Search

A popular measure for citation inequalities of individual scientists has been the Hirsch index (h). If for any scientist the number nc of citations is plotted against the serial number np of the papers having those many citations (when the papers are ordered from the highest cited to the lowest), then h corresponds to the nearest lower integer value of np below the fixed point of the non-linear citation function (or given by nc = h = np if both np and nc are a dense set of integers near the h value). The same index can be estimated (from h = s = ns) for the avalanche or cluster of size (s) distributions (ns) in the elastic fiber bundle or percolation models. Another such inequality index called the Kolkata index (k) says that (1 − k) fraction of papers attract k fraction of citations (k = 0.80 corresponds to the 80–20 law of Pareto). We find, for stress (σ), the lattice occupation probability (p) or the Kolkata Index (k) near the bundle failure threshold (σc) or percolation threshold (pc) or the critical value of the Kolkata Index kc a good fit to Widom–Stauffer like scaling h/[N/log⁡N] = f(N[σc−σ]α), h/[N/log⁡N]=f(N|pc−p|α) or h/[Nc/logNc]=f(Nc|kc−k|α), respectively, with the asymptotically defined scaling function f, for systems of size N (total number of fibers or lattice sites) or Nc (total number of citations), and α denoting the appropriate scaling exponent. We also show that if the number (Nm) of members of parliaments or national assemblies of different countries (with population N) is identified as their respective h − indexes, then the data fit the scaling relation Nm∼N/log⁡N, resolving a major recent controversy.

Microfluidic paper-based analytical devices (μPADs) are gaining popularity due to their low cost and ease of use, but controlling fluid flow for more complex biochemical assays within these devices remains challenging. This study investigates femtosecond laser ablation of nitrocellulose (NC), a preferred material for µPADs, to create mechanically switchable barriers and flow controllers. We investigated NC ablation using single laser pulses and spatially overlapping pulses that generate lines. Single pulse ablation thresholds were determined for wavelengths of 1,030 nm, 515 nm and 343 nm. Line ablation characteristics were investigated as a function of the temporal and spatial pulse separation and laser wavelength. High aspect ratio grooves (up to 4.26) were achieved under specific conditions. These grooves can be used to define the spatial separation of the flow in separated microchannels or to form a barrier line perpendicular to the microchannel that can modulate the temporal behavior of the fluid flow. This barrier introduced an additional high flow resistance slowing down the flow or, if it was designed to cut through nitrocellulose at the entire depth, completely stopped the liquid flow. It was further shown that a barrier formed in this way could be switched by mechanically bending the µPAD at the barrier position. The femtosecond laser patterning method presented here provides precise spatio-temporal control not only for flow branching and multiplexing, but also for controlling flow speed and switching flow on and off within the same manufacturing process. Our results open up new possibilities for complex, multi-step assays on µPADs.

The editorial in "Frontiers in Physics" discusses the historical significance of nuclear physics and its impact on scientific progress, technological advancements, and medical applications. It highlights the emergence of high-intensity lasers as a new tool for exploring nuclear processes and outlines the challenges and opportunities in laser-driven nuclear physics research. The editorial introduces a Research Topic comprising 18 research papers from prestigious institutions worldwide, focusing on experimental and theoretical aspects of laser-driven nuclear reactions, advanced particle sources, and computational methods for nuclear physics. The papers cover a wide range of topics, including fusion experiments, particle acceleration, and nuclear excitation studies, showcasing the interdisciplinary nature of the field and its potential for practical applications. [Extracted from the article]

Humans collaborate in different contexts such as in creative or scientific projects, in workplaces and in sports. Depending on the project and external circumstances, a newly formed collaboration may include people that have collaborated before in the past, and people with no collaboration history. Such existing relationships between team members have been reported to influence the performance of teams. However, it is not clear how existing relationships between team members should be quantified, and whether some relationships are more likely to occur in new collaborations than others. Here we introduce a new family of structural patterns, m-patterns, which formalize relationships between collaborators and we study the prevalence of such structures in data and a simple random-hypergraph null model. We analyze the frequency with which different collaboration structures appear in our null model and show how such frequencies depend on size and hyperedge density in the hypergraphs. Comparing the null model to data of human and non-human collaborations, we find that some collaboration structures are vastly under- and overrepresented in empirical datasets. Finally, we find that structures of scientific collaborations on COVID-19 papers in some cases are statistically significantly different from those of non-COVID-19 papers. Examining citation counts for 4 different scientific fields, we also find indications that repeat collaborations are more successful for 2-author scientific publications and less successful for 3-author scientific publications as compared to other collaboration structures. [ABSTRACT FROM AUTHOR]

Electronic paper display has received increasing attention due to its outstanding properties of wide viewing angle, bistable state and lower energy consumption. Among various electronic paper display technologies, electrowetting e-paper (EWD) is the most promising as it can achieve full-color display by using stacked layers or RGB color filter. However, the EWD is faced with several challenges, including poor color brightness, low contrast ratio and small color gamut. To improve the performance of the EWD, we proposed a new configuration of EWD consisting of three independent PCLC reflection films based on the cholesteric liquid crystal materials. We designed and experimentally fabricated three types of reflection films, and then added the PCLC films to the tri-layered EWD device. We experimentally characterized the reflectivity and color gamut of the sample in the dark room. It is found that the experimental results and simulation results match with each other. It is demonstrated that with the PCLC films, the reflectivity is improved by 20%, while the color gamut is improved by 80%, which is sufficient for the daily display demand of E-paper. The proposed EWD device containing PCLC reflection films provide a new strategy to improve the brightness and color gamut of current EWD device, and is promising for realizing the full-color E-paper display.

