parameter according to the complexity of the problem. Hence, a two-stage differential evolution algorithm with mutation strategy combination (TS-MSCDE) is proposed. Numerical experiments were conducted using CEC2017, CEC2020 and four real-world optimization problems from CEC2011. The results show that when computing resources are sufficient, the algorithm is competitive, especially for complex multimodal problems.

Particle swarm optimization (PSO) has the disadvantages of easily getting trapped in local optima and a low search accuracy. Scores of approaches have been used to improve the diversity, search accuracy, and results of PSO, but the balance between exploration and exploitation remains sub-optimal. Many scholars have divided the population into multiple sub-populations with the aim of managing it in space. In this paper, a multi-stage search strategy that is dominated by mutual repulsion among particles and supplemented by attraction was proposed to control the traits of the population. From the angle of iteration time, the algorithm was able to adequately enhance the entropy of the population under the premise of satisfying the convergence, creating a more balanced search process. The study acquired satisfactory results from the CEC2017 test function by improving the standard PSO and improved PSO.

Nonlinear acoustic damping is a key nonlinearity in miniature loudspeakers when the air velocity is at a high amplitude. Measurement of nonlinear acoustic damping is beneficial for predicting and analyzing the performance of miniature loudspeakers. However, the general measuring methods for acoustic impedance, such as the standing-wave tube method or the impedance tube method, are not applicable in this scenario because the nonlinear acoustic damping in miniature loudspeakers is coupled with other system nonlinearities. In this study, a measurement method based on nonlinear system identification was constructed to address this issue. The nonlinear acoustic damping was first theoretically analyzed and then coupled in an equivalent circuit model (ECM) to describe the full dynamics of miniature loudspeakers. Based on the ECM model, the nonlinear acoustic damping was identified using measured electrical data and compared with theoretical calculations. The satisfactory agreement between the identification and theoretical calculations confirms the validity of the proposed identification method.

The estimation of the late reverberant spectral variance (LRSV) is of paramount importance in most reverberation suppression algorithms. This letter proposes an improved single-channel LRSV estimator based on Habets LRSV estimator by using an adaptive parameter estimator. Instead of estimating the direct-to-reverberation ratio (DRR), the proposed LRSV estimator directly estimates the parameter κ in a generalized statistical model since the experimental results show that even the κ calculated using measured ground truth DRR may not be the optimal parameter for the LRSV estimator. Experimental results using synthetic reverberant signals demonstrate the superiority of the proposed estimator to conventional approaches.

Piezoelectric microelectromechanical system (MEMS) loudspeakers have received extensive attention in recent years. In particular, the piezoelectric cantilever MEMS loudspeaker, which uses multilayer piezoelectric cantilever actuators (MPCAs), has attracted attention because of its small size, low cost, ease of manufacture, and desirable piston movement. However, owing to the complex driving principles of MPCAs, no adequately efficient and appropriate method currently exists that can be used to analyze and predict the performance of piezoelectric cantilever MEMS loudspeakers. In this study, the equivalent circuit method (ECM) is adopted to theoretically model piezoelectric cantilever MEMS loudspeakers, and an ECM model with a special MPCA transformer for electromechanical conversion is proposed. With the proposed ECM model, the performance characteristics of piezoelectric cantilever MEMS loudspeakers, such as the displacement and sound pressure response, can be calculated efficiently and conveniently. To verify the accuracy of the ECM model, the finite element method is adopted for simulation, and the simulated results are compared with those of the ECM models. A satisfactory agreement was found, which verifies the accuracy of the proposed ECM model.

Along with the rapid development of advanced driving assistance systems for intelligent vehicles, essential functions such as forward collision warning and collaborative cruise control need to detect the relative positions of surrounding vehicles. This paper proposes a relative planar localization system based on the ultra-wideband (UWB) ranging technology. Three UWB modules are installed on the top of each vehicle. Because of the limited space on the vehicle roof compared with the ranging error, the traditional triangulation method leads to significant positioning errors. Therefore, an optimal localization algorithm combining homotopy and the Levenberg–Marquardt method is first proposed to enhance the precision. The triangular side lengths and directed area are introduced as constraints. Secondly, a UWB sensor error self-correction method is presented to further improve the ranging accuracy. Finally, we carry out simulations and experiments to show that the presented algorithm in this paper significantly improves the relative position and orientation precision of both the pure UWB localization system and the fusion system integrated with dead reckoning.

