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• The LMS-VRR scheme has been proposed to multiplex/demultiplex acoustic OAM beams in complex environments. • The acoustic OAM beams with up to 40 channels have been multiplexed/demultiplexed. • The acoustic OAM beams have been multiplexed/demultiplexed over 20 m away from the randomly arranged sound sources. The spiral sound waves carrying orbital angular momentum (OAM) are significantly more efficient in delivering information and energy than the ordinary plane waves. A major bottleneck in exploring the use of acoustic OAM is the excitation and detection of OAM in an arbitrary environment. Here we propose a scheme combining the multichannel least-mean-square (LMS) algorithm and an improved virtual rotating receiver (VRR) method to multiplex and demultiplex acoustic OAM beams in various complex environments. We experimentally demonstrate that the LMS-VRR scheme can excite and detect the OAM beams in indoor and outdoor real-world environments. We use the LMS algorithm to multiplex OAM beams with up to 40 channels at a circular microphone array by compensating the source signals with the equalization filters. On the other hand, the circular microphone array works as a VRR to demultiplex the OAM beams from the rotational Doppler effect. Additional preprocesses are taken to eliminate the plane wave pollution for the VRR method. The acoustic OAM beams have been multiplexed and demultiplexed over 20 m away from the sound sources on a shady lane. Our work has shown practical prospects in covert communication, and shall further unleash the application potential of acoustic OAM beams by removing the restrictions on exciting and detecting OAM in complex environments. [ABSTRACT FROM AUTHOR]

Dynamic area coverage algorithm is often used in multiple unmanned aerial vehicle (multi-UAV) systems to realize searching or monitoring an area of interest (AOI). Improving coverage efficiency is one of the important research issues of dynamic area coverage. In this paper, we propose a distributed dynamic area coverage algorithm based on a reinforcement learning (RL) algorithm to enhance the coverage efficiency in complex application environments. A coverage information fusion based discrete soft actor–critic algorithm (herein FDSAC) is proposed to solve the non-stationary problem of RL algorithm in area coverage task under the communication constraint environment. Considering the equivalence of UAVs in the area coverage tasks, we introduce a multi-UAV cooperative learning method for FDSAC by sharing the strategy and experience of the UAVs to accelerate the learning speed of FDSAC in multi-UAV system. With the FDSAC algorithm, the UAVs can intelligently select the best coverage points and achieve almost optimal coverage point planning for the entire coverage process. In addition, we also provide a coverage point adjustment method based on the distribution of obstacles to enable the proposed algorithm adapt to obstacle environments. The experimental results demonstrate that the proposed algorithm can efficiently complete the area coverage task in complex communication and stochastic control noise environment. The good scalability, environment adaptability, and practicality of our algorithm have been also confirmed in different complex environments of different experimental platforms. [ABSTRACT FROM AUTHOR]

• β-FeOOH/N,P-MXene Schottky-junction is treated using a simple hydrothermal method. • The doping of nitrogen and phosphorus elements not only enhances the photoelectric properties,but also adds the active site. • The FO-NPM200/H 2 O 2 exhibits a consistently high TC removal rate across a wide pH range. • The FO-NPM200 have high degradation stability and recycling performance. Effective separation of photogenerated electrons and holes, as well as providing ample reactive sites, is crucial for enhancing the activity of photocatalytic degradation of pollutants in water. In this study, we have expanded MXene through a simple water swelling method and successfully doped N and P elements into MXene via a hydrothermal process. This modification not only exfoliates the layered structure of MXene, enlarging its specific surface area and providing abundant reactive sites but also endows MXene with enhanced metallic properties. As a result, an N,P-MXene/FeOOH Schottky junction photocatalyst is constructed. Our photocatalytic experiments revealed that the FO-NPM200/H 2 O 2 system performs well under extreme pH conditions and exhibits excellent photocatalytic stability across a wide pH range (1–13), suggesting its outstanding potential for practical applications in complex environments. Under conditions of pH = 7 without the presence of inorganic salt ions, the degradation rate of tetracycline reached 98.1 %, and the photocatalytic reduction rate of the common heavy metal salt Cr(VI) was as high as 99.4 %. We have also unveiled the band structure of FO-NPM200 using UPS and UV–vis DRS. This research paves a new way for the design and construction of Schottky junction photocatalysts. [ABSTRACT FROM AUTHOR]

Future conflicts will increasingly take place in complex, urban environments. This calls for a change from operating in large formations and from static encampments to dispersed, networked, decentralized and small-scale operations. Concept development for dispersed operations and their accompanying strategies has only just begun. To aid in this early phase of concept development, this study presents (i) a framework for modeling the effects of communication strategies during dispersed land operations in a complex environment using the Agent-Based Modeling method, and (ii) a case study to illustrate the framework using the research question 'What are the effects of communication strategies that make use of either a cloud or bundling on the shared Situational Awareness of units in dispersed operations in a complex environment?', using a peace enforcement scenario in an urban area. [ABSTRACT FROM AUTHOR]

