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This paper develops a digital watermarking algorithm using an informed watermark retrieval architecture. The developed method uses the fractional Fourier transform to embed the watermark in the space-frequency domain and extracts the watermark using blind source separation techniques. The watermark embedding is further enhanced using a heuristic algorithm to increase the strength of the watermarking system. We use genetic algorithm to find the optimal fractional domain by minimizing the coefficient of RMSE between the input image and the watermarked image. The algorithm's performance against various common attacks, e.g., JPEG compression and Gaussian noise, is presented to estimate the algorithm's robustness. [ABSTRACT FROM AUTHOR]

Background: We performed a systematic review that identified at least 9,000 scientific papers on PubMed that include immunofluorescent images of cells from the central nervous system (CNS). These CNS papers contain tens of thousands of immunofluorescent neural images supporting the findings of over 50,000 associated researchers. While many existing reviews discuss different aspects of immunofluorescentmicroscopy, such as image acquisition and staining protocols, few papers discuss immunofluorescent imaging from an image-processing perspective. We analyzed the literature to determine the image processing methods that were commonly published alongside the associated CNS cell, microscopy technique, and animal model, and highlight gaps in image processing documentation and reporting in the CNS research field. Methods: We completed a comprehensive search of PubMed publications using Medical Subject Headings (MeSH) terms and other general search terms for CNS cells and common fluorescent microscopy techniques. Publications were found on PubMed using a combination of column description terms and row description terms. We manually tagged the comma-separated values file (CSV) metadata of each publication with the following categories: animal or cell model, quantified features, threshold techniques, segmentation techniques, and image processing software. Results: Of the almost 9,000 immunofluorescent imaging papers identified in our search, only 856 explicitly include image processing information. Moreover, hundreds of the 856 papers are missing thresholding, segmentation, and morphological feature details necessary for explainable, unbiased, and reproducible results. In our assessment of the literature, we visualized current image processing practices, compiled the image processing options from the top twelve software programs, and designed a road map to enhance image processing. We determined that thresholding and segmentation methods were often left out of publications and underreported or underutilized for quantifying CNS cell research. Discussion: Less than 10% of papers with immunofluorescent images include image processing in their methods. A few authors are implementing advanced methods in image analysis to quantify over 40 different CNS cell features, which can provide quantitative insights in CNS cell features that will advance CNS research. However, our review puts forward that image analysis methods will remain limited in rigor and reproducibility without more rigorous and detailed reporting of image processing methods. Conclusion: Image processing is a critical part of CNS research that must be improved to increase scientific insight, explainability, reproducibility, and rigor. [ABSTRACT FROM AUTHOR]

Camouflage is the main means of anti-optical reconnaissance, and camouflage pattern design is an extremely important step in camouflage. Many scholars have proposed many methods for generating camouflage patterns. k-means algorithm can solve the problem of generating camouflage patterns quickly and accurately, but k-means algorithm is prone to inaccurate convergence results when dealing with large data images leading to poor camouflage effects of the generated camouflage patterns. In this paper, we improve the k-means clustering algorithm based on the maximum pooling theory and Laplace's algorithm, and design a new camouflage pattern generation method independently. First, applying the maximum pooling theory combined with discrete Laplace differential operator, the maximum pooling-Laplace algorithm is proposed to compress and enhance the target background to improve the accuracy and speed of camouflage pattern generation; combined with the k-means clustering principle, the background pixel primitives are processed to iteratively calculate the sample data to obtain the camouflage pattern mixed with the background. Using color similarity and shape similarity for evaluation, the results show that the combination of maximum pooling theory with Laplace algorithm and k-means algorithm can effectively solve the problem of inaccurate results of k-means algorithm in processing large data images. The new camouflage pattern generation method realizes the design of camouflage patterns for different backgrounds and achieves good results. In order to verify the practical application value of the design method, this paper produced test pieces based on the designed camouflage pattern generation method and tested the camouflage effect of camouflage pattern in sunny and cloudy days respectively, and the final test results were good. [ABSTRACT FROM AUTHOR]

Ca2+ imaging is the most frequently used technique to study glial cell physiology. While chemical Ca2+ indicators served to visualize and measure changes in glial cell cytosolic Ca2+ concentration for several decades, genetically encoded Ca2+ indicators (GECIs) have become state of the art in recent years. Great improvements have been made since the development of the first GECI and a large number of GECIs with different physical properties exist, rendering it difficult to select the optimal Ca2+ indicator. This review discusses some of the most frequently used GECIs and their suitability for glial cell research. [ABSTRACT FROM AUTHOR]

"Classical peer review" has been subject to intense criticism for slowing down the publication process, bias against specific categories of paper and author, unreliability, inability to detect errors and fraud, unethical practices, and the lack of recognition for unpaid reviewers. This paper surveys innovative forms of peer review that attempt to address these issues. Based on an initial literature review, we construct a sample of 82 channels of scientific communication covering all forms of review identified by the survey, and analyze the review mechanisms used by each channel. We identify two major trends: the rapidly expanding role of preprint servers (e.g., ArXiv) that dispense with traditional peer review altogether, and the growth of "non-selective review," focusing on papers' scientific quality rather than their perceived importance and novelty. Other potentially important developments include forms of "open review," which remove reviewer anonymity, and interactive review, as well as new mechanisms for post-publication review and out-of-channel reader commentary, especially critical commentary targeting high profile papers. One of the strongest findings of the survey is the persistence of major differences between the peer review processes used by different disciplines. None of these differences is likely to disappear in the foreseeable future. The most likely scenariofor the coming years is thus continued diversification, in which different review mechanisms serve different author, reader, and publisher needs. Relatively little is known about the impact of these innovations on the problems they address. These are important questions for future quantitative research. [ABSTRACT FROM AUTHOR]

Introduction: Facial expression recognition has always been a hot topic in computer vision and artificial intelligence. In recent years, deep learning models have achieved good results in accurately recognizing facial expressions. BILSTM network is such a model. However, the BILSTM network's performance depends largely on its hyperparameters, which is a challenge for optimization. Methods: In this paper, a Northern Goshawk optimization (NGO) algorithm is proposed to optimize the hyperparameters of BILSTM network for facial expression recognition. The proposed methods were evaluated and compared with other methods on the FER2013, FERplus and RAF-DB datasets, taking into account factors such as cultural background, race and gender. Results: The results show that the recognition accuracy of the model on FER2013 and FERPlus data sets is much higher than that of the traditional VGG16 network. The recognition accuracy is 89.72% on the RAF-DB dataset, which is 5.45, 9.63, 7.36, and 3.18% higher than that of the proposed facial expression recognition algorithms DLP-CNN, gACNN, pACNN, and LDL-ALSG in recent 2 years, respectively. Discussion: In conclusion, NGO algorithm effectively optimized the hyperparameters of BILSTM network, improved the performance of facial expression recognition, and provided a new method for the hyperparameter optimization of BILSTM network for facial expression recognition. [ABSTRACT FROM AUTHOR]