Swarm-based algorithm can successfully avoid the local optimal constraints, thus achieving a smooth balance between exploration and exploitation. Salp swarm algorithm (SSA), as a swarm-based algorithm on account of the predation behavior of the salp, can solve complex daily life optimization problems in nature. SSA also has the problems of local stagnation and slow convergence rate. This paper introduces an improved salp swarm algorithm, which improve the SSA by using the chaotic sequence initialization strategy and symmetric adaptive population division. Moreover, a simulated annealing mechanism based on symmetric perturbation is introduced to enhance the local jumping ability of the algorithm. The improved algorithm is referred to SASSA. The CEC standard benchmark functions are used to evaluate the efficiency of the SASSA and the results demonstrate that the SASSA has better global search capability. SASSA is also applied to solve engineering optimization problems. The experimental results demonstrate that the exploratory and exploitative proclivities of the proposed algorithm and its convergence patterns are vividly improved.

The collision warning system (CWS) plays an essential role in vehicle active safety. However, traditional distance-measuring solutions, e.g., millimeter-wave radars, ultrasonic radars, and lidars, fail to reflect vehicles’ relative attitude and motion trends. In this paper, we proposed a vehicle-to-vehicle (V2V) cooperative collision warning system (CCWS) consisting of an ultra-wideband (UWB) relative positioning/directing module and a dead reckoning (DR) module with wheel-speed sensors. Each vehicle has four UWB modules on the body corners and two wheel-speed sensors on the rear wheels in the presented configuration. An over-constrained localization method is proposed to calculate the relative position and orientation with the UWB data more accurately. Vehicle velocities and yaw rates are measured by wheel-speed sensors. An extended Kalman filter (EKF) is applied based on the relative kinematic model to combine the UWB and DR data. Finally, the time to collision (TTC) is estimated based on the predicted vehicle collision position. Furthermore, through UWB signals, vehicles can simultaneously communicate with each other and share information, e.g., velocity, yaw rate, which brings the potential for enhanced real-time performance. Simulation and experimental results show that the proposed method significantly improves the positioning, directing, and velocity estimating accuracy, and the proposed system can efficiently provide collision warning.

The program material is of great importance for the results of the listening tests on loudspeakers, while the process of how to select the program material remains ambiguous. This paper investigates the criterion for selecting programs suitable for low-frequency perceptual evaluation based on the audio features of the program. A listening test was conducted to identify the more discriminating and revealing programs in the low-frequency range. Based on the listening test results, various characteristics of the programs, including dynamic, timbral, rhythmic, and spectral features, were extracted. Their relationships with the program’s discrimination ability are discussed. The results suggest that programs with a slow and clear rhythm and a smooth and even spectrum in the whole band are more discriminating in detecting the spectral differences in the low frequencies. By using these significant features, a discriminant analysis was performed to predict the ability of the program to reveal the spectral irregularities. The predictive accuracy of the derived discriminant function was 95% in separating the discriminating and undiscriminating programs.

The swarm intelligence algorithm has become an important method to solve optimization problems because of its excellent self-organization, self-adaptation, and self-learning characteristics. However, when a traditional swarm intelligence algorithm faces high and complex multi-peak problems, population diversity is quickly lost, which leads to the premature convergence of the algorithm. In order to solve this problem, dimension entropy is proposed as a measure of population diversity, and a diversity control mechanism is proposed to guide the updating of the swarm intelligence algorithm. It maintains the diversity of the algorithm in the early stage and ensures the convergence of the algorithm in the later stage. Experimental results show that the performance of the improved algorithm is better than that of the original algorithm.