• A dual-branch neural network model is proposed for crop disease recognition. • A new frequency feature extraction network was constructed using the ghost bottleneck module. • A new MASF module has been proposed to fuse frequency and spatial information features. • CV_bias loss function is to reduce the misleading problem in the random prediction process. Crop diseases severely affect crop yield and quality, and accurate identification of crop diseases is crucial for disease management. Although deep neural networks have made progress in the task of crop disease identification, the complex environment of crop diseases, including background interference, morphological differences, and scale variations, has led to limited accuracy in disease recognition. To address these issues, this study proposes a dual-branch deep neural network for crop disease identification, integrating both frequency domain and spatial domain information. The frequency branch takes frequency domain information as input to extract rich crop disease frequency component features, while the deformable attention Transformer branch excels in representing global features and selectively focusing on local features of crop diseases. A new fusion method, Multi-Spectral Channel Attention Fusion (MSAF), is adopted to better integrate crop disease frequency and spatial features. Additionally, an improved bias loss function (cv_bias) is proposed to optimize the dual-branch network model, achieving an accuracy of 96.7 % on the test dataset, which is 2.0 % higher than the existing state-of-the-art deformable attention Transformer model. With only 14 M model parameters, this study's model provides an effective method for future applications in complex environment. [ABSTRACT FROM AUTHOR]

Accurate simulation of pollution diffusion under uneven terrain with complicated obstacles is a difficult but important scientific and technological issue. The inlet-boundary was important and single boundary meteorological factors (e.g., wind direction and speed) input were generally employed for the Computational Fluid Dynamics (CFD) simulation. However, for uneven terrain with complicated obstacles, the boundary meteorological condition may be quite different, single boundary input may bring large simulation uncertainty. Consequently, the impact of different inlet boundary number and locations on CFD simulation was investigated based on a scarce tracer-gas field experiment carried out in a chemical industrial park located in a mountainous area. The experimental results implied gas diffusion was influenced by a combination of meteorological conditions from multi-inlet boundaries. The simulation results confirmed that and indicated commonly-used single-inlet boundary input can bring great inaccuracies. The case of inlet boundaries closed to emission sources and intersected the crosswind plane agreed best with measurements. It was also found that the inlet boundary number and locations significantly influence velocity and turbulence. Finally, a guideline for determining the inlet boundaries was proposed. This study can enhance the understanding on CFD simulation, and is beneficial for the selection of inlet meteorological sites to precise prediction. [ABSTRACT FROM AUTHOR]

Accurate simulation of pollution diffusion under uneven terrain with complicated obstacles is a difficult but important scientific and technological issue. The inlet-boundary was important and single boundary meteorological factors (e.g., wind direction and speed) input were generally employed for the Computational Fluid Dynamics (CFD) simulation. However, for uneven terrain with complicated obstacles, the boundary meteorological condition may be quite different, single boundary input may bring large simulation uncertainty. Consequently, the impact of different inlet boundary number and locations on CFD simulation was investigated based on a scarce tracer-gas field experiment carried out in a chemical industrial park located in a mountainous area. The experimental results implied gas diffusion was influenced by a combination of meteorological conditions from multi-inlet boundaries. The simulation results confirmed that and indicated commonly-used single-inlet boundary input can bring great inaccuracies. The case of inlet boundaries closed to emission sources and intersected the crosswind plane agreed best with measurements. It was also found that the inlet boundary number and locations significantly influence velocity and turbulence. Finally, a guideline for determining the inlet boundaries was proposed. This study can enhance the understanding on CFD simulation, and is beneficial for the selection of inlet meteorological sites to precise prediction. [ABSTRACT FROM AUTHOR]

• Using ER3BP as an accurate model for governing dynamic. • Finding periodic orbits with proper initial guess and correction algorithm. • Coupling orbital and attitude dynamic through SCM. • Searching for periodic attitude behavior of different spacecraft structures along periodic orbit. • Refine full coupled periodic solution in orbit-attitude ER3BP. A way to improve the accuracy of the three-body problem model is taking into account the eccentricity of primary attractors. Elliptic Restricted Three-Body Problem (ER3BP) is a model for studying spacecraft trajectory within the three-body problem such that the orbital eccentricity of primaries is reflected in it. As the principal cause of perturbation in the employed dynamical model, the primaries eccentricity changes the structure of orbits compared to the ideal Circular Restricted Three-Body Problem (CR3BP). It also changes the attitude behavior of a spacecraft revolving along periodic orbits in this regime. In this paper, the coupled orbit-attitude dynamics of a spacecraft in the ER3BP are exploited to find precise periodic solutions as the spacecraft is considered to be in planar orbits around Lagrangian points and Distant Retrograde Orbits (DRO). Periodic solutions are repetitious behaviors in which spacecraft whole dynamics are repeated periodically, these periodic behaviors are the main interest of this study because they are beneficial for future mission designs and allow delineation of the system's governing dynamics. Previous studies laid the foundation for spacecraft stability analysis or studying pitch motion of spacecraft in the ER3BP regime. While in this paper, at first, initial guesses for correction algorithms were derived through verified search methods, then correction algorithms were used to refine calculated orbit-attitude periodic behaviors. Periodic orbits and full periodic solutions are portrayed and compared to previous studies and simpler models. Natural periodic solutions are valuable information eventuate in the longer functional lifetime of spacecraft. Since the problem assumption considered in this paper is much closer to real mission conditions, these results may be the means to use natural bounded motions in the actual operational environment. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV) systems are required to independently make timely and accurate attack and control strategies according to dynamic battlefield situations. To optimize the trajectory control of UAVs for ground penetration combat, a 3-degree-of-freedom model and a dynamic model of UAV were established. Considering the constraints of initial and terminal conditions and flight performance, the penetration combat model under multiple constraints is established. A dynamic radar cross section (RCS) threat model is demonstrated based on characteristics of RCS. Combined with the hp-adaptive-Radau pseudo-spectral method and the receding horizon control algorithm under the optimal control model, the flight time and the threat probability of the UAV to the combat area were taken as the objective functions. An algorithm to adaptively adjust the weight coefficient of the performance index and switch the sub-objective function was used. The simulation results show that the proposed method can realize autonomous control of UAV attack trajectory under complex battlefield environment and multiple constraints. It can generate UAV flight path with little threat and short time and output relevant state and control parameters. [ABSTRACT FROM AUTHOR]