Regional innovation ecosystems (RIEs) are a new paradigm in innovation research, and the development of RIEs under digitization is constrained by the development of innovation environments. The study of ecological niche suitability of regional innovation environment provides the basis for the government to create an innovation environment suitable for the development of a regional innovation ecosystem. This paper conducts an empirical analysis based on panel data from 29 provinces (autonomous regions) in China from 2012 to 2021. The paper analyzes the suitability of the ecological niche of the innovation environment as well as the spatio-temporal evolution pattern of the regional innovation ecosystem under digitalization in China. The results of the study show that: (1) The innovation environment of regional innovation ecosystems under digitization in China has become increasingly suitable during the sample period. (2) From the perspective of evolution in time, the suitability of the ecological niche of the regional innovation environment has always maintained an evolutionary trend of rolling forward in the echelon. (3) From the perspective of spatial evolution, the center line of the curve of the development level of the suitability of the regional innovation environment shows a rightward shifting evolution. [ABSTRACT FROM AUTHOR]

Microfluidic paper-based analytical devices (μPADs) are gaining popularity due to their low cost and ease of use, but controlling fluid flow for more complex biochemical assays within these devices remains challenging. This study investigates femtosecond laser ablation of nitrocellulose (NC), a preferred material for µPADs, to create mechanically switchable barriers and flow controllers. We investigated NC ablation using single laser pulses and spatially overlapping pulses that generate lines. Single pulse ablation thresholds were determined for wavelengths of 1,030 nm, 515 nm and 343 nm. Line ablation characteristics were investigated as a function of the temporal and spatial pulse separation and laser wavelength. High aspect ratio grooves (up to 4.26) were achieved under specific conditions. These grooves can be used to define the spatial separation of the flow in separated microchannels or to form a barrier line perpendicular to the microchannel that can modulate the temporal behavior of the fluid flow. This barrier introduced an additional high flow resistance slowing down the flow or, if it was designed to cut through nitrocellulose at the entire depth, completely stopped the liquid flow. It was further shown that a barrier formed in this way could be switched by mechanically bending the µPAD at the barrier position. The femtosecond laser patterning method presented here provides precise spatio-temporal control not only for flow branching and multiplexing, but also for controlling flow speed and switching flow on and off within the same manufacturing process. Our results open up new possibilities for complex, multi-step assays on µPADs. [ABSTRACT FROM AUTHOR]

Composite structure is widely used in various technological fields because of its superior material properties. Composite structure detection technology has been exploring efficient and fast damage detection technology. In this paper, image-based NDT technology is proposed to detect composite damage using deep learning. A data set was established through literature, which contained images of damaged and non-damaged composite material structures. Then, five convolutional neural network models Alexnet, VGG16, ResNet-34, ResNet-50, and GoogleNet were used to automatically classify the damage. Finally, the performance of five pre-trained network architectures is evaluated, and the results show that RESNET-50 technology can successfully detect damage in a reasonable computation time with the highest accuracy and low complexity using relatively small image datasets. [ABSTRACT FROM AUTHOR]

Introduction: As 5G networks become widespread and their application scenarios expand, massive amounts of traffic data are continuously generated. Properly analyzing this data is crucial for enhancing 5G services. Methods: This paper uses the visibility graph method to convert 5G traffic data into a visibility graph network, conducting a feature analysis of the 5G traffic data. Using the AfreecaTV dataset as the research object, this paper constructs visibility networks at different scales and observes the evolution of degree distribution with varying data volumes. The paper employs the Hurst index to evaluate the 5G traffic network and uses community detection to study the networks converted from 5G traffic data of different applications. Results: Experimental results reveal significant differences in node degree distribution and topological structures of 5G traffic data across different application scenarios, such as star structures and multiple subnetwork structures. It is found that the node degree distribution of 5G traffic networks exhibits heterogeneity, reflecting the uneven growth of node degrees during network expansion. The Hurst index analysis discovers that the 5G traffic network retains the long-term dependence and trends of the original data. Through community detection, it is observed that networks converted from 5G traffic data of different applications exhibit diverse community structures, such as high centrality nodes, star-like community structures, modularity, and multilayer characteristics. Discussion: These findings indicate that 5G traffic networks in different application scenarios exhibit complex and diverse characteristics. The heterogeneity of node degree distribution and differences in topological structures reflect the imbalance in node connection methods during network expansion. The results of the Hurst index show that the 5G traffic network inherits the long-term dependence of the original data, providing a basis for analyzing the dynamic characteristics of the network. The diverse community structures reveal the inherent modularity and hierarchy of the network, which helps to understand the performance and optimization directions of 5G networks in different applications. [ABSTRACT FROM AUTHOR]

Background: Developmental dislocation of the hip joint (DDH) is a condition that severely threatens children's healthy growth. Without timely and correct treatment, it will lead to osteoarthritis and hip dysfunction in the evolution of children. Objective: It is essential to develop an intelligent model for diagnosing hip dislocation and performing accurate quantitative analysis. Methods: In this paper, 46 cases of computed tomography (CT) images were retrospectively collected, including 19 cases of hip dislocation and 27 cases of healthy people. The experiment first uses ITK-SNAP to sketch the ilium and femoral head in the original image. Then, it uses 3D U-Net to send the label of the background, ilium, and femoral head into three channels, respectively, to realize the three-dimensional segmentation of the ilium and femoral head. Next, the extraction of the surface of the acetabulum and femoral head is performed. Subsequently, the erroneous points are eliminated, and the spherical surfaces of the acetabulum and femoral head are fitted using the least squares method. Ultimately, the spherical center distance is calculated quantitatively to predict whether the hip joint is dislocated. Results: Under the independent test set, the segmentation average dice coefficients of the ilium and femoral head are 89% and 93%, respectively. The spherical center distance between the acetabulum and femoral head is calculated quantitatively. If the value exceeds 10 mm, it is considered a hip dislocation. Compared with the doctor's diagnosis, the accuracy result is 94.4%. Conclusion: This paper successfully implements a precise and automated intelligent diagnostic system for the identification of hip dislocation. Commencing with the development of a 3D segmentation algorithm for the ilium and femoral head, we further introduce a novel method that computes the spherical distance for the prediction of hip dislocation. This approach provides robust quantitative analysis, thereby facilitating more informed clinical decision-making. [ABSTRACT FROM AUTHOR]

Due to the self-affine property of the grinding surface, the sample images with different roughness captured by the micron-scale camera exhibit certain similarities. This similarity affects the prediction accuracy of the deep learning model. In this paper, we propose an illumination method that can mitigate the impact of self-affinity using the two-scale fractal theory as a foundation. This is followed by the establishment of a machine vision detection method that integrates a neural network and correlation function. Initially, a neural network is employed to categorize and forecast the microscopic image of the workpiece surface, thereby determining its roughness category. Subsequently, the corresponding correlation function is determined in accordance with the established roughness category. Finally, the surface roughness of the workpiece was calculated based on the correlation function. The experimental results demonstrate that images obtained using this lighting method exhibit significantly enhanced accuracy in neural network classification. In comparison to traditional lighting methods, the accuracy of this method on the micrometer scale has been found to have significantly increased from approximately 50% to over 95%. Concurrently, the mean squared error (MSE) of the surface roughness calculated by the proposed method does not exceed 0.003, and the mean relative error (MRE) does not exceed 5%. The two-scale fractal geometry offers a novel approach to image processing and machine learning, with significant potential for advancement. [ABSTRACT FROM AUTHOR]

The detection of crucial components in the power distribution network is of great significance for ensuring the safe operation of the power grid. However, the challenges posed by complex environmental backgrounds and the difficulty of detecting small objects remain key obstacles for current technologies. Therefore, this paper proposes a detection method for crucial components in the power distribution network based on an improved YOLOv9 model, referred to as SGI-YOLOv9. This method effectively reduces the loss of fine-grained features and improves the accuracy of small objects detection by introducing the SPDConv++ downsampling module. Additionally, a global context fusion module is designed to model global information using a self-attention mechanism in both spatial and channel dimensions, significantly enhancing the detection robustness in complex backgrounds. Furthermore, this paper proposes the Inner-PIoU loss function, which combines the advantages of Powerful-IoU and Inner-IoU to improve the convergence speed and regression accuracy of bounding boxes. To verify the effectiveness of SGI-YOLOv9, extensive experiments are conducted on the CPDN dataset and the PASCAL VOC 2007 dataset. The experimental results demonstrate that SGI-YOLOv9 achieves a significant improvement in accuracy for small object detection tasks, with an mAP@50 of 79.1% on the CPDN dataset, representing an increase of 3.9% compared to the original YOLOv9. Furthermore, it achieves an mAP@50 of 63.3% on the PASCAL VOC 2007 dataset, outperforming the original YOLOv9 by 1.6%. [ABSTRACT FROM AUTHOR]

Regular inspections of pipelines are of great significance to ensure their long-term safe and stable operation, and the rapid 3D reconstruction of constant-diameter straight pipelines (CDSP) based on monocular images plays a crucial role in tasks such as positioning and navigation for pipeline inspection drones, as well as defect detection on the pipeline surface. Most of the traditional 3D reconstruction methods for pipelines rely on marked poses or circular contours of end faces, which are complex and difficult to apply, while some existing 3D reconstruction methods based on contour features for pipelines have the disadvantage of slow reconstruction speed. To address the above issues, this paper proposes a rapid 3D reconstruction method for CDSP. This method solves for the spatial pose of the pipeline axis based on the geometric constraints between the projected contour lines and the axis, provided that the radius is known. These constraints are derived from the perspective projection imaging model of the single-view CDSP. Compared with traditional methods, the proposed method improves the reconstruction speed by 99.907% while maintaining similar accuracy. [ABSTRACT FROM AUTHOR]

In recent years, satellite navigation systems have witnessed widespread adoption across diverse fields, including military surveillance, precision agriculture, traffic monitoring, resource exploration, and disaster assessment. However, navigation signals are susceptible to interference, with deceptive interference posing the most significant threat to navigation systems. This paper provides a comprehensive overview of satellite navigation spoofing and anti-spoofing techniques. It reviews the current state of spoofing and anti-spoofing technologies, analyzing advancements in spoofing techniques and the evolution of countermeasures. Furthermore, the paper elaborates on the impact of spoofing interference on receiver performance, examining its effects on positioning, timing, and velocity estimations. A detailed analysis of various anti-spoofing methods is presented, categorizing them into detection, identification, suppression, and localization techniques. This review aims to provide a thorough understanding of the evolving landscape of satellite navigation spoofing and anti-spoofing technologies, fostering further research and development efforts to ensure the integrity and resilience of satellite navigation systems in the face of sophisticated threats. [ABSTRACT FROM AUTHOR]

This article presents a thorough examination of the progress and limitations in the application of Natural Language Processing (NLP) and Machine Learning (ML), particularly Deep Learning (DL), in the healthcare industry. This paper examines the progress and limitations in the utilisation of Natural Language Processing (NLP) and Machine Learning (ML) in the healthcare field, specifically in relation to Electronic Medical Records (EMRs). The review also examines the incorporation of Natural Language Processing (NLP) and Machine Learning (ML) in medical imaging as a supplementary field, emphasising the transformative impact of these technologies on the analysis of healthcare data and patient care. This review attempts to analyse both fields in order to offer insights into the current state of research and suggest potential chances for future advancements. The focus is on the use of these technologies in Electronic Medical Records (EMRs) and medical imaging. The review methodically detects, chooses, and assesses literature published between 2015 and 2023, utilizing keywords pertaining to natural language processing (NLP) and healthcare in databases such as SCOPUS. After applying precise inclusion criteria, 100 papers were thoroughly examined. The paper emphasizes notable progress in utilizing NLP and ML methodologies to improve healthcare decision-making, extract information from unorganized data, and evaluate medical pictures. The key findings highlight the successful combination of natural language processing (NLP) and image processing to enhance the accuracy of diagnoses and improve patient care. The study also demonstrates the effectiveness of deep learning-based NLP pipelines in extracting valuable information from electronic medical records (EMRs). Additionally, the research suggests that NLP has the potential to optimize the allocation of medical imaging resources. The identified gaps encompass the necessity for scalable and practical implementations, improved interdisciplinary collaboration, the consideration of ethical factors, the analysis of longitudinal patient data, and the customization of approaches for specific medical situations. Subsequent investigations should focus on these deficiencies in order to fully exploit the capabilities of natural language processing (NLP) and machine learning (ML) in the healthcare sector, consequently enhancing patient outcomes and the delivery of healthcare services. [ABSTRACT FROM AUTHOR]

With the rapid advancement of pulse technology, given the extraordinarily high repetition frequency of high-repetition pulse interference, with pulse widths typically ranging from a few nanoseconds to several hundred nanoseconds, it possesses ultra-wideband characteristics, research on how to avoid spectral conflict and malicious interference between these pulse signals and navigation receiver systems has emerged as a pressing and popular issue. This paper introduces the mathematical model of high repetition pulse interference signals. Following this, the paper summarizes and analyses the transient response, nonlinear distortion, and linear distortion that accompany signal processing at the Radio Frequency (RF) front end of the receiver. It concludes that the main source of interference in the limiter's transient response is peak leakage, the primary factor in low noise amplifier's (LNA) interference is third-order intermodulation distortion, and filter interference is due to the interlaced response from adjacent pulses. Lastly, the current research progress on the mechanism of high repeti-tion pulse interference with navigation receivers is reviewed, providing reference for future study. [ABSTRACT FROM AUTHOR]

Introduction: As a new and efficient method for obstacle removal, laser clearance has promising applications in eliminating tree obstacles in transmission lines. This paper aims to explore various aspects related to the laser ablation of tree obstacles in transmission lines through simulation and experimentation. Methods: This paper employs simulation and experimentation to test the ignition time, burn-through time, peak temperature, and basic carbonization rate of various types of tree obstacles in transmission lines. Additionally, it analyzes the effects of tree density, laser power, clearance distance, and tree moisture content on laser ablation characteristics. Results: The results reveal that different types of tree obstacles exhibit distinct ablation characteristics. With increasing tree density, both the ignition and burn-through times increase, while the peak temperature and basic carbonization rate decrease. Additionally, laser power and clearance distance significantly influence laser ablation behavior. Higher laser energy density results in greater heat flux density, leading to shorter ignition and burn-through times and higher peak temperatures and carbonization rates. Moreover, the tree obstacle with the highest moisture content (58.4%) had the shortest burn-through time of 46.56 s, whereas the one with the lowest moisture content (14.8%) took the longest at 58.41 s, which demonstrates that increased moisture content enhances the laser ablation rate. Discussion: These findings provide a basis for the application of laser-based tree obstacle removal in power transmission lines. The understanding of how different factors such as tree density, laser power, clearance distance, and moisture content affect the laser ablation process can help in optimizing the laser clearance operations for more efficient removal of tree obstacles in transmission lines. [ABSTRACT FROM AUTHOR]

This paper presents the acoustic analysis of a three-dimensional cylindrical shell model under electromagnetic vibration, a critical factor affecting the performance of electric motors in various applications such as automotive, aerospace, and industrial systems. The study provides a multidisciplinary approach that integrates electromagnetics, structural vibration, and acoustics, solved using the fast multipole boundary element method (FMBEM). The results summarize the validation of the analytical models and numerical simulations, offering insights into effective vibration reduction methods. The conclusions indicate that the 3-D numerical analysis using FMBEM aligns well with the analytical solution for the sound pressure in the exterior acoustic domain of the cylindrical shell model. The paper contributes valuable insights for the design of low-noise motors and the control of electromagnetic vibration and noise in electric motors. [ABSTRACT FROM AUTHOR]

In the era of big data, the propagation of malicious software poses a significant threat to corporate data security. To safeguard data assets from the encroachment of malware, it is essential to conduct a dynamic analysis of various information propagation behaviors within software. This paper introduces a dynamic analysis detection method for malicious behavior based on feature extraction (MBDFE), designed to effectively identify and thwart the spread of malicious software. The method is divided into three stages: First, variable-length N-gram algorithms are utilized to extract subsequences of varying lengths from the sample APl call sequences as continuous dynamic features. Second, feature selection techniques based on information gain are employed to identify suitable classification features. Lastly, recurrent neural networks (RNN) are applied for the classification training and prediction of diverse software behaviors. Experimental results and analysis demonstrate that this approach can accurately detect and promptly interrupt the information dissemination of malicious software when such behavior occurs, thereby enhancing the precision and timeliness of malware detection. [ABSTRACT FROM AUTHOR]

The internal kink mode is one of the crucial factors affecting the stability of magnetically confined fusion devices. This paper explores the key features influencing the growth rate of internal kink modes using machine learning techniques such as Random Forest, Extreme Gradient Boosting (XGboost), Permutation, and SHapley Additive exPlanations (SHAP). We conduct an in-depth analysis of the significant physical mechanisms by which these key features impact the growth rate of internal kink modes. Numerical simulation data were used to train high-precision machine learning models, namely Random Forest and XGBoost, which achieved coefficients of determination values of 95.07% and 94.57%, respectively, demonstrating their capability to accurately predict the growth rate of internal kink modes. Based on these models, key feature analysis was systematically performed with Permutation and SHAP methods. The results indicate that resistance, pressure at the magnetic axis, viscosity, and plasma rotation are the primary features influencing the growth rate of internal kink modes. Specifically, resistance affects the evolution of internal kink modes by altering current distribution and magnetic field structure; pressure at the magnetic axis impacts the driving force of internal kink modes through the pressure gradient directly related to plasma stability; viscosity modifies the dynamic behavior of internal kink modes by regulating plasma flow; and plasma rotation introduces additional shear forces, affecting the stability and growth rate of internal kink modes. This paper describes the mechanisms by which these four key features influence the growth rate of internal kink modes, providing essential theoretical insights into the behavior of internal kink modes in magnetically confined fusion devices. [ABSTRACT FROM AUTHOR]

This article is a commentary on a paper by Ibrahim et al. that proposed the use of the shifted screened Kratzer potential for analyzing the spectra of diatomic molecules. The authors of the commentary analyze the results presented in the paper and point out several weaknesses and inaccuracies. They argue that the parameters used by Ibrahim et al. do not accurately represent the dissociation energy and equilibrium bond length, and that the figures presented in the paper are incomprehensible. Additionally, they criticize the calculation of the canonical partition function and argue that the resulting thermodynamic functions are not applicable to a physical system. [Extracted from the article]

In the railway system environment, the interconnection of a vast array of intelligent sensing devices has brought about revolutionary changes in the management and monitoring of railway transportation. However, this also poses challenges to the communication service quality within the railway Internet of Things (IoT). Through collective intelligence and collaboration, the nodes within the railway IoT can not only share data and information but also work synergistically to enhance the overall intelligence level and improve decision-making quality of the network. Therefore, this paper proposes a reconnection mechanism based on the computation of node game-theoretic benefits and optimizes this process with the concept of swarm intelligence collaboration. Initially, the game-theoretic benefit values of the nodes in the railway IoT network are calculated. Subsequently, based on the weight priority of the edges, the two edges with the larger weights are selected, and connections are established between nodes with similar game-theoretic benefit values to enhance the network's robustness. This approach enables rapid networking and efficient communication transmission within the railway IoT, providing robust assurance for the safe and stable operation of the railway. [ABSTRACT FROM AUTHOR]

This paper introduces a new green decomposition model of carbon productivity that aims to further analyze the regional differences in carbon productivity and its interaction with regional industrial performance. We combine desired outputs and undesired outputs orientation, and multiple factor inputs to derive a new green decomposition theorem, establish a new green decomposition model of carbon productivity, and obtain nine effects of regional carbon productivity differences. Empirical analysis is conducted using input-output data from 29 provinces and 15 industries in China, comparing the differences in carbon productivity from both the provincial and industry perspectives and exploring the mechanism of action. This paper provides theoretical basis and empirical evidence for regional carbon productivity enhancement and economic and industrial optimization from the perspective of multi-factor inputs, as well as policy insights for regional low-carbon transition development. [ABSTRACT FROM AUTHOR]

In recent years, Internet of Things security incidents occur frequently, which is often accompanied by malicious events. Therefore, anomaly detection is an important part of Internet of Things security defense. In this paper, we create a process whitelist based on the K-Core decomposition method for detecting anomalous processes in IoT devices. The method first constructs an IoT process network according to the relationships between processes and IoT devices. Subsequently, it creates a whitelist and detect anomalous processes. Our work innovatively transforms process data into a network framework, employing K-Core analysis to identify core processes that signify high popularity. Then, a threshold-based filtering mechanism is applied to formulate the process whitelist. Experimental results show that the unsupervised method proposed in this paper can accurately detect anomalous processes on real-world datasets. Therefore, we believe our algorithm can be widely applied to anomaly process detection, ultimately enhancing the overall security of the IoT. [ABSTRACT FROM AUTHOR]

In order to solve the problem of low gray level due to the few driving chips developed based on the photoelectric characteristics of electrowetting display, a driving method based on modulation is proposed to double the gray level of electrowetting display. In this method, the driving waveform based on pulse amplitude modulation and pulse width modulation hybrid modulation is designed, and the gray level-luminance curve of the electrowetting display is measured and analyzed. On this basis, the luminance nonlinear correction is carried out, and the improvement of 64 Gy levels to 127 Gy levels is realized by the principle of human visual persistence phenomenon. The experimental results show that the proposed driving scheme can break through the limitation of the driving chip and realize the multiplication of gray levels, in which 96% gray levels increase steadily with an average luminance difference of 0.07, and at the same time enhance the contrast and improve the display effect. [ABSTRACT FROM AUTHOR]

In recent years, a surge in malicious network incidents and instances of network information theft has taken place, with malware identified as the primary culprit. The primary objective of malware is to disrupt the normal functioning of computers and networks, all the while surreptitiously gathering users' private and sensitive information. The formidable concealment and latency capabilities of malware pose significant challenges to its detection. In light of the operational characteristics of malware, this paper conducts an initial analysis of prevailing malware detection schemes. Subsequently, it extracts fuzzy features based on the distinct characteristics of malware traffic. The approach then integrates traffic detection techniques with Type II fuzzy recognition theory to effectively monitor malware-related traffic. Finally, the paper classifies the identified malware instances according to fuzzy association rules. Experimental results showcase that the proposed method achieves a detection accuracy exceeding 90%, with a remarkably low false alarm rate of approximately 5%. This method adeptly addresses the challenges associated with malware detection, thereby making a meaningful contribution to enhancing our country's cybersecurity. [ABSTRACT FROM AUTHOR]

The editorial discusses the challenge of defining interdisciplinary physics and its evolving nature. It explores the hierarchical approach of physics, emphasizing experiments and synthesis. The Research Topic includes papers on interdisciplinary research in sociophysics, econophysics, social inequality, and the Hirsch index. The articles highlight the importance of collaboration across disciplines and the need to remove artificial boundaries for scientific progress. [Extracted from the article]

The impact of financial institution governance on systemic risk is crucial and controversial. In view of the important role of fund shareholding in the corporate governance of financial institutions, this paper takes financial institutions in China as the research object, constructs the association network of financial institutions based on fund shareholding, and empirically examines the impact of fund shareholding in financial institutions on systemic risk. The results show that fund shareholding significantly increases the systemic risk of financial institutions through the network. Convergence in funds' choice of investment targets is an important mechanism for risk contagion among banks, insurance companies, securities companies, and across sectors. The governance externality created by fund holdings in financial institutions is thus corroborated. Further analysis shows the peer effect of funds' investment behaviour is an important cause of share price convergence and governance convergence of held financial institutions, while majority shareholder monitoring and information transparency can effectively curb systemic risk contagion from governance externality. The study confirms the proposition of corporate governance externality in financial institutions, enriches the formation mechanism of systemic risk contagion from the perspective of corporate governance, and provides a theoretical guideline for effectively curbing the systemic risk caused by governance externality. [ABSTRACT FROM AUTHOR]

Introduction: Chemical special steels are widely used in chemical equipment manufacturing and other fields, and small defects on its surface (such as cracks and punches) are easy to cause serious accidents in harsh environments. Methods: In order to solve this problem, this paper proposes an improved defect detection algorithm for chemical special steel based on YOLOv8. Firstly, in order to effectively capture local and global information, a ParC2Net (Parallel-C2f) structure is proposed for feature extraction, which can accurately capture the subtle features of steel defects. Secondly, the loss function is adjusted to MPD-IOU, and its dynamic non-monotonic focusing characteristics are used to effectively solve the overfitting problem of the bounding box of low-quality targets. In addition, RepGFPN is used to fuse multi-scale features, deepen the interaction between semantics and spatial information, and significantly improve the efficiency of cross-layer information transmission. Finally, the RexSE-Head (ResNeXt-Squeeze-Excitation) design is adopted to enhance the positioning accuracy of small defect targets. Results and discussion: The experimental results show that the mAP@0.5 of the improved model reaches 93.5%, and the number of parameters is only 3.29M, which realizes the high precision and high response performance of the detection of small defects in chemical special steels, and highlights the practical application value of the model. The code is available at https://github.com/improvment/prs-yolo. [ABSTRACT FROM AUTHOR]

Time-dependent thermoremanent magnetization (TRM) studies have been instrumental in probing energy dynamics within the spin glass phase. In this paper, we review the evolution of the TRM experiment over the last half century and discuss some aspects related to how it has been used in the understanding of spin glasses. We also report on recent experiments using high-resolution DC SQUID magnetometry to probe the TRM at temperatures less than but nearto the transition temperature Tc. These experiments have been performed as a function of waiting time, temperature, and five different magnetic fields. We find that as the transition temperature is approached from below, the characteristic time scale of TRM is suppressed up to several orders of magnitude in time. In the highest-temperature region, we find that the waiting time effect subsides, and a waiting time-independent crossover line is reached. We also find that increasing the magnetic field further suppresses the crossover line. Using a first-principles energy argument across the crossover line, we derive an equation that is an excellent fit to the crossover lines for all magnetic fields probed. The data show strong evidence for critical slowing down and an H = 0 Oe phase transition. [ABSTRACT FROM AUTHOR]

This paper deals with the distribution of sources, transport, and exhaust of particles in a tokamak. Knowledge and understanding of all the physical phenomena involved in the global particle buildup are necessary to study and predict density regimes and subsequently to develop optimized scenarios for tokamak operation in order to control heat and particle exhaust. Neutral particles and their interactions with plasma are central in this perspective. This paper discusses the impact of varying the intensity of particle fueling in 2D transport simulations of a WEST discharge. Simulations are performed with an updated version of SOLEDGE-HDG that allows a more realistic transport of neutrals using a self-consistent diffusive model based on charge exchange and ionization processes. New code capabilities allow the entire WEST poloidal cross section to be simulated in a realistic configuration for both geometry and the range of control parameters. A gas puff scan illustrates the main features of the sheath-limited, high-recycling, and detached regimes, such as the buildup of the temperature gradient and the pressure drop in the scrape-off layer (SOL), the target temperature falling to 1 eV, and the ionization source moving away from the targets, as well as the particle flux rollover. A crude estimate of wall erosion is also provided, showing the respective role of each plasma wall component in each of these regimes. [ABSTRACT FROM AUTHOR]

In today's era of increasing energy constraints, harnessing the power of electromagnetic waves and converting them into directly usable energy has great potential in the field of renewable energy. This paper presents a highly efficient electromagnetic energy harvester with wide operation angles based on metasurface. The metasurface unit comprises four rotated copper rings plated on a Rogers RT5880 substrate. This unique design incorporates a 3.6 kΩ resistor within each of the four-cornered rotating metal rings, effectively emulating the input impedance circuit of a rectifier. Simulation results reveal that the metasurface achieves a remarkable electromagnetic wave harvesting efficiency of up to 92.3% at a frequency of 5.85 GHz. Additionally, it exhibits polarization insensitivity, enhancing its versatility and suitability for wide incident angles, which augments its practicality in various environments. The proposed metasurface energy harvester represents a significant advancement in electromagnetic energy collection technology, offering an efficient and reliable energy harvesting solution that contributes to addressing energy shortages and is applicable across multiple scenarios. [ABSTRACT FROM AUTHOR]

Cable insulation defect detection ensures electrical safety, prevents accidents, extends equipment life and guarantees stable system operation. For the traditional cable insulation defect detection and identification of difficult problems, this paper proposes the use of ultrasonic cable insulation defect detection and combined with the Convolutional Neural Network (CNN)-transformer model of cable insulation defect classification method. Firstly, the ultrasonic probe is used to obtain different cable insulation defect signals, and then the CNN-transformer model is used to classify different cable insulation defects. The CNN is used to initially extract the characteristics of the cable insulation defects from the input signals, and then the multi-attention mechanism in the time series Transformer is used to extract the transient local and periodic global characteristics of the cable insulation defect signals. The deeper transient local features and periodic global features of the cable insulation defect signal are extracted by the multi-attention mechanism in the time series Transformer; finally, the recognition results are outputted by the fully connected layer and softmax classifier. The results show that ultrasonic reflection and transmission phenomena occur at the defects, and different defects can be accurately reflected by the defect echo time and amplitude, and the accuracy of cable insulation defect recognition using the CNN-transformer model reaches 100%, with good generalization ability. [ABSTRACT FROM AUTHOR]

The traditional base station in C-RAN is divided into three parts: a pool of centralized baseband units (BBUs), a fronthaul network that links the BBUs and remote radio units (RRUs), and RRUs. This paper proposes a novel cooperative algorithm for resource optimization in a time-wavelength division multiplexed (TWDM) passive optical network (PON) incorporating a cloud radio access network (C-RAN). First, a joint collaborative strategy is deployed to optimize cooperative caching and transmission in the wireless and optical domains. Then, the quality of experience (QoE) is improved by bandwidth configuration and caching. Simulation results show that the average throughput of the proposed QoE-aware video cooperative caching and transmission mechanism (QACCTM) algorithm is approximately 30% higher than that of other algorithms. Compared with the relative average residual clutter power (RARCP) and quality-aware wireless edge caching (QAWEC) algorithms, the proposed QACCTM algorithm reduces the access delay by approximately 27.1% and 15.9%, respectively. [ABSTRACT FROM AUTHOR]

This paper is devoted to investigating a class of stochastic neutral-type neural networks with delays. By using the fixed point theorem and the properties of neutral-type operator, we obtain the existence conditions for periodic solutions of stochastic neutral-type neural networks. Furthermore, we obtain the conditions for the exponential stability of periodic solutions using Gronwall-Bellman inequality and stochastic analysis technique. Finally, a numerical example is given to show the effectiveness and merits of the present results. Our results can be used to obtain the existence and exponential stability of periodic solution to the corresponding deterministic systems. [ABSTRACT FROM AUTHOR]

In order to effectively prevent and combat online public opinion crises triggered by major emergencies, this paper explores the dissemination mechanism of uncertain information on online social platforms. According to the decisionmaking behavior of netizens after receiving uncertain information, they are divided into eight categories. Considering that there will be a portion of netizens who clarify uncertain information after receiving it, this paper proposes a SEFTFbTbMR model of uncertain information clarification behavior. The propagation dynamics equations of the model are given based on the theory of differential equations, the basic regeneration number R0 of the model is calculated, and the existence and stability of the equilibrium point of the model are analyzed. The theoretical analysis of the model is validated using numerical simulation software, and sensitivity analysis is performed on the parameters related to R0. In order to reduce the influence caused by uncertain information, the optimal control strategy of the model is proposed using the Hamiltonian function. It is found that the dissemination of uncertain information among netizens can be suppressed by strengthening the regulation of social platforms, improving netizens' awareness of identifying the authenticity of information, and encouraging netizens to participate in the clarification of uncertain information. The results of this work can provide a theoretical basis for future research on the uncertain information dissemination mechanism triggered by major emergencies. In addition, the results can also provide methodological support for the relevant government departments to reduce the adverse effects caused by uncertain information in the future. [ABSTRACT FROM AUTHOR]

Introduction: Optical and SAR image matching is one of the fields within multisensor imaging and fusion. It is crucial for various applications such as disaster response, environmental monitoring, and urban planning, as it enables comprehensive and accurate analysis by combining the visual information of optical images with the penetrating capability of SAR images. However, the differences in imaging mechanisms between optical and SAR images result in significant nonlinear radiation distortion. Especially for SAR images, which are affected by speckle noises, resulting in low resolution and blurry edge structures, making optical and SAR image matching difficult and challenging. The key to successful matching lies in reducing modal differences and extracting similarity information from the images. Method: In light of this, we propose a structure similarity virtual map generation network (SVGNet) to address the task of optical and SAR image matching. The core innovation of this paper is that we take inspiration from the concept of image generation, to handle the predicament of image matching between different modalities. Firstly, we introduce the Attention U-Net as a generator to decouple and characterize optical images. And then, SAR images are consistently converted into optical images with similar textures and structures. At the same time, using the structural similarity (SSIM) to constrain structural spatial information to improve the quality of generated images. Secondly, a conditional generative adversarial network is employed to further guide the image generation process. By combining synthesized SAR images and their corresponding optical images in a dual channel, we can enhance prior information. This combined data is then fed into the discriminator to determine whether the images are true or false, guiding the generator to optimize feature learning. Finally, we employ least squares loss (LSGAN) to stabilize the training of the generative adversarial network. Results and Discussion: Experiments have demonstrated that the SVGNet proposed in this paper is capable of effectively reducing modal differences, and it increases the matching success rate. Compared to direct image matching, using image generation ideas results in a matching accuracy improvement of more than twice. [ABSTRACT FROM AUTHOR]

This document is an editorial published in the journal Frontiers in Physics. It discusses the theory and applications of long-range dependent (LRD) processes, which are of interest to researchers in various fields, including physics and computer science. LRD processes differ from conventional random processes in terms of their autocorrelation function, power spectrum density, and probability density function. The editorial highlights two models of LRD processes: fractional Gaussian noise (fGn) and the generalized Cauchy process. It also mentions several papers included in the research topic, which explore the use of LRD processes in precision time protocol, dynamic routing algorithms, identification of cryptomining behavior, and modeling global sea surface chlorophyll concentration. [Extracted from the article]

Currently, there is limited research on the influence of gas ionization on the pulse formation process in pulse power source-driven loads. This paper introduces a road-field-Particle-In-Cell (PIC)/Monte Carlo Collision (MCC) collaborative simulation method that can accurately simulate gas ionization in Linear Transformer Driver (LTD) electron beam generation (EBG). The method couples the electromagnetic field and charged particle simulated through PIC/MCC with the circuit modules, and the load's voltammetry characteristics can real-time feedback to the Blumlein Pulse Forming Network (BPFN) of the LTD. In contrast to prior simulations that used fitted ideal T-shaped pulse input waveforms to model the load, this method provides a clearer depiction of the influence of gas ionization on the pulse shape. Additionally, the paper conducts simulation studies on LTD electron beam generator operating at different argon gas pressures. The findings indicate that introducing gas can effectively increase current while reducing voltage amplitude, thereby lowering the diode impedance. A small amount of gas can slightly enhance peak power, but excessive gas diminishes peak power and significantly shortens voltage pulse width. This is attributed to the beneficial effect of a small amount of gas ionization-produced plasma on the device. However, an excessive amount of gas ionization-generated plasma can lead to impedance mismatch in the device, even resulting in a load short circuit. This phenomenon causes a decrease in pressure drop at the top, consequently shortening the pulse width. [ABSTRACT FROM AUTHOR]

The aim of this paper is to analyze the impact of the digital transformation of banks on their systemic risks. We find that the digital transformation of commercial banks can significantly inhibit the systemic risk of banks, and this conclusion is still valid after considering the endogeneity of the model. The bank's digital transformation reduces its systemic risk by increasing its own competitiveness. Further analysis shows that the reduction of banks' marginal costs due to digital transformation is a key factor in promoting banks' competitiveness as the mechanism by which digital transformation reduces banks' systemic risk. The role of bank digital transformation in reducing systemic risk is heterogeneous, which is more obvious in large commercial banks, commercial banks that have not established financial technology subsidiaries, and systemically important banks. [ABSTRACT FROM AUTHOR]

This paper investigates the decision-making behaviors of opinion leaders and netizens in the context of uncertain information dissemination with the aim of effectively managing online public opinion crises triggered by major sudden events. The decision-making behaviors of opinion leaders are categorized into positive and negative guidance, while those of netizens are classified into acceptance and nonacceptance. Using an evolutionary game model, this study introduces random factors to examine their influence on the decisionmaking processes of both groups. A stochastic evolutionary game model is constructed to analyze the behaviors of opinion leaders and netizens in the context of uncertain information dissemination. The evolutionary stability strategies and stochastic evolutionary processes of the model are analyzed based on the theory of Itô stochastic differential equations. The impacts of key variables such as random disturbances, the degree of psychological identification of netizens with opinion leaders, and the intensity of government penalties for those spreading negative information are examined through numerical simulations. The findings indicate that opinion leaders evolve to make stable strategies more rapidly than netizens do; random disturbances slow the evolution of stable strategies for both groups but do not alter their strategic choices; a higher degree of psychological identification increases the likelihood of netizens adopting the views of opinion leaders; and as punitive measures intensify, both opinion leaders and netizens are inclined to choose strategies of positive guidance and acceptance. The results of this study offer theoretical insights and decision-making guidance for future government strategies for managing similar online collective behaviors. [ABSTRACT FROM AUTHOR]

Dimension reduction is an effective method for system's resilience analysis. In this paper, we investigate the effect of network structure on the accuracy of resilience dimension reduction. First, we introduce the resilience dimension reduction method and define the evaluation indicator of the resilience dimension reduction method. Then, by adjusting node connections, preferential connection mechanisms, and connection probabilities, we generate artificial networks, small-world networks and social networks with tunable assortativity coefficients, average clustering coefficients, and modularities, respectively. Experimental results for the gene regulatory dynamics show that the network structures with positive assortativity, large clustering coefficient, and significant community can enhance the accuracy of resilience dimension reduction. The result of this paper indicates that optimizing network structure can enhance the accuracy of resilience dimension reduction, which is of great significance for system resilience analysis and provides a new perspective and theoretical basis for selecting dimension reduction methods in system resilience analysis. [ABSTRACT FROM AUTHOR]

Introduction: Rydberg atoms are ideal for measuring electric fields due to their unique physical properties. However, low-frequency electric fields below MHz can be challenging due to the accumulation of ionized free electrons on the atomic vapor cell's surface, acting as a shield. Method: This paper proposes a Cavity-enhanced three-photon system (CETPS) measurement scheme, which uses a long-wavelength laser to excite the Rydberg state, reducing atomic ionization and enhancing detection spectrum resolution. A theoretical model is proposed to explain the quantum coherence effect of the light field, measured electric field, and the atomic system. Result: The results show that the proposed scheme significantly increases the electromagnetically induced transparency (EIT) spectral peak and narrows the spectral width, resulting in the maximum slope increasing by more than an order of magnitude. Discussion: The paper also discusses the impact of the Rabi frequency of the two laser fields and the coupling coefficient of the optical cavity on the transmission spectrum amplitude and linewidth, along with the optimal configuration of these parameters in the CEPTS scheme. [ABSTRACT FROM AUTHOR]

To enhance the impact resistance of penetration fuze, this paper investigates the response of fuze buffer materials to stress waves and develops a model for stress wave transmission inside the fuze. The stress concentration impacts of different cell structures of Imitation Bamboo Type Penetration Fuze Buffer Protection Structure (IBS) under stress wave action are compared and analyzed. The paper elucidates the impact of different cell parameters on stress concentration impacts, establishes nonlinear fitting functions of Stress Concentration Factor (SCF) and cell parameters, and solves the prediction error. Based on the wave function expansion method, an expression for Dynamic Stress Concentration Factor (DSCF) when stress waves interact with the potting material is derived, and numerical results of DSCF around bubbles under different physical parameters are provided. Finally, dynamic impact tests are conducted on the combined buffer scheme of penetration fuze. Impact test results show that, under an initial velocity impact of 50m/s, the overload peak attenuation rate is 39.42%, and under an initial velocity impact of 70 m/s, the overload peak attenuation rate is 32.87%. IBS can effectively protect the electronic components inside the fuze. [ABSTRACT FROM AUTHOR]

The Lorentz symmetry breaking theory not only affects the space-time background but also the dynamic behavior of bosons and fermions in curved space-time. Therefore, the Lorentz symmetry breaking theory will affect the quantum tunneling rate, Hawking temperature, black hole entropy, and other physical quantities of black holes. According to the modification of the space-time background and the modification of the particle dynamic equations, the quantum tunneling radiation of the Kerr-Sen-like black hole in bumblebee gravitational theory and its related contents are deeply studied. The research methods and a series of new results obtained in this paper are discussed. This makes the research methods and conclusions in this paper of more astrophysical significance and reference value. [ABSTRACT FROM AUTHOR]

The accuracy of the pointing error in the subreflector is of great importance to the large steerable radio telescope, so the measurement of the six degree-of- freedom (DOF) poses of the subreflector is crucial, which are measured by the detector and the laser array. The errors in the pose measurement system of the subreflector contain random measure errors and fitting iteration errors; all of them will add up to the pose measurement, which affects the accuracy of the six DOF parameters. This paper proposed the accuracy analysis method in the pose measurement of the subreflector of large antennas, which includes the error in the variable model in laser three-dimensional point space linear fitting, iterative accuracy analysis of spatial equations in Newton's downhill method, and the analysis of the adjustment theory of the Bursa model. The experimental results show that the measure time in six-DOF poses of the subreflector in a large- aperture antenna is at most 0.5 s, while the measurement accuracy error of translation is within 0.0131 mm and the error of rotation is within 0.2323°, which indicates that the pose measurement method of the subreflector in large antennas based on stereo structured light is efficient and applicable enough to analyze the measurement system accuracy. [ABSTRACT FROM AUTHOR]

Accurately predicting landslide displacement is essential for reducing and managing associated risks. To address the challenges of both underdecomposition and over-decomposition in landslide displacement analysis, as well as the low predictive accuracy of individual models, this paper proposes a novel prediction model based on time series theory. This model integrates a Convolutional Neural Network (CNN) with a Bidirectional Long-Short Term Memory network (BiLSTM) and an attention mechanism to form a comprehensive CNN-BiLSTM-Attention model. It harnesses the feature extraction capabilities of CNN, the bidirectional data mining strength of BiLSTM, and the focus-enhancing properties of the attention mechanism to enhance landslide displacement predictions. Furthermore, this paper proposes utilizing the Variational Mode Decomposition (VMD) method to decompose both landslide displacement and its influencing factors. The VMD algorithm's parameters are optimized through the Sparrow Search Algorithm (SSA), which effectively minimizes the influence of subjective bias while maintaining the integrity of the decomposition. A fusion of the Maximal Information Coefficient (MIC) and Grey Relational Analysis (GRA) is then employed to identify the critical influencing factors. The selected sequence of factors that conforms to the criteria is used as the input variable for displacement prediction via the CNN-BiLSTM-Attention model. The cumulative displacement prediction is derived by aggregating the results from each sequence. The study reveals that the SSA-VMD-CNN-BiLSTM-Attention model introduced herein achieves superior predictive accuracy for both periodic and random term displacements than individual models. This advancement provides a dependable benchmark for forecasting displacement in similar landslide scenarios. [ABSTRACT FROM AUTHOR]

In this paper we present measurements performed with a Micromegas X-ray detector setup. The detector is a prototype in the context of the BabyIAXO helioscope, which is under construction to search for an emission of the hypothetical axion particle from the Sun. An important component of such a helioscope is a low background X-ray detector with a high efficiency in the 1-10 keV energy range. The goal of the measurement was to study techniques for background discrimination. In addition