Proposing new strategies to improve the optimization performance of differential evolution (DE) is an important research study. The jSO algorithm was the announced winner of the Congress on Evolutionary Computation (CEC) 2017 competition on numerical optimization, and is the state-of-the-art algorithm in the SHADE (Success-History based Adaptive Differential Evolution) algorithm series. However, the jSO algorithm converges prematurely in the search space with different dimensions and is prone to falling into local optimum during evolution, as well as the problem of decreasing population diversity. In this paper, a modified jSO algorithm (MjSO) is proposed which is based on cosine similarity with parameter adaptation and a novel opposition-based learning restart mechanism incorporated with symmetry to address the above problems, respectively. Moreover, it is well known that parameter setting has a significant impact on the performance of the algorithm and the search process can be divided into two symmetrical parts. Hence, a parameter control strategy based on a symmetric search process is introduced in the MjSO. The effectiveness of these designs is supported by presenting a population clustering analysis, along with a population diversity measure to evaluate the performance of the proposed algorithm, three state-of-the-art DE variant algorithms (EBLSHADE, ELSHADE-SPACMA, and SALSHADE-cnEPSin) and two original algorithms (jSO and LSHADE) are compared with it, for solving 30 CEC’17 benchmark functions and three classical engineering design problems. The experimental results and analysis reveal that the proposed algorithm can outperform other competitions in terms of the convergence speed and the quality of solutions. Promisingly, the proposed method can be treated as an effective and efficient auxiliary tool for more complex optimization models and scenarios.

Single objective optimization algorithms are the foundation of establishing more complex methods, like constrained optimization, niching and multi-objective algorithms. Therefore, improvements to single objective optimization algorithms are important because they can impact other domains as well. This paper proposes a method using turning-based mutation that is aimed to solve the problem of premature convergence of algorithms based on SHADE (Success-History based Adaptive Differential Evolution) in high dimensional search space. The proposed method is tested on the Single Objective Bound Constrained Numerical Optimization (CEC2020) benchmark sets in 5, 10, 15, and 20 dimensions for all SHADE, L-SHADE, and jSO algorithms. The effectiveness of the method is verified by population diversity measure and population clustering analysis. In addition, the new versions (Tb-SHADE, TbL-SHADE and Tb-jSO) using the proposed turning-based mutation get apparently better optimization results than the original algorithms (SHADE, L-SHADE, and jSO) as well as the advanced DISH and the jDE100 algorithms in 10, 15, and 20 dimensional functions, but only have advantages compared with the advanced j2020 algorithm in 5 dimensional functions.

The recycling and treatment of wastewater using microbial fuel cells (MFCs) has been attracting significant attention as a way to control energy crises and water pollution simultaneously. Despite all efforts, MFCs are unable to produce high energy or efficiently treat pollutants due to several issues, one being the anode’s material. The anode is one of the most important parts of an MFC. Recently, different types of anode materials have been developed to improve the removal rate of pollutants and the efficiency of energy production. In MFCs, carbon-based materials have been employed as the most commonly preferred anode material. An extensive range of potentials are presently available for use in the fabrication of anode materials and can considerably minimize the current challenges, such as the need for high quality materials and their costs. The fabrication of an anode using biomass waste is an ideal approach to address the present issues and increase the working efficiency of MFCs. Furthermore, the current challenges and future perspectives of anode materials are briefly discussed.

Security and efficiency are the two main challenges for designing a smart home system. In this paper, by incorporating Chinese remainder theorem (CRT) into the elliptic curve Diffie−Hellman (ECDH), a lightweight key agreement protocol for smart home systems is constructed. Firstly, one-way hash authentication is used to identify the sensor nodes instead of mutual authentication to reduce the authentication cost. Secondly, the CRT is introduced to enhance the security of the original ECDH key agreement. Security analysis showed that the proposed protocol can validate the data integrity and resist the replay attack, the man-in-middle attack, and other attacks. Performance analysis and experiments showed that the protocol achieves high security with low communication and computation costs, and can be implemented in smart home systems.

Scene recognition is an essential part in the vision-based robot navigation domain. The successful application of deep learning technology has triggered more extensive preliminary studies on scene recognition, which all use extracted features from networks that are trained for recognition tasks. In the paper, we interpret scene recognition as a region-based image retrieval problem and present a novel approach for scene recognition with an end-to-end trainable Multi-column convolutional neural network (MCNN) architecture. The proposed MCNN utilizes filters with receptive fields of different sizes to have Multi-level and Multi-layer image perception, and consists of three components: front-end, middle-end and back-end. The first seven layers VGG16 are taken as front-end for two-dimensional feature extraction, Inception-A is taken as the middle-end for deeper learning feature representation, and Large-Margin Softmax Loss (L-Softmax) is taken as the back-end for enhancing intra-class compactness and inter-class-separability. Extensive experiments have been conducted to evaluate the performance according to compare our proposed network to existing state-of-the-art methods. Experimental results on three popular datasets demonstrate the robustness and accuracy of our approach. To the best of our knowledge, the presented approach has not been applied for the scene recognition in literature.