Substandard discharge of industrial wastewater leads to abundant silver ions (Ag+) emission into water and soil, causing serious environmental threat. Precise and reliable detection of Ag+ is vital for environmental surveillance, while it remains a great challenge in complex actual environment. Herein, a lab-to-factory adoptable photoelectrochemical (PEC) approach for highly selective and sensitive detection of Ag+ in industrial wastewater has been first demonstrated. BiOI nanosheets (NSs) were prepared as photocathode and showed a spontaneous and selective ion-exchange process with Ag+ ions when it immersed into complicated industrial wastewater. The in situ generated AgI/Ag exhibits unique localized surface plasmonic resonance (LSPR) effect to modulate PEC signals for highly selective and sensitive Ag+ detection. As a result, the prepared photocathode delivers an ultralow detection limit (0.21 nM) and a broad linear range (up to 300 μM), outperforming most reported results. This work opens a feasible avenue for Ag+ monitoring in actual environment. • BiOI photocathode can selectively recognize and ion-exchange with Ag+ in complex industrial wastewater. • In situ generated AgI/Ag exhibits unique localized surface plasmonic resonance effect to realize Ag+ detection. • Superior detection property with ultralow detection limit of 0.21 nM and wide linear range up to 300 μM is achieved. • The stable BiOI photocathode show promising potential for practical industrial application. [ABSTRACT FROM AUTHOR]

• Simulated corrosion tests of cementitious composites under complex coastal environment were carried out. • Synergistic effect of nano-SiO 2 and PVA fiber on durability of cementitious composites under complex environment was revealed. • Pore structure and size distribution of cementitious composites under complex environment were analyzed. • Microcosmic enhancement mechanism of nano-SiO 2 and PVA fiber was illustrated. As the most commonly used construction material for marine buildings, cementitious composites are exposed to a complex environment of coupled action of relative humidity, temperature, and chloride salts erosion, which severely impacts the safety and longevity of marine buildings. The effect of nano-SiO 2 dosage on the durability of polyvinyl alcohol fiber-reinforced cementitious composites (PVAF-RCC) for the preparation of marine buildings under the complex environment was investigated in this work. The complex environment with the temperature of 50 °C, relative humidity of 100%, and chloride salt concentration of 5% was simulated by the environment chamber. The cementitious composites exposed to the complex environment for 30 d were tested for anti-permeability, anti-chloride ion penetration, frost resistance, and chloride salt erosion resistance. Furthermore, the pore characteristics of the cementitious composites and the micromorphology of nano-SiO 2 , matrix, and fibers were characterized and analyzed by the combination of scanning electron microscope and mercury intrusion porosimetry tests. Results revealed that the durability of cementitious composites improved initially and subsequently decreased with rising the dose of nano-SiO 2 under the complex environment. PVAF-RCC consisting of 1.5% nano-SiO 2 obtained the best durability. As the nano-SiO 2 dosage increased, the total porosity and cumulative porosity of PVAF-RCC decreased first and subsequently increased. Nano-SiO 2 refined and improved the pore structure of PVAF-RCC and the interface between matrix and PVA fibers through its filling effect and participation in the hydration reaction to generate C-S-H gels with filling effect to convert more harmful and harmful pores into less harmful or harmless pores, thus enhancing the compactness and durability of cementitious composites under the complex environment. [ABSTRACT FROM AUTHOR]

In some complex environment, the train and the base station (BS) cannot connect directly. And the train-mounted mobile terminals cannot receive global positioning system (GPS) signals because of the shadow of the train shells. To address this issue, an unmanned aerial vehicle (UAV) aided localization algorithm with relay is proposed in this paper, which consists of four steps: coordinates dividing, step-by-step positioning, coupling and results comparison. The proposed method transforms the complex in-train environment into a traditional indoor scene to avoid the influence of non-line-of-sight (NLOS). It locates the absolute position of the train in a wireless sensor network (WSN) unit composed of nodes installed on UAVs. In addition, aiming at reducing the distance-measuring errors caused by velocity difference between the train and the UAV, an error compensation factor is carried out in received signal strength indicator (RSSI). Based on the simulation results, we find that the proposed method is more accurate working in confined space where GPS signals cannot be captured. Compared with traditional methods, it can also correct the velocity difference errors. The positioning system achieves a mean positioning accuracy of 2.13 m. [ABSTRACT FROM AUTHOR]

Collision avoidance problems of a swarm of unmanned air vehicles (UAVs) flying in an obstacle-laden environment are investigated in this paper. Given that the UAV swarm needs to adapt to the obstacle distribution in dynamic operation, a priority configuration is designed to guide the UAVs to pass through the obstacles in turn. Based on the collision cone approach and the prediction of the collision time, a collision evaluation model is established to judge the urgency of the imminent collision of each UAV, and the evaluation result is used to assign the priority of each UAV to further instruct them going through the obstacles in descending order. At last, the simulation results provide the promising validation in terms of the efficiency and scalability of the proposed approach. [ABSTRACT FROM AUTHOR]

In this paper, an end-to-end learning framework—RGB enhanced point cloud fusion network (REPF-Net) is designed based on the complementary and enhanced fusion of multi-sensor (camera and lidar) data, aiming to realize the accurate perception of the robot in a random complex environment. In other words, the three-dimensional position and geometric shape of environmental targets can be obtained excellently and the accuracy of target recognition can be improved. Meanwhile, three key problems in 3D perception tasks were solved including, enhancing the fusion point cloud with RGB feature points, achieving effective fusion of heterogeneous data, and enhancing the skeleton information of the target point set. Point-by-point channel attention network (PCA-Net) is proposed to obtain the deep image features. The loss of attention consistent execution (ACE) loss function is proposed to overcome the inconsistencies of localization and classification confidence. Numerous experiments on the KITTI dataset prove that the proposed REPF-Net is superior to the currently common advanced 3D sensing algorithms. [ABSTRACT FROM AUTHOR]

Target identification is one of the most popular radar uses in real life. Target identification is a classifier that analyzes whether a signal contains an echo from a target (target-present) or is merely noise (target-absent). Deep learning techniques are a popular topic in classification, and they have evinced to be effective in a range of applications. In this paper, a 64 layers Circular Disk type RADAR Target Detection (CDRTD) model is proposed based on Transfer Learning using the SqueezeNet architecture of Convolutional Neural Network (CNN) that functions directly with processed radar target return eco signal and minimize the requirement of conventional laborious radar signal processing. Further, the proposed 64 layers SqueezeNet-based CNN CDRTD model was then implemented to identify circular disk type targets in complex environment. Finally, the target return eco data was tested to identify the circular disk type radar target in complex environments. We further analyzed target detection probability, false alarm rate, precision, recall, F1 in a complex environment and compared it with the ideal case. We found that our proposed CDRTD model can classify 83.3% of the test samples correctly with an overall accuracy of 94.59% in a noisy and cluttered environment whereas 100% of the test samples are classified correctly with an overall accuracy of 100% in an ideal environment. [ABSTRACT FROM AUTHOR]

The utilization of multiple small-sized automated ground vehicles (AGVs) with omnidirectional movement for cooperative transportation of large objects in manufacturing and logistics is a growing area of research. Utilizing multiple AGVs can improve system flexibility and efficiency compared to using a single large AGV, particularly in clustered and narrow spaces. This study presents a control framework for multiple AGVs to cooperatively transport an object in a cluster environment. The framework utilizes the rapidly-exploring random tree (RRT ∗) algorithm to plan a feasible path through the environment, and a new distributed synchronization control algorithm for each AGV to track its desired trajectory and synchronize with its neighbors. The proposed control framework can be applied in cases with time-varying communication delays and networks with directed spanning tree connections, making it more versatile than existing synchronization control methods. Simulation and experimental results are provided to validate the proposed framework. [ABSTRACT FROM AUTHOR]

• A novel method using deep learning-based amplification and tracking is proposed to measure the micro-vibration. • This method can be used under complex light fields with different illumination and backgrounds. • The target object is first magnified and tracked, and then converted into actual displacement values. A new deep learning-based vision measurement method is proposed to accurately measure the micro-vibration displacements of objects in different illuminations and backgrounds. The measurement method pre-process the video, then the deep learning correlation methods are used to zoom in the target object and track the vibration trajectory, and the pixel displacement is converted to actual displacement by pixel equivalents. By comparing the three sets of experiments, the proposed method has exceptional accuracy. When measuring vibration displacement of 0.1 mm, the Root Mean Square Error (RMSE) and Normalized Root Mean Square Error (NRMSE) are 0.0234 mm and 11.8601 %. By comparing with the Rectangle Detection Algorithm and the Template Matching Algorithm, the proposed algorithm outperforms these two traditional methods, especially for the complex environments. It can be concluded that this method, as a new visual measurement method, can be adapted to a variety of complex environments and can accurately measure micro-amplitude vibrations. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the network models of traditional intelligent single-pixel imaging methods have poor suppression ability in complex environments (e.g., smoke, fog, haze, etc.), a single-pixel imaging method based on random discarding mechanism (DSPINet) is proposed. The method integrates compressed sensing and deep learning. It compresses and samples the image directly, discards part of the information, and reconstructs the image with a small number of the one-dimensional detection signal. In view of the fact that most of the data of traditional single-pixel imaging methods are obtained in a controlled environment and do not have good generalization ability, this paper introduces the random discarding mechanism into the network model to simulate the light scattering loss and system noise in the complex environment. The test results show that when the simulation test's sampling rate is 10.00 %, the proposed DSPINet model is compared with the DLGI (improved), DLBGI, Bsr2-Net, and TVAL3, the structural similarity (SSIM) of reconstructed images is improved by 2.20 %, 4.49 %, 5.68 %, and 63.16 %, respectively. In the actual test of introducing the scattering medium, when the sampling rate is only 3.00–10.00 %, the reconstructed images generated by the DSPINet model have a good ability to suppress the interference and show a high reconstruction accuracy in the complex environment. [ABSTRACT FROM AUTHOR]

In this study, a multifunctional polyetherimide (PEI) nanofiber membrane decorated by Cu-based metal-organic framework (CuMOF) was proposed for high-efficient filtration/separation in complex environments. In the presence of CuMOF, the hybrid nanofiber membrane exhibited effective antibacterial ability, lotus leaf-like self-cleaning function, and enhanced particulate matter (PM) capture capacity. Significantly, the removal efficiency of resultant membrane for PM of 0.3 µm, 0.5 µm and 0.8 µm was as high as 99.94 %, 99.95 % and 99.99 %, respectively, and the filtration efficiency for particles of ≥ 1.25 µm could reach nearly 100 %. Due to the excellent stability of PEI and CuMOF, the hybrid nanofiber membrane maintained a high removal ability after high temperature (up to 200 °C), acidic and alkaline treatment, and still displayed outstanding recyclability and long-term purification capacity in seriously PM polluted level of > 800 µg/m3. Furthermore, the special selective wettability of hydrophobicity and lipophilicity, as well as the structural stability, enabled the obtained membrane present superior oil/water separation efficiency even in highly salty, acidic and alkaline environments. This work provides a facile and effect approach for the development of comprehensive nanofiber material for various filtration and separation applications. [Display omitted] [ABSTRACT FROM AUTHOR]

Automatic bayberry picking can substantially reduce labor costs and improve picking efficiency in an orchard management system. Nowadays, an automatic picking system mainly relies on machine vision to segment bayberry fruit from the background. Most existing methods are carried out in an environment where the light intensity is relatively fixed and the bayberries are unobstructed. However, due to the complexity of the growing environment, including variations in lighting and widespread occlusion, segmentation accuracy is quite limited, which affects the large-scale application of automatic picking systems. Aiming at these issues, in this study, a bayberry segmentation method based on a multi-module convolutional neural network is proposed. First, the bayberry images in a real scene were collected and preprocessed to form a dataset. Then, a convolutional neural network was constructed, with an image correction module to improve the network's robustness to natural ambient lighting. Finally, a shape completion module with a puzzle algorithm was utilized to overcome the occlusion in the natural environment. The experimental results show that the average precision of the proposed method for semantic segmentation and instance segmentation of bayberry fruit can reach 0.913 and 0.755, respectively, which outperforms the existing methods and has important significance for automatic picking in orchards. • A multi-module CNN is proposed for bayberry segmentation in complex environment. • An image correction module is adopted to make the model robust to lighting changes. • A puzzle algorithm is proposed to enhance the model's robustness to occlusion. • Ablation experiments are implemented to analyze the effect of each module seriatim. [ABSTRACT FROM AUTHOR]

Papua New Guinea's difficult and varied topography, poor transport infrastructure, changing dynamics of population and economy in recent times and understaffed and poorly financed health service present major challenges for successful delivery of vaccination and other preventative health interventions to both the rural majority and urban populations, thereby posing risks for vaccine preventable disease outbreaks in the country. The country has struggled to meet the vaccination coverage targets required for the eradication of poliomyelitis and elimination of measles. Escalation of inter and intra country migration resulting from major industrial developments, particularly in extraction industries, has substantially increased the risk of infectious disease importation. This case study documents the evolution of immunisation programmes since the introduction of supplementary immunisation activities (SIAs). Single antigen SIAs have advantages and disadvantages. In situations in which the delivery of preventative health interventions is difficult, it is likely that the cost benefit is greater for multiple than for single intervention. The lessons learned from the conduct of single antigen SIAs can be effectively used for programmes delivering multiple SIA antigens, routine immunisations, and other health interventions. This paper describes a successful and cost effective multiple intervention programme in Papua New Guinea. The review of the last SIA in Papua New Guinea showed relatively high coverage of all the interventions and demonstrated the operational feasibility of delivering multiple interventions in resource constrained settings. Studies in other developing countries such as Lesotho and Ethiopia have also successfully integrated health interventions with SIA. In settings such as Papua New Guinea there is a strong case for integrating supplementary immunisation activity with routine immunisation and other health interventions through a comprehensive outreach programme. [ABSTRACT FROM AUTHOR]

The path planning of Unmanned Aerial Vehicle (UAV) is a real-world optimization problem, and even develops into a hard optimization problem with many objectives and constraints when UAVs work in a complex environment. In a complex environment, the resulting constraints can lead to decrease the quantity of the feasible solutions, so that can bring difficulties to plan routes for UAVs. Therefore, it is necessary to design a high-quality planner for a UAV in a complex environment. In this work, we have proposed a Multi-Strategy Fusion Differential Evolution algorithm (MSFDE). The proposed algorithm integrates the multi-population strategy, the novel self-adaptive strategy and the ensemble of the interactive mutation strategy in order to balance the exploitation and exploration capabilities. The multi-population strategy is used to divide the whole population into the three indicator subpopulations and a reward subpopulation for maintaining the diversity of the whole population; the novel self-adaptive strategy is introduced to control the parameters F and CR based on the teaching-learning-based optimization method; the ensemble of the interactive mutation strategy is to exchange the information among the three indicator subpopulations on each generation for boosting the population diversity. The constraints in the UAV path planning are transformed into the objective functions by the linear weighted sum method. Scenario 1, 2, 3, and 4 are designed with different complex level, and other eight algorithms are introduced to be compared with MSFDE. The simulation results confirm that MSFDE has an outstanding performance for the UAV three-dimensional path planning in the complex environment. [ABSTRACT FROM AUTHOR]

Currently, industrial waste gas and oily wastewater are usually at high temperature and contain corrosive components (e.g., acid, alkali, oxidant, or high salt, etc.), presenting great challenges on filtration/separation materials. Here, a multi-purpose Poly(m -phenylene isophthalamide)/polyacrylonitrile/silica (PMIA/PAN/SiO 2) nanofiber composite membrane with a high yield was prepared simply via electrospinning to satisfy the demands of air filtration and oil/water separation in complex environments. Under the synergy of PMIA, PAN and SiO 2 , the composite membrane possesses high PM 0.3 removal capacity of 99.69%, robust purification ability against real smoke PM 2.5 , effective oil/water separation performance of > 99.6%, superior high temperature stability (about 250 °C) and excellent chemical resistance, showing the potential application in filtration/separation process under complex conditions. Moreover, the influence mechanism of SiO 2 NPs on mechanical properties and filtration performance was systematically investigated through experiments and simulations, paving the way for future intensive research. This study provides an option for the facile and effective preparation of high-performance filtration/separation membranes applied in the field of dust filtration and oily wastewater separation, even in harsh environments. [Display omitted] • A multi-purpose PMIA/PAN/SiO 2 membrane was prepared simply and efficiently. • Composite membranes present prospects in dust filtration and oily wastewater separation, even under complex environments. • The influence mechanism of SiO 2 was deeply studied. [ABSTRACT FROM AUTHOR]

The study of crowd evacuation dynamics is important for the management of public safety, and crowd evacuation dynamics models can be used to simulate the behaviors of pedestrians during an evacuation. In this paper, we propose a new force model for simulation of the evacuation of complex environments. In comparison to existing models, this model considers more environmental factors to make it applicable to complex emergency environments. By verification using real data, the model is demonstrated to be able to predict evacuation efficiency and better reproduce the microscopic behavior of pedestrians under extreme conditions. We discuss the impact of the presence of different kinds of hazards and different types of obstacles in the environment on pedestrian evacuation performance and put forward suggestions for crowd evacuation management. • A new model is proposed to simulate pedestrian movement in a complex environment. • The model includes rigid and flexible obstacles with different interaction rules. • A self-driving force helps reproduce detouring and other behavior. • The influence of hazards on expected speeds is analyzed. • The model is verified using field evacuation video data, and suggestions are made. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a novel approach for crowd simulation in complex environments. Our method is based on the continuum model proposed by Treuille et al. [13]. Compared to the original method, our solution is well-suited for complex environments. First, we present an environmental structure and a corresponding discretization scheme that helps us to organize and simulate crowds in large-scale scenarios. Second, additional discomfort zones around obstacles are auto-generated to keep a certain, psychologically plausible distance between pedestrians and obstacles, making it easier to obtain smoother trajectories when people move around these obstacles. Third, we propose a technique for density conversion; the density field is dynamically affected by each individual so that it can be adapted to different grid resolutions. The experiment results demonstrate that our hybrid solution can perform plausible crowd flow simulations in complex dynamic environments. [ABSTRACT FROM AUTHOR]

Low-cost and scalable fabrication processes are needed to prepare efficient superhydrophobic materials, particularly for oil/water separation and metal protection against severe oil spills and metal corrosion. Here, an effective oil/water separation mesh with long-lasting acid/base durability and corrosive resistance is reported. In this work, the mesh was fabricated by simply spraying nano soil particles cross-linked with melamine resin and impregnating them with polyethylene (PE) solution. The multi-layered morphology obtained from the spraying and impregnating process which provides the unique wetting properties for the stainless steel (SS) mesh, such as superhydrophobicity and low water adhesion. Also the low chemical activity of alkyl sp3 carbon-hydrogen bonds provides acid/base resistance for the manufactured material. By utilizing these unique properties, not only can gravity-driven efficient oil/corrosive fluid separation be achieved, but the fabricated superhydrophobic stainless steel (SSS) mesh retains high separation efficiency (> 99%) and superhydrophobicity (water contact angle, WCA > 150°) in reuse process of wastewater in treated oil. More importantly, this excellent protection can effectively reduce the corrosion rate of the mesh by more than two orders of magnitude (SS mesh i corr 1.82 × 10−8 A·cm−2; SSS mesh i corr 9.84 × 10−11 A·cm−2) which providing a long-lasting protection even after 16 days of immersion in 3.5 wt% NaCl solution. All in all, the easy operation, acid/base resistance, high separation efficiency, and corrosion inhibition efficiency of the SSS mesh give them great potential for oil/water separation and metal protection applications. [Display omitted] [ABSTRACT FROM AUTHOR]

• Images with different complexity backgrounds were collected. To measure the detection performance on various images, a method was proposed to quantify image background complexity. • We constructed an efficient detection model EFDet to detect cucumber disease in complex images. • EFDet has high robustness for complex environments and fewer parameters and computation. Improving the application capability of the disease detection model is a key issue in the field of agricultural informatization. The complex backgrounds, image diversity, and model complexity are the main factors that affect the realization of automatic disease recognition. This study constructs an efficient detection model (EFDet), which mainly consists of the efficient backbone network, a feature fusion module, and a predictor. EFDet improves the detection effect for cucumber leaves in complex backgrounds by fusing feature maps at different levels. We collected three category cucumber leaves including downy mildew, bacterial angular spot, and health to construct the cucumber disease dataset. It contains 7,488 images with three complexity levels for model training and evaluation. YOLO V3-V5, EfficientDet-D1, YOLO V3-ASFF, and other six detection models as the comparison models, we verify the EFDet performance in terms of model size , FLOPs , and mAP. Experimental results show that EFDet has strong robustness for cucumber disease leaf in complex environments. It also has smaller parameters and calculations that are suitable for actual applications. [ABSTRACT FROM AUTHOR]

The direction-of-arrival (DOA) tracking performance of microphone arrays having aperture sizes ranging from 0.3 to 34m is examined for an experiment involving a vehicle traversing a moderately complex terrain. A segment of the vehicle’s path was obscured behind a small, 6.7-m high, vegetated hill. The combination of the hill and upwind propagation created an acoustic shadow during this segment. DOA tracks were estimated with a minimum-variance distortionless response (MVDR) beamformer operating in two frequency bands: 25–60Hz and 60–105Hz. In the lower frequency band, array sizes between 1 and 8m gave the best results, with DOA errors between 2° and 5°. Furthermore, in this band shadowing from the hill and wind refraction had a minimal affect on DOA error. In the higher frequency band, the acoustic shadow zone produced a distinct interval of high DOA error, with the 8-m array giving the best overall performance. Modeling of the beamforming process shows that high DOA errors corresponded to MVDR wavenumber patterns that are degraded by distortions to the propagating wavefronts. Our experimental results indicate that small acoustic arrays with apertures less than 0.3m, operating at frequencies above 100Hz, should be considered line of sight sensors. Given the moderate complexity of the test conditions, it is anticipated that the observed effects are likely to be present in most attempts to localize outdoor sound sources. [Copyright &y& Elsevier]

Abstract: Traumatic brain injury (TBI) is most prevalent in children and young adults. The long-term effects of pediatric TBI include cognitive and behavioral impairments; however, over time, it is difficult to distinguish individual variability in intellect and behavior from sequelae of early injury. Postnatal day (PND) 19 rats underwent lateral fluid percussion (FP) injury, followed by rearing in either standard (STD) or enriched environment (EE) conditions. The hypothesis was that the traditional enhancement of cognitive functioning following EE rearing would be attenuated when this rearing is preceded by TBI at PND19. Thirty days after injury, Morris water maze (MWM) acquisition and subsequent probe trial retention were used to assess the behavioral effects of injury on experience-dependent plasticity induced by housing in EE at two different time windows. MWM acquisition demonstrated improvements following early EE rearing in both sham and injured animals; however, the degree of improvement was greater for uninjured animals. When EE rearing was delayed for 2 weeks after injury, the injury effect was absent and the effect of rearing even stronger. Memory testing in the early EE groups using a delayed probe trial showed an effect of injury and housing, with the sham EE animals benefiting the most. After the delayed EE, sham EE animals again showed more probe target hits, while FPEE animals did not, demonstrating an enduring memory deficit. These data confirm that early TBI has effects on experience-dependent plasticity resulting in long-term neurobehavioral deficits. In addition, the ability to benefit from environmental stimulation following TBI is dependent upon time after injury. [Copyright &y& Elsevier]

Abstract: Damage to the adult brain can result in adaptive plasticity in regions adjacent to the site of the principal insult and that the plastic changes may be modulated by post-injury rehabilitation training. In this study, we examined the effects of rehabilitation training on synaptic morphology in the dentate gyrus following transient global cerebral ischemia and the metabolic correlates of the ultrastructural changes. Forty adult male Wistar rats were included in the study and assigned to either ischemia or sham group. Following ischemic or sham surgery, rats were randomized to either complex environment housing (EC), exercise (EX), or social condition (SC, paired housing) group. Electron microscopy and unbiased stereological methods were used to evaluate synaptic plasticity and the number and size of mitochondria in synaptic axon terminals. Increased number of granule neurons was seen in all ischemic groups and in the sham EC rats. Changes in the number of synapses per neuron in the outer and inner molecular layers of the dentate gyrus parallel those seen in granule neurons. Similarly, ischemia and behavioral experience in EC independently increased the number of synaptic mitochondria in presynaptic terminals in both the outer and inner molecular layers; however, no significant changes were seen in mitochondrial size. These data suggest a link between behavioral training and synaptic plasticity in the region adjacent to the injury and that the likely metabolic correlate of this synaptic plasticity is increased number of mitochondria at synaptic axon terminals. [Copyright &y& Elsevier]

Studies have demonstrated the effects of complex environment (EC) housing on brain plasticity both during postnatal development and in adulthood, but it is not clear how long these plastic changes persist nor what happens when environmental exposure is discontinued. Here we examined layer IV in the visual cortex of adult male rats for the: (1) effects of EC housing on synaptic plasticity, and (2) persistence of the synaptic changes after withdrawal from the complex environment. Fifty-eight adult male Long Evans rats were assigned to either: EC, socially paired housing (SC), or individual housing (IC). These rats remained in their assigned environment for 30 days. After 30 days, all rats in SC and some animals from the EC and IC groups were removed and perfused. The remaining animals in EC were then assigned to either remain in EC (ECEC) or be subsequently housed in IC (ECIC) for another 30 days. Similarly, rats in the IC group either remained in IC (ICIC) or were subsequently housed in EC (ICEC) for another 30 days. Electron microscopy results showed that all rats exposed to EC had significantly more synapses/neuron compared to SC, IC, and ICIC animals. Longer exposure to EC (ECEC) did not result in statistically more synapses per neuron; however, decreased neuron volume was seen. EC-induced synaptic changes persisted for an additional 30 days after withdrawal from EC (ECIC) confirming that EC-induced plastic changes occur in the brain regardless of age and indicating that once changes occur they tend to persist. [Copyright &y& Elsevier]

Behavioral training has been shown to induce synaptic plasticity in both intact and injured animals. Because of the possibility that the adaptive changes after ischemic damage may make the brain more malleable to behavioral training, we examined the effects of complex environment (EC) housing and exercise (EX) after global cerebral ischemia on synaptic structural alterations. Forty-two adult male Wistar rats were included in the study and assigned to either ischemia or sham group. Following ischemic or sham surgery, rats were randomized to either EC, EX, or social condition (SC, paired housing) group. CA1 was processed for electron microscopy and unbiased stereological techniques were used to evaluate plasticity. Significantly decreased neuron density was seen in anterior and medial CA1 in ischemic animals regardless of behavioral training. Neuron density in anterior CA1 was 31% less than the medial area. Synaptogenesis was influenced by cerebral ischemia and behavioral training in that all ischemic groups and sham EC animals showed greater number of synapses per neuron compared to the sham EX and SC groups. Analysis of synapse configuration showed that the synaptogenesis in ischemia EX and SC rats was formed mainly by synapses with single synaptic boutons, whereas in the ischemia EC and sham EC rats synaptogenesis was formed mainly by synapses with multiple synaptic boutons. Furthermore, housing of sham and ischemia rats in EC resulted in increased number of synapses with perforated postsynaptic density. Together, these data suggest that behavioral experience in EC after insult may be able to enhance synaptic plasticity. [Copyright &y& Elsevier]

In the past several decades, extensive investigations have been carried out on the hydroelastic responses of Very Large Floating Structures (VLFS) in the deep open sea by researchers around the world. However, when tackling the hydroelastic problems of VLFS used as the protection and exploitation of marine resources near offshore and islands, the effects of complex environments, such as the seabed profile and inhomogeneous wave conditions should be taken into account. Based on the development history of VLFS and the process of classical hydroelasticity of VLFS, the research results of hydroelastic responses of VLFS in complex environments have been systematically summarized in this paper, so as to provide a basis for the further research. • Many factors have significant influences on the hydroelastic responses of VLFS deployed near seashore or an island. • The research results of hydroelastic responses of VLFS in complex environments have been systematically summarized. • The challenging key technical problems and the further development of the VLFS are also discussed. [ABSTRACT FROM AUTHOR]

This paper mainly studies the obstacle avoidance and rapid reconstruction of UAV formation to improve the adaptability of UAV formation to complex dynamic environment. Firstly, the obstacle and formation constraint model in complex environment are established, and a Bi-directional-Rapidly Exploring Random Tree (Bi-RRT*) algorithm based on greedy strategy is proposed to realize obstacle avoidance, sudden response and formation reconstruction for UAV formation; it improves the efficiency and formation stability of the current optimization algorithm in complex environments. Then, this paper ameliorates the extension of algorithm nodes to improve the efficiency of the solution, the optimization method of nodes growth greatly not only avoid the invalid extension, but also save storage space and improve the convergence speed of the algorithm. In addition, this paper adopts the adaptive step size rolling detection method to improve the sensitivity of the UAV formation to the sudden threat. The experimental results show that the complex environment constraint model proposed in this paper combined with the improved Bi-RRT* algorithm can effectively improve the adaptive ability of UAV formation to the dynamic environment, and it also can plan the ideal trajectory under sudden threats, which greatly improving the survivability of the UAV formation. [ABSTRACT FROM AUTHOR]

• In volatile operational environments coordination is less dependent on formal design. • Patterns of information flow in incident command system may be adapted. • Information-sharing and situational awareness requires adaptability in managers' roles. In recent years there has been an increase in commercial activities in the Arctic, including maritime traffic. This increase has led to economic development but may increase the risk of unwanted incidents. Concerns have been raised regarding the emergency response capacity of states to respond to maritime incidents effectively. Efforts from several agencies and also from neighboring countries may be needed. The coordination of such operations is facilitated by established incident command systems that define managerial roles, responsibilities, and information flows between individuals and organizations participating in rescue-work in large-scale crises. However, because of contextual challenges in the Arctic, the tactical and operational management-levels may have to adapt and improvise both their organizational structure and tasks to function efficiently. This paper has focused on how the different managerial roles influence information sharing between the participants in a complex rescue operation. The study is based on an in-depth case-study of a rescue operation in the waters around Svalbard, that is, the high Arctic. We demonstrate how information-sharing, coordination mechanisms, and managerial roles may need to be adapted during rescue operations to handle volatile operational conditions. The implications of our findings for the planning of mass rescue operations are reflected upon, in particular, the need for adaptive approaches to emergency response. [ABSTRACT FROM AUTHOR]

• The dataset is obtained under the complex background of multi factor interference. • We established a high accuracy classification model of cucumber similar diseases. • The improved EfficientNet-B4 based on Ranger has higher classification accuracy. The intelligent identification and classification of greenhouse plant diseases is an important research object in smart horticulture. In this study, our main task is to find an efficient method to solve the problem of disease similarity caused by two kinds of diseases occurring in the same leaf and the influence of external light. First, we obtain a cucumber leaf disease dataset in a naturally complex greenhouse background, which includes not only powdery mildew, downy mildew, healthy leaves, but also the combination of powdery mildew and downy mildew. Secondly, we use the current state-of-the-art method EfficientNet to construct a classification model for the above four types, Model accuracy is 97%, and prove that EfficientNet-B4 is the most suitable method for this study. Finally, we constructed a two-classification model of cucumber similar diseases by using EfficientNet-B4 improved with the most state-of-the-art optimizer Ranger, obtained unexpected accuracy (96%). The experimental results show that our improved method has significant effect on the classification of similar diseases of cucumber. [ABSTRACT FROM AUTHOR]