The intervention time of stability control system is determined by stability judgment, which is the basis of vehicle stability control. According to the different working conditions of the vehicle, we construct the phase plane of the vehicle's sideslip angle and sideslip angular velocity, and establish the sample dataset of the stable region of the different phase planes. To reduce the complexity of phase plane stable region division and avoid large amount of data, we established the support vector regression (SVR) model, and realized the automatic regression of dynamic stable region. The testing of the test set shows that themodel established in this paper has strong generalization ability. We designed a direct yaw-moment control (DYC) stability controller based on linear time-varying model predictive control (LTV-MPC). The influence of key factors such as centroid position and road adhesion coefficient on the stable region is analyzed through phase diagram. The effectiveness of the stability judgment and control algorithm is verified by simulation tests. [ABSTRACT FROM AUTHOR]

The use of computers to understand video content can accurately and quickly label various videos. Behavior recognition technology can help users filter the video by screening the content. However, this calculation mode, which is only sensitive to the features in a pixel neighborhood, cannot effectively extract cross-frame long-range video features. In addition, the common long-range dependency capture methods are based on pixel pairs, which contain less semantic information and cannot accurately model dependencies. Based on this, this paper generates semantic units with rich semantic information in the form of neighborhood pixel aggregation and proposes a multi-semantic long-range dependency capture algorithm to solve this problem, which makes the established dependency relationship more accurate. At the same time, this paper proposes an early dependency transfer technology to speed up the reasoning speed of the multi-semantic long- range dependency capture algorithm. By embedding the proposed algorithm into the original convolutional neural network, and conducting sufficient performance tests and evaluations on different data sets, it is shown that the proposed algorithm outperforms other current algorithms in terms of recognition accuracy and achieves the optimal recognition effect, which can effectively enhance the long-range dependency capture ability and temporal modeling ability of the convolutional network, and improve the quality of video feature representation. [ABSTRACT FROM AUTHOR]

The field of computer science has undergone rapid expansion due to the increasing interest in improving system performance. This has resulted in the emergence of advanced techniques, such as neural networks, intelligent systems, optimization algorithms, and optimization strategies. These innovations have created novel opportunities and challenges in various domains. This paper presents a thorough examination of three intelligent methods: neural networks, intelligent systems, and optimization algorithms and strategies. It discusses the fundamental principles and techniques employed in these fields, as well as the recent advancements and future prospects. Additionally, this paper analyzes the advantages and limitations of these intelligent approaches. Ultimately, it serves as a comprehensive summary and overview of these critical and rapidly evolving fields, offering an informative guide for novices and researchers interested in these areas. [ABSTRACT FROM AUTHOR]

In this paper, we propose a directionally selective fractional-order lobular giant motion detector (LGMD) visual neural network. Unlike most collision-sensing network models based on LGMDs, our model can not only sense collision threats but also obtain the motion direction of the collision object. Firstly, this paper simulates the membrane potential response of neurons using the fractional-order differential operator to generate reliable collision response spikes. Then, a new correlation mechanism is proposed to obtain the motion direction of objects. Specifically, this paper performs correlation operation on the signals extracted from two pixels, utilizing the temporal delay of the signals to obtain their position relationship. In this way, the response characteristics of direction-selective neurons can be characterized. Finally, ON/OFF visual channels are introduced to encode increases and decreases in brightness, respectively, thereby modeling the bipolar response of special neurons. Extensive experimental results show that the proposed visual neural system conforms to the response characteristics of biological LGMD and direction-selective neurons, and that the performance of the system is stable and reliable. [ABSTRACT FROM AUTHOR]

In the practical application scenarios of USVs, it is necessary to identify a vessel in order to accomplish tasks. Considering the sensors equipped on the USV, visible images provide the fastest and most effcient way of determining the hull number. The current studies divide the task of recognizing vessel plate number into two independent subtasks: text localization in the image and its recognition. Then, researchers are focusing on improving the accuracy of localization and recognition separately. However, these methods cannot be directly applied to USVs due to the difference between these two application scenarios. In addition, as the two independent models are serial, there will be inevitable propagation of error between them, as well as an increase in time costs, resulting in a less satisfactory performance. In view of the above, we proposed a method based on object detection model for recognizing vessel plate number in complicated sea environments applied to USVs. The accuracy and stability of model have been promoted by recursive gated convolution structure, decoupled head, reconstructing loss function, and redesigning the sizes of anchor boxes. To facilitate this research, a vessel plate number dataset is established in this paper. Furthermore, we conducted a experiment utilizing a USV platform in the South China Sea. Compared with the original YOLOv5, themAP (mean Average Precision) value of proposed method is increased by 6.23%. The method is employed on the "Tian Xing" USV platform and the experiment results indicates both the ship and vessel plate number can be recognized in real-time. In both the civilian andmilitary sectors, this has a great deal of significance. [ABSTRACT FROM AUTHOR]

Spinal cord injury (SCI) is a serious neurological injury that causes severe trauma to motor and sensory functions. Although long considered incurable, recent research has brought new hope for functional recovery from SCI. After SCI, astrocytes are activated into many polarization states. Here we discuss the two most important classical phenotypes: the 'A1' neurotoxic phenotype and the 'A2' neuroprotective phenotype, with A1 astrocytes being neurotoxic and impeding neurorecovery, and A2 astrocytes being neuroprotective. This paper discusses the changes in astrocyte responsiveness after SCI and the pros and cons of their polarization in SCI. It also elucidates the feasibility of astrocyte polarization as a therapeutic target for neuroprotection. In the future, multiple intervention strategies targeting astrocyte polarization are expected to gain wider clinical application, ultimately improving motor-sensory function and quality of life in SCI patients. [ABSTRACT FROM AUTHOR]

The increasing prevalence of neurodevelopmental disorders has highlighted the need for improved testing methods to determine developmental neurotoxicity (DNT) hazard for thousands of chemicals. This paper proposes the integration of organoid intelligence (OI); leveraging brain organoids to study neuroplasticity in vitro, into the DNT testing paradigm. OI brings a new approach to measure the impacts of xenobiotics on plasticity mechanisms - a critical biological process that is not adequately covered in current DNT in vitro assays. Finally, the integration of artificial intelligence (AI) techniques will further facilitate the analysis of complex brain organoid data to study these plasticity mechanisms. [ABSTRACT FROM AUTHOR]

Cerebral aneurysm (CA) is a significant health concern that results from pathological dilations of blood vessels in the brain and can lead to severe and potentially life-threatening conditions. While the pathogenesis of CA is complex, emerging studies suggest that endothelial progenitor cells (EPCs) play a crucial role. In this paper, we conducted a comprehensive literature review to investigate the potential role of EPCs in the pathogenesis and treatment of CA. Current research indicates that a decreased count and dysfunction of EPCs disrupt the balance between endothelial dysfunction and repair, thus increasing the risk of CA formation. Reversing these EPCs abnormalities may reduce the progression of vascular degeneration after aneurysm induction, indicating EPCs as a promising target for developing new therapeutic strategies to facilitate CA repair. This has motivated researchers to develop novel treatment options, including drug applications, endovascular-combined and tissue engineering therapies. Although preclinical studies have shown promising results, there is still a considerable way to go before clinical translation and eventual benefits for patients. Nonetheless, these findings offer hope for improving the treatment and management of this condition. [ABSTRACT FROM AUTHOR]

This paper proposes an iterative transfer learning approach to achieve swarming collective motion in groups of mobile robots. By applying transfer learning, a deep learner capable of recognizing swarming collective motion can use its knowledge to tune stable collective motion behaviors across multiple robot platforms. The transfer learner requires only a small set of initial training data from each robot platform, and this data can be collected from random movements. The transfer learner then progressively updates its own knowledge base with an iterative approach. This transfer learning eliminates the cost of extensive training data collection and the risk of trial-and-error learning on robot hardware. We test this approach on two robot platforms: simulated Pioneer 3DX robots and real Sphero BOLT robots. The transfer learning approach enables both platforms to automatically tune stable collective behaviors. Using the knowledge-base library the tuning procedure is fast and accurate. We demonstrate that these tuned behaviors can be used for typical multi-robot tasks such as coverage, even though they are not specifically designed for coverage tasks. [ABSTRACT FROM AUTHOR]

Studying the task assignment problem of multiple underwater robots has a broad effect on the field of underwater exploration and can be helpful in military, fishery, and energy. However, to the best of our knowledge, few studies have focused on multi-constrained underwater detection task assignment for heterogeneous autonomous underwater vehicle (AUV) clusters with autonomous decision-making capabilities, and the current popular heuristic methods have difficulty obtaining optimal cluster unit task assignment results. In this paper, a fast graph pointer network (FGPN) method, which is a hybrid of graph pointer network (GPN) and genetic algorithm, is proposed to solve the task assignment problem of detection/communication AUV clusters, and to improve the assignment efficiency on the basis of ensuring the accuracy of task assignment. A two-stage detection algorithm is used. First, the task nodes are clustered and pre-grouped according to the communication distance. Then, according to the clustering results, a neural network model based on graph pointer network is used to solve the local task assignment results. A large-scale cluster cooperative task assignment problem and a detection/communication cooperative work mode are proposed, which transform the cooperative cooperation problem of heterogeneous AUV clusters into a Multiple Traveling salesman problem (MTSP) for solving. We also conducted a large number of experiments to verify the effectiveness of the algorithm. The experimental results show that the solution efficiency of the method proposed in this paper is better than the traditional heuristic method on the scale of 300/500/750/1,000/1,500/2,000 task nodes, and the solution quality is similar to the result of the heuristic method. We hope that our ideas and methods for solving the large-scale cooperative task assignment problem can be used as a reference for large-scale task assignment problems and other related problems in other fields. [ABSTRACT FROM AUTHOR]

For upper limb amputees, wearing a myoelectric prosthetic hand is the only way for them to continue normal life. Even until now, the proposal of a high-precision and natural performance real-time control system based on surface electromyography (sEMG) signals is still challenging. Researchers have proposed many strategies for motion classification or regression prediction tasks based on sEMG signals. However, most of them have been limited to ofline analysis only. There are even few papers on real-time control based on deep learning models, almost all of which are about motion classification. Rare studies tried to use deep learning-based regression models in real-time control systems for multi-joint angle estimation via sEMG signals. This paper proposed a CW-CNN regression model-based real-time control system for virtual hand control. We designed an Adaptive Kalman Filter to smooth the joint angles output before sending them as control commands to control a virtual hand. Eight healthy participants were invited, and three sessions experiments were conducted on two different days for all of them. During the real-time experiment, we analyzed the joint angles estimation accuracy and computational latency. Moreover, target achievement control (TAC) test was applied to emphasize motion regression in real-time. The experimental results show that the proposed control system has high precision for 3-DOFs motion regression in simultaneously, and the system remains stable and low computational latency. In the future, the proposed real-time control system can be applied to actual prosthetic hand. [ABSTRACT FROM AUTHOR]

Brain degenerations in sporadic Alzheimer's disease (AD) are observed earliest in the locus coeruleus (LC), a population of noradrenergic neurons, in which hyperphosphorylated tau protein expression and β-amyloid (Aβ) accumulation begin. Along with this, similar changes occur in the basal forebrain cholinergic neurons, such as the nucleus basalis of Meynert. Neuronal degeneration of the two neuronal nuclei leads to a decrease in neurotrophic factors such as brain-derived neurotrophic factor (BDNF) in the hippocampus and cerebral cortex, which results in the accumulation of Aβ and hyperphosphorylated tau protein and ultimately causes neuronal cell death in those cortices. On the other hand, a large number of epidemiological studies have shown that tooth loss or masticatory dysfunction is a risk factor for dementia including AD, and numerous studies using experimental animals have also shown that masticatory dysfunction causes brain degeneration in the basal forebrain, hippocampus, and cerebral cortex similar to those observed in human AD, and that learning and memory functions are impaired accordingly. However, it remains unclear how masticatory dysfunction can induce such brain degeneration similar to AD, and the neural mechanism linking the trigeminal nervous system responsible for mastication and the cognitive and memory brain system remains unknown. In this review paper, we provide clues to the search for such "missing link" by discussing the embryological, anatomical, and physiological relationship between LC and its laterally adjoining mesencephalic trigeminal nucleus which plays a central role in the masticatory functions. [ABSTRACT FROM AUTHOR]

Engineered 3D neural tissues made of neurons and glial cells derived from human induced pluripotent stem cells (hiPSC) are among the most promising tools in drug discovery and neurotoxicology. They represent a cheaper, faster, and more ethical alternative to in vivo animal testing that will likely close the gap between in vitro animal models and human clinical trials. Micro-Electrode Array (MEA) technology is known to provide an assessment of compound effects on neural 2D cell cultures and acute tissue preparations by real-time, noninvasive, and long-lasting electrophysiological monitoring of spontaneous and evoked neuronal activity. Nevertheless, the use of engineered 3D neural tissues in combination with MEA biochips still involves series of constraints, such as drastically limited diffusion of oxygen and nutrients within tissues mainly due to the lack of vascularization. Therefore, 3D neural tissues are extremely sensitive to experimental conditions and require an adequately designed interface that provides optimal tissue survival conditions. A well-suited technique to overcome this issue is the combination of the Air-Liquid Interface (ALI) tissue culture method with the MEA technology. We have developed a full 3D neural tissue culture process and a data acquisition system composed of high-end electronics and novel MEA biochips based on porous, flexible, thin-film membranes integrating recording electrodes, named as "Strip-MEA," to allow the maintenance of an ALI around the 3D neural tissues. The main motivation of the porous MEA biochips development was the possibility to monitor and to study the electrical activity of 3D neural tissues under different recording configurations, (i) the Strip-MEA can be placed below a tissue, (ii) or by taking advantage of the ALI, be directly placed on top of the tissue, or finally, (iii) it can be embedded into a larger neural tissue generated by the fusion of two (or more) tissues placed on both sides of the Strip-MEA allowing the recording from its inner part. This paper presents the recording and analyses of spontaneous activity from the three positioning configurations of the Strip-MEAs. Obtained results are discussed with the perspective of developing in vitro models of brain diseases and/or impairment of neural network functioning. [ABSTRACT FROM AUTHOR]

Background: Noise-induced hearing loss (NIHL) has always been a global public health problem, which is related to noise exposure and genetic factors. Many researchers have tried to identify the polymorphisms that cause different individuals' susceptibility to NIHL. We conducted a meta-analysis of the most frequently studied polymorphisms to identify those genes that may be associated with NIHL and may provide value in risk prevention. Methods: PubMed, China National Knowledge Infrastructure (CNKI) database, Embase, Wang Fang, Web of Science and Cochrane library were searched, and qualified studies on the correlation between polymorphismandNIHL susceptibility were screened, and then polymorphisms cited in at least three studies were selected for meta-analysis. Fixed- or random-effects models were used to calculate odds ratios and 95%confidence intervals. Statistical I2 tests and sensitivity analyses were used to detect interstudy heterogeneity and test the statistical stability of overall estimates, respectively. Egger's tests were applied to detect publication bias among included studies. All of the above analyses were performed using stata 17.0. Results: 64 genes were initially selected and introduced in 74 papers. Among them, 10 genes (and 25 polymorphisms) have been reported in more than 3 papers. Twenty five polymorphisms participated in the meta-analysis. Of the 25 polymorphisms, only 5 were significantly associated with the risk of AR: rs611419 (GRHL2) polymorphism and rs3735715 polymorphism (GRHL2), rs208679 polymorphism(CAT), rs3813346 polymorphism(EYA4) were significantly associated with the susceptibility of NIHL, rs2227956 polymorphism (HSP70) was significantly associated with the susceptibility of white population NIHL, and the remaining 20 gene polymorphisms were not significantly associated with NIHL. Conclusion: We found polymorphisms that are valuable for the prevention of NIHL and polymorphisms that are not related to NIHL. This is the first step to establish an effective risk prediction systemfor the population, especially for high-risk groups, whichmay help us better identify and prevent the occurrence of NIHL. In addition, our research results contribute to the in-depth exploration of NIHL. [ABSTRACT FROM AUTHOR]

Background: Traumatic brain injury (TBI) is a critical global health concern characterized by elevated rates of both morbidity and mortality. The pathological and physiological changes after TBI are closely related to microglia. Microglia, the primary immune cells in the brain, are closely linked to the mechanisms and treatment of TBI. With increasing research in this area, this study employs bibliometric analysis to identify current research hotspots and predict future trends. Objective: We decided to perform a bibliometric analysis to provide a comprehensive overview of the advancements in microglia research related to traumatic brain injury. We aim to offer researchers insights into current trends and future research directions. Method: We collected all articles and reviews related to microglia and traumatic brain injury published between 2000 and 2023 from the Web of Science Core Collection. These records were analyzed using VOSviewer, CiteSpace, and the R package "bibliometrix". Results: We retrieved 665 publications from 25 countries, with the majority contributed by the United States and China. The number of publications on traumatic brain injury and microglia has been steadily increasing each year. Our analysis highlighted the Journal of Neurotrauma and the Journal of Neuroinflammation as the most influential journals in this field. Alan I. Faden and David J. Loane are recognized as leading contributors. Keyword analysis indicates that neuroinflammation, microglial polarization, and neurodegenerative diseases are pivotal areas for future research. Conclusion: In recent years, research on TBI-related microglia has proliferated, with current studies primarily focusing on microglial involvement in neuroinflammation, neurodegenerative changes, and microglial polarization following TBI. Since neuroinflammation and neurodegeneration are two hallmark features of TBI, targeting microglia in TBI treatment may become a central focus for future research. [ABSTRACT FROM AUTHOR]

Music therapy is a common method to relieve anxiety and pain in cancer patients after surgery in recent years, but due to the lack of technical and algorithmic support, this therapy is not particularly stable and the therapeutic effect is not good. In this study, a neural network robotic system based on breast cancer patients was designed to analyze the effect of music relaxation training on alleviating adverse reactions after chemotherapy in breast cancer patients. Firstly, this paper introduces the necessity of neural network robot system research under the background of music therapy, and then summarizes the positive effect of music relaxation therapy on alleviating adverse reactions after chemotherapy in breast cancer patients, finally, uses neural network robot system to construct music therapy system. The experimental results show that the new music therapy proposed in this study has a good effect in alleviating the adverse reactions of breast cancer patients after chemotherapy, and the cure rate is increased by 7.84%. The research results of this paper provide reference for the next development of neural network robot system in the medical field. [ABSTRACT FROM AUTHOR]

In recent years, visual tracking algorithms based on Siamese networks have attracted attention for their desirable balance between speed and accuracy. The performance of such tracking methods relies heavily on target templates. Static templates cannot cope with the adverse effects of target appearance change. The dynamic template method, with a template update mechanism, can adapt to the change in target appearance well, but it also causes new problems, which may lead the template to be polluted by noise. Based on the DaSiamRPN and UpdateNet template update networks, a Siamese tracker with "dynamic-static" dual-template fusion and dynamic template adaptive update is proposed in this paper. The new method combines a static template and a dynamic template that is updated in real time for object tracking. An adaptive update strategy was adopted when updating the dynamic template, which can not only help adjust to the changes in the object appearance, but also suppress the adverse effects of noise interference and contamination of the template. The experimental results showed that the robustness and EAO of the proposed method were 23% and 9.0% higher than those of the basic algorithm on the VOT2016 dataset, respectively, and that the precision and success were increased by 0.8 and 0.4% on the OTB100 dataset, respectively. Themost comprehensive real-time tracking performance was obtained for the above two large public datasets. [ABSTRACT FROM AUTHOR]

In general, the traditional spacecraft semi-physical docking tests include the evaluation of docking and separation performance. However, these tests often rely on "specific" equipment, such as specially designed actuators and fast-response hydraulic systems, to meet the stringent dynamic response requirements of semi-physical testing. In this paper, a novel docking test platform is designed based on a general-purpose industrial manipulator using 3-D force and 3-D torque sensors. Different from the traditional solution, this novel platform is well-assembled and cost-effective. Furthermore, an actuation delay compensation method is introduced to improve the performance. Finally, the proposed method is evaluated using simulations. The results show that the novel method is with promising performance in terms of actuation delay compensation. [ABSTRACT FROM AUTHOR]

Wheel-legged robots have fast and stable motion characteristics on flat roads, but there are the problems of poor balance ability and low movement level in special terrains such as rough roads. In this paper, a new type of wheel-legged robot with parallel four-bar mechanism is proposed, and the linear quadratic regulator (LQR) controller and fuzzy proportion differentiation (PD) jumping controller are designed and developed to achieve stable motion so that the robot has the ability to jump over obstacles and adapt to rough terrain. The amount of energy released by the parallel four-bar linkage mechanism changes with the change of the link angle, and the height of the jump trajectory changes accordingly, which improves the robot's ability to overcome obstacles facing vertical obstacles. Simulations and real scene tests are performed in different terrain environments to verify obstacle crossing capabilities. The simulation results show that, in the pothole terrain, the maximum height error of the two hip joint motors is 2 mm for the obstacle surmounting method of the adaptive retractable wheel-legs; in the process of single leg obstacle surmounting, the maximum height error of the hip joint motors is only 6.6 mm. The comparison of simulation data and real scene experimental results shows that the robot has better robustness in moving under complex terrains. [ABSTRACT FROM AUTHOR]

With the rapid development of artificial intelligence technology, many researchers have begun to focus on visual language navigation, which is one of the most important tasks in multi-modal machine learning. The focus of this multi-modal field is how to fuse multiple inputs, which is crucial for the integrated feedback of intrinsic information. However, the existing models are only implemented through simple data augmentation or expansion, and are obviously far from being able to tap the intrinsic relationship between modalities. In this paper, to overcome these challenges, a novel multi-modal matching feedback self-tuning model is proposed, which is a novel neural network called Vital Information Matching Feedback Self-tuning Network (VIM-Net). Our VIM-Net network is mainly composed of two matching feedback modules, a visual matching feedback module (V-mat) and a trajectory matching feedback module (T-mat). Specifically, V-mat matches the target information of visual recognition with the entity information extracted by the command; T-mat matches the serialized trajectory feature with the direction of movement of the command. Ablation experiments and comparative experiments are conducted on the proposed model using the Matterport3D simulator and the Room-to-Room (R2R) benchmark datasets, and the final navigation effect is shown in detail. The results prove that the model proposed in this paper is indeed effective on the task. [ABSTRACT FROM AUTHOR]

The emergence of multimodal medical imaging technology greatly increases the accuracy of clinical diagnosis and etiological analysis. Nevertheless, each medical imaging modal unavoidably has its own limitations, so the fusion of multimodal medical images may become an effective solution. In this paper, a novel fusion method on the multimodal medical images exploiting convolutional neural network (CNN) and extreme learning machine (ELM) is proposed. As a typical representative in deep learning, CNN has been gaining more and more popularity in the field of image processing. However, CNN often suffers from several drawbacks, such as high computational costs and intensive human interventions. To this end, the model of convolutional extreme learning machine (CELM) is constructed by incorporating ELM into the traditional CNN model. CELM serves as an important tool to extract and capture the features of the source images from a variety of different angles. The final fused image can be obtained by integrating the significant features together. Experimental results indicate that, the proposed method is not only helpful to enhance the accuracy of the lesion detection and localization, but also superior to the current state-of-the-art ones in terms of both subjective visual performance and objective criteria. [ABSTRACT FROM AUTHOR]

Non-clinical toxicology is a major cause of drug candidate attrition during development. In particular, drug-induced seizures are the most common finding in central nervous system (CNS) toxicity. Current safety pharmacology tests for assessing CNS functions are often inadequate in detecting seizure-inducing compounds early in drug development, leading to significant delays. This paper presents an in vitro seizure liability assay using voltage-sensitive dye (VSD) imaging techniques in hippocampal brain slices, offering a powerful alternative to traditional electrophysiological methods. Hippocampal slices were isolated from mice, and VSD optical responses evoked by stimulating the Schaffer collateral pathway were recorded and analyzed in the stratum radiatum (SR) and stratum pyramidale (SP). VSDs allow for the comprehensive visualization of neuronal action potentials and postsynaptic potentials on a millisecond timescale. By employing this approach, we investigated the in vitro druginduced seizure liability of representative pro-convulsant compounds. Picrotoxin (PiTX; 1-100 mM), gabazine (GZ; 0.1-10 mM), and 4-aminopyridine (4AP; 10-100 mM) exhibited seizure-like responses in the hippocampus, but pilocarpine hydrochloride (Pilo; 10-100 mM) did not. Our findings demonstrate the potential of VSD-based assays in identifying seizurogenic compounds during early drug discovery, thereby reducing delays in drug development and providing insights into the mechanisms underlying seizure induction and the associated risks of pro-convulsant compounds. [ABSTRACT FROM AUTHOR]

The long history of studying the propagation of an action potential has revealed that an electrical signal is accompanied by mechanical and thermal effects. All these effects together generate an ensemble of waves. The consistent models of such a complex phenomenon can be derived by using properly the fundamental physical principles. In this paper, attention is paid to the analysis of concepts of continuum physics that constitute a basis for deriving the mathematical models which describe the emergence and propagation of a wave ensemble in an axon. Such studies are interdisciplinary and based on biology, physics, mathematics, and chemistry. The governing equations for the action potential together with mechanical and thermal effects are derived starting from basics: Maxwell equations, conservation of momentum, Fourier’s law, etc., but modified following experimental studies in electrophysiology. Several ideas from continuum physics like external forces and internal variables can also be used in deriving the corresponding models. Some mathematical concepts used in modeling are also briefly described. A brief overview of several mathematical models is presented that allows us to analyze the present ideas of modeling. Most mathematical models deal with the propagation of signals in a healthy axon. Further analysis is needed for better modeling the pathological situations and the explanation of the influence of the structural details like the myelin sheath or the cytoskeleton in the axoplasm. The future possible trends in improving the models are envisaged. [ABSTRACT FROM AUTHOR]

Introduction: Dopamine release in the forebrain by midbrain ventral tegmental nucleus (VTA) and substantia nigra pars compacta (SNc) neurons is implicated in reward processing, goal-directed learning, and decision-making. Rhythmic oscillations of neural excitability underlie coordination of network processing, and have been reported in these dopaminergic nuclei at several frequency bands. This paper provides a comparative characterization of several frequencies of oscillations of local field potential and single unit activity, highlighting some behavioral correlates. Methods: We recorded from optogenetically identified dopaminergic sites in four mice training in operant olfactory and visual discrimination tasks. Results: Rayleigh and Pairwise Phase Consistency (PPC) analyses revealed some VTA/SNc neurons phase-locked to each frequency range, with fast spiking interneurons (FSIs) prevalent at 1-2.5 Hz (slow) and 4 Hz bands, and dopaminergic neurons predominant in the theta band. More FSIs than dopaminergic neurons were phase-locked in the slow and 4 Hz bands during many task events. The highest incidence of phase-locking in neurons was in the slow and 4 Hz bands, and occurred during the delay between the operant choice and trial outcome (reward or punishment) signals. Discussion: These data provide a basis for further examination of rhythmic coordination of activity of dopaminergic nuclei with other brain structures, and its impact for adaptive behavior. [ABSTRACT FROM AUTHOR]

Introduction: Visual evoked potentials (VEPs) are a non-invasive technique routinely used in clinical and preclinical practice. Discussion about inclusion of VEPs in McDonald criteria, used for Multiple Sclerosis (MS) diagnosis, increased the importance of VEP in MS preclinical models. While the interpretation of the N1 peak is recognized, less is known about the first and second positive VEP peaks, P1 and P2, and the implicit time of the different segments. Our hypothesis is that P2 latency delay describes intracortical neurophysiological dysfunction from the visual cortex to the other cortical areas. Methods: In this work, we analyzed VEP traces that were included in our two recently published papers on Experimental Autoimmune Encephalomyelitis (EAE) mouse model. Compared with these previous publications other VEP peaks, P1 and P2, and the implicit time of components P1-N1, N1-P2 and P1-P2, were analyzed in blind. Results: Latencies of P2, P1-P2, P1-N1 and N1-P2 were increased in all EAE mice, including group without N1 latency change delay at early time points. In particular, at 7 dpi the P2 latency delay change was significantly higher compared with N1 latency change delay. Moreover, new analysis of these VEP components under the influence of neurostimulation revealed a decrease in P2 delay in stimulated animals. Discussion: P2 latency delay, P1-P2, P1-N1, and N1-P2 latency changes which reflect intracortical dysfunction, were consistently detected across all EAE groups before N1 change. Results underline the importance of analyzing all VEP components for a complete overview of the neurophysiological visual pathway dysfunction and treatment efficacy. [ABSTRACT FROM AUTHOR]

Background: Advances in the understanding of the genetic and molecular etiologies of inner ear disorders have enabled the identification of therapeutic targets and innovative delivery approaches to the inner ear. As this field grows, the need for knowledge about effective delivery of therapeutics to the inner ear has become a priority. This review maps all clinical and pre-clinical research published in English in the field to date, to guide both researchers and clinicians about local drug delivery methods in the context of novel therapeutics. Methods: A systematic search was conducted using customized strategies in Cochrane, pubmed and EMBASE databases from inception to 30/09/2018. Two researchers undertook study selection and data extraction independently. Results: Our search returned 12,200 articles, of which 837 articles met the inclusion criteria. 679 were original research and 158 were reviews. There has been a steady increase in the numbers of publications related to inner ear therapeutics delivery over the last three decades, with a sharp rise over the last 2 years. The intra-tympanic route accounts for over 70% of published articles. Less than one third of published research directly assesses delivery efficacy, with most papers using clinical efficacy as a surrogate marker. Conclusion: Research into local therapeutic delivery to the inner ear has undergone a recent surge, improving our understanding of how novel therapeutics can be delivered. Direct assessment of delivery efficacy is challenging, especially in humans, and progress in this area is key to understanding how to make decisions about delivery of novel hearing therapeutics. [ABSTRACT FROM AUTHOR]

It is now widely accepted that glia cells and gamma-aminobutyric acidergic (GABA) interneurons dynamically regulate synaptic transmission and neuronal activity in time and space. This paper presents a biophysical model that captures the interaction between an astrocyte cell, a GABA interneuron and pre/postsynaptic neurons. Specifically, GABA released from a GABA interneuron triggers in astrocytes the release of calcium (Ca 2+) from the endoplasmic reticulum via the inositol 1, 4, 5-trisphosphate (IP 3) pathway. This results in gliotransmission which elevates the presynaptic transmission probability rate (PR) causing weight potentiation and a gradual increase in postsynaptic neuronal firing, that eventually stabilizes. However, by capturing the complex interactions between IP 3, generated from both GABA and the 2-arachidonyl glycerol (2-AG) pathway, and PR, this paper shows that this interaction not only gives rise to an initial weight potentiation phase but also this phase is followed by postsynaptic bursting behavior. Moreover, the model will show that there is a presynaptic frequency range over which burst firing can occur. The proposed model offers a novel cellular level mechanism that may underpin both seizure-like activity and neuronal synchrony across different brain regions. [ABSTRACT FROM AUTHOR]

This article explores the use of convolutional neural networks (CNN) in studying retinal neural networks. It discusses the application of CNN as a framework for analyzing retinal circuits and examines the relationship between structural parameters and information-processing functions. The article focuses on the primary rod signal pathway in mouse and macaque retinas, particularly electrical synapses. By comparing the differences in retinal circuitry between these species, the author suggests that insights into neurocircuitry principles and adaptational designs can be gained. The article also discusses the methodology for observing neural circuitry using electron microscopy and emphasizes the importance of human pattern recognition in interpreting the images. It concludes by stating that connectomic studies on visual processing can enhance our understanding of natural intelligence. The document includes ethical guidelines, financial support, acknowledgments, a conflict of interest statement, and references to related research papers. [Extracted from the article]

Car-following modeling is essential in the longitudinal control for connected and autonomous vehicles (CAVs). Considering the advantage of the generative adversarial network (GAN) in capturing realistic data distribution, this paper applies conditional GAN (CGAN) to car-following modeling. The generator is elaborately designed with a sequence-to-sequence structure to reflect the decision-making process of human driving behavior. The proposed model is trained and tested based on the empirical dataset, and it is compared with a supervised learning model and a mathematical model. Numerical simulations are conducted to verify the model’s performance, especially in the condition of mixed traffic flow. The comparison result shows that the CGAN model outperforms others in trajectory reproduction, indicating it can effectively imitate human driving behavior. The simulation results suggest that the introduction of CGAN-based CAVs improves the stability and efficiency of the mixed traffic flow. [ABSTRACT FROM AUTHOR]

As a crucial component of the autonomous driving task, the vehicle target detection algorithm directly impacts driving safety, particularly in inclement weather situations, where the detection precision and speed are significantly decreased. This paper investigated the You Only Look Once (YOLO) algorithm and proposed an enhanced YOLOv4 for real-time target detection in inclement weather conditions. The algorithm uses the Anchor-free approach to tackle the problem of YOLO preset anchor frame and poor fit. It better adapts to the detected target size, making it suitable for multi-scale target identification. The improved FPN network transmits feature maps to unanchored frames to expand the model's sensory field and maximize the utilization of model feature data. Decoupled head detecting head to increase the precision of target category and location prediction. The experimental dataset BDD-IW was created by extracting specific labeled photos from the BDD100K dataset and fogging some of them to test the proposed method's practical implications in terms of detection precision and speed in Inclement weather conditions. The proposed method is compared to advanced target detection algorithms in this dataset. Experimental results indicated that the proposed method achieved a mean average precision of 60.3%, which is 5.8 percentage points higher than the original YOLOv4; the inference speed of the algorithm is enhanced by 4.5 fps compared to the original, reaching a real-time detection speed of 69.44 fps. The robustness test results indicated that the proposed model has considerably improved the capacity to recognize targets in inclement weather conditions and has achieved high precision in real-time detection. [ABSTRACT FROM AUTHOR]

Introduction: With the development of artificial intelligence and brain science, brain-inspired navigation and path planning has attracted widespread attention. Methods: In this paper, we present a place cell based path planning algorithm that utilizes spiking neural network (SNN) to create efficient routes for drones. First, place cells are characterized by the leaky integrate-and-fire (LIF) neuron model. Then, the connection weights between neurons are trained by spike-timing-dependent plasticity (STDP) learning rules. Afterwards, a synaptic vector field is created to avoid obstacles and to find the shortest path. Results: Finally, simulation experiments both in a Python simulation environment and in an Unreal Engine environment are conducted to evaluate the validity of the algorithms. Discussion: Experiment results demonstrate the validity, its robustness and the computational speed of the proposed model. [ABSTRACT FROM AUTHOR]

Safety and ergonomics of Physical Human-Robot Collaboration (PHRC) are crucial to make human-robot collaborative systems trustworthy and make a significant impact in real-world applications. One big obstacle to the development of relevant research is the lack of a general platform for evaluating the safety and ergonomics of proposed PHRC systems. This paper aims to create a Physical emulatoR enabling safEty anD ergonomICs evaluation and Training of physical human-rObot collaboRation (PREDICTOR). PREDICTOR consists of a dual-arm robot system and a VR headset as its hardware and contains physical simulation, haptic rendering and visual rendering modules as its software. The dual-arm robot system is used as an integrated admittance-type haptic device, which senses the force/torque applied by a human operator as an input to drive the simulation of a PHRC system and constrains the handles' motion to match their virtual counterparts in the simulation. The motion of the PHRC systemin the simulation is fed back to the operator through the VR headset. PREDICTOR combines haptics and VR to emulate PHRC tasks in a safe environment since the interactive forces are monitored to avoid any risky events. PREDICTOR also brings flexibility as different PHRC tasks can be easily set up by changing the PHRC system model and the robot controller in the simulation. The effectiveness and performance of PREDICTOR were evaluated by experiments. [ABSTRACT FROM AUTHOR]

Background: Blast induced Traumatic Brain Injury (bTBI) has become a signature casualty of military operations. Recently, military medics observed neurocognitive deficits in servicemen exposed to repeated low level blast (LLB) waves during military heavy weapons training. In spite of significant clinical and preclinical TBI research, current understanding of injury mechanisms and short- and long-term outcomes is limited. Mathematical models of bTBI biomechanics and mechanobiology of sensitive neuro-structures such as synapses may help in better understanding of injury mechanisms and in the development of improved diagnostics and neuroprotective strategies. Methods and results: In this work, we formulated a model of a single synaptic structure integrating the dynamics of the synaptic cell adhesion molecules (CAMs) with the deformation mechanics of the synaptic cleft. The model can resolve time scales ranging from milliseconds during the hyperacute phase of mechanical loading to minutes-hours acute/chronic phase of injury progression/repair. The model was used to simulate the synaptic injury responses caused by repeated blast loads. Conclusion: Our simulations demonstrated the importance of the number of exposures compared to the duration of recovery period between repeated loads on the synaptic injury responses. The paper recognizes current limitations of the model and identifies potential improvements. [ABSTRACT FROM AUTHOR]

Keywords: neuroplasticity; axonal plasticity; dendritic plasticity; synaptic plasticity; functional neurological recovery; excellence; innovation; therapy EN neuroplasticity axonal plasticity dendritic plasticity synaptic plasticity functional neurological recovery excellence innovation therapy 1 3 3 06/09/22 20220603 NES 220603 Progress in Cellular Neuropathology is strongly facilitated by paradigm changes. Besides others, Ramon y Cajal postulated that adult central nervous system (CNS) neurons are able to sprout and create new axonal growth cone connections, which he considered as structural correlate of memory formation and learning (Sherrington, [13]). Ramon y Cajal concluded that CNS axons of adult human neurons exhibit limited potential for white matter tract regeneration and sprouting (Ramon y Cajal, [9]). [Extracted from the article]

At the end of 2019, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was discovered in China, causing a new coronavirus disease, termed COVID-19 by the WHO on February 11, 2020. At the time of this paper (January 31, 2021), more than 100 million cases have been recorded, which have claimed over 2 million lives worldwide. The most important clinical presentation of COVID-19 is severe pneumonia; however, many patients present various neurological symptoms, ranging from loss of olfaction, nausea, dizziness, and headache to encephalopathy and stroke, with a high prevalence of inflammatory central nervous system (CNS) syndromes. SARS-CoV-2 may also target the respiratory center in the brainstem and cause silent hypoxemia. However, the neurotropic mechanism(s) by which SARS-CoV-2 affects the CNS remain(s) unclear. In this paper, we first address the involvement of astrocytes in COVID-19 and then elucidate the present knowledge on SARS-CoV-2 as a neurotropic virus as well as several other neurotropic flaviviruses (with a particular emphasis on the West Nile virus, tick-borne encephalitis virus, and Zika virus) to highlight the neurotropic mechanisms that target astroglial cells in the CNS. These key homeostasis-providing cells in the CNS exhibit many functions that act as a favorable milieu for virus replication and possibly a favorable environment for SARS-CoV-2 as well. The role of astrocytes in COVID-19 pathology, related to aging and neurodegenerative disorders, and environmental factors, is discussed. Understanding these mechanisms is key to better understanding the pathophysiology of COVID-19 and for developing new strategies to mitigate the neurotropic manifestations of COVID-19. [ABSTRACT FROM AUTHOR]

In this paper, a prescribed performance adaptive event-triggered consensus control method is developed for a class ofmultiagent systems with the consideration of input dead zone and saturation. In practical engineering applications, systems are inevitably suffered from input saturation. In addition, input dead zone is widely existing. As the larger signal is limited and the smaller signal is difficult to effectively operate, system efficacious input encounters unknown magnitude limitations, which seriously impact system control performance and even lead to system instability. Furthermore, when constrained multiagent systems are required to converge quickly, the followers would achieve it with drastic and quick variation of states, which may violate the constraints and even cause security problems. To address those problems, an adaptive event-triggered consensus control is proposed. By constructing the transform function and the barrier Lyapunov function, while state constrained is guaranteed, multiagent systems quickly converge with prescribed performance. Finally, some examples are adopted to confirm the effectiveness of the proposed control method. [ABSTRACT FROM AUTHOR]

The package design can transmit the value of a product to consumers visually and can therefore influence the consumers' decisions. The traditional package design is an open-loop process in which a design can only be evaluated after the products are sent to the market. Thus, the designers cannot refine the design without any helpful advice. In this paper, a robust robot image classification is proposed to help the designers to evaluate their package design and improve their design in a closed-loop process, which is essentially the establishment of a cyber-physical system for the package design. The robust robot image classification adopts the total variation regularization, which ensures that the proposed robot image classification can give the right answers even if it is trained by noisy labels. The robustness against noisy labels is emphasized here since the historical data set of package design evaluations may have some false labels that can be equivalently regarded as disturbed labels from the true labels by noises. To validate the effectiveness of the proposed robot image classification method, experimental data-based validations have been implemented. The results show that the proposed method exhibits much better accuracy in classification compared to the traditional trainingmethod when noisy labels are used for the training process. [ABSTRACT FROM AUTHOR]

Although the increase in the use of dynamical modeling in the literature on cultural evolution makes current models more mathematically sophisticated, these models have yet to be tested or validated. This paper provides a testable deep active inference formulation of social behavior and accompanying simulations of cumulative culture in two steps: First, we cast cultural transmission as a bi-directional process of communication that induces a generalized synchrony (operationalized as a particular convergence) between the belief states of interlocutors. Second, we cast social or cultural exchange as a process of active inference by equipping agents with the choice of who to engage in communication with. This induces trade-offs between confirmation of current beliefs and exploration of the social environment. We find that cumulative culture emerges from belief updating (i.e., active inference and learning) in the form of a joint minimization of uncertainty. The emergent cultural equilibria are characterized by a segregation into groups, whose belief systems are actively sustained by selective, uncertainty minimizing, dyadic exchanges. The nature of these equilibria depends sensitively on the precision afforded by various probabilistic mappings in each individual's generative model of their encultured niche. [ABSTRACT FROM AUTHOR]

In a complicated forest environment, it is usual to install many ground-fixed devices, and patrol personnel periodically collects data from the device to detect forest pests and valuable wild animals. Unlike human patrols, UAV (Unmanned Aerial Vehicles) may collect data from ground-based devices. The existing UAV path planning method for fixed-point devices is usually acceptable for simple UAV flight scenes. However, it is unsuitable for forest patrol. Meanwhile, when collecting data, the UAV should consider the timeliness of the collected data. The paper proposes two-point path planning and multi-point path planning methods to maximize the amount of fresh information collected from ground-fixed devices in a complicated forest environment. Firstly, we adopt chaotic initialization and co-evolutionary algorithmto solve the two-point path planning issue considering all significant UAV performance and environmental factors. Then, a UAV path planning method based on simulated annealing is proposed for the multi-point path planning issue. In the experiment, the paper uses benchmark functions to choose an appropriate parameter configuration for the proposed approach. On simulated simple and complicated maps, we evaluate the effectiveness of the proposed method compared to the existing pathplanning strategies. The results reveal that the proposed ways can effectively produce a UAV patrol path with higher information freshness in fewer iterations and at a lower computing cost, suggesting the practical value of the proposed approach. [ABSTRACT FROM AUTHOR]

Introduction: Through remote sensing images, we can understand and observe the terrain, and its application scope is relatively large, such as agriculture, military, etc. Methods: In order to achievemore accurate and effcientmulti-source remote sensing data fusion and classification, this study proposes DB-CNN algorithm, introduces SVM algorithm and ELM algorithm, and compares and verifies their performance through relevant experiments. Results: From the results, we can find that for the dual branch CNN network structure, hyperspectral data and laser mines joint classification of data can achieve higher classification accuracy. On different data sets, the global classification accuracy of the joint classification method is 98.46%. DB-CNN model has the highest training accuracy and fastest speed in training and testing. In addition, the DB-CNN model has the lowest test error, about 0.026, 0.037 lower than the ELM model and 0.056 lower than the SVM model. The AUC value corresponding to the ROC curve of its model is about 0.922, higher than that of the other two models. Discussion: It can be seen that the method used in this paper can significantly improve the effect ofmulti-source remote sensing data fusion and classification, and has certain practical value. [ABSTRACT FROM AUTHOR]