Tobacco is an essential economic crop in China. The detection of tobacco plants in aerial images plays an important role in the management of tobacco plants and, in particular, in yield estimations. Traditional yield estimation is based on site inspections, which can be inefficient, time-consuming, and laborious. In this paper, we proposed an algorithm to detect tobacco plants in RGB aerial images automatically. The proposed algorithm is comprised of two stages: (1) A candidate selecting algorithm extracts possible tobacco plant regions from the input, (2) a trained CNN (Convolutional Neural Network) classifies a candidate as either a tobacco-plant region or a nontobacco-plant one. This proposed algorithm is trained and evaluated on different datasets. It demonstrates good performance on tobacco plant detection in aerial images and obtains a significant improvement on AP (Average Precision) compared to faster R-CNN (Regions with CNN features) and YOLOv3 (You Only Look Once v3).

The reconnection of broken edges is an effective way to avoid drawback for the commons in past studies. Inspired by this, we proposed a public goods game model under the edges rules, where we evaluate the weight of edges by their nodes’ payoff. The results proved that the game obtains a larger range of cooperation with a small gain factor by this proposed model by consulting Monte Carlo simulations (MCS) and real experiments. Furthermore, as the following the course of game and discussing the reason of cooperation, in the research, we found that the distribution entropy of the excess average degree is able to embody and predict the presence of cooperation.

Since the beginning of the 21st century, research on artificial intelligence has made great progress. Bayesian networks have gradually become one of the hotspots and important achievements in artificial intelligence research. Establishing an effective Bayesian network structure is the foundation and core of the learning and application of Bayesian networks. In Bayesian network structure learning, the traditional method of utilizing expert knowledge to construct the network structure is gradually replaced by the data learning structure method. However, as a result of the large amount of possible network structures, the search space is too large. The method of Bayesian network learning through training data usually has the problems of low precision or high complexity, which make the structure of learning differ greatly from that of reality, which has a great influence on the reasoning and practical application of Bayesian networks. In order to solve this problem, a hybrid optimization artificial bee colony algorithm is discretized and applied to structure learning. A hybrid optimization technique for the Bayesian network structure learning method is proposed. Experimental simulation results show that the proposed hybrid optimization structure learning algorithm has better structure and better convergence.

The in-vehicle controller area network (CAN) bus is one of the essential components for autonomous vehicles, and its safety will be one of the greatest challenges in the field of intelligent vehicles in the future. In this paper, we propose a novel system that uses a deep neural network (DNN) to detect anomalous CAN bus messages. We treat anomaly detection as a cross-domain modelling problem, in which three CAN bus data packets as a group are directly imported into the DNN architecture for parallel training with shared weights. After that, three data packets are represented as three independent feature vectors, which corresponds to three different types of data sequences, namely anchor, positive and negative. The proposed DNN architecture is an embedded triplet loss network that optimizes the distance between the anchor example and the positive example, makes it smaller than the distance between the anchor example and the negative example, and realizes the similarity calculation of samples, which were originally used in face detection. Compared to traditional anomaly detection methods, the proposed method to learn the parameters with shared-weight could improve detection efficiency and detection accuracy. The whole detection system is composed of the front-end and the back-end, which correspond to deep network and triplet loss network, respectively, and are trainable in an end-to-end fashion. Experimental results demonstrate that the proposed technology can make real-time responses to anomalies and attacks to the CAN bus, and significantly improve the detection ratio. To the best of our knowledge, the proposed method is the first used for anomaly detection in the in-vehicle CAN bus.

Nondestructive and fast measurement and characterization of fish is highly desired during various processing treatments. This research investigated the effectiveness of low field LF-NMR and MRI as fast monitoring techniques to estimate the qualities of half-smooth tongue sole fillets treated with edible coating combined with modified atmosphere packaging during refrigeration. T2 relaxation spectra showed three peaks representing bound water (T21), immobile water (T22), and free water (T23), respectively. pT22 accounted for the largest proportion of three types of water, followed by pT23. The weighted MRI provided the internal structure information associated with different samples, indicting the combination of edible coating and MAP (70% CO2 + 30% N2) is the best performance in the maintenance of qualities and freshness of HTS fillets. All results demonstrated that the combination of LF-NMR and MRI as fast and nondestructive methods have great potential to monitor qualities deterioration and predict shelf life in of HTS fillets during refrigerated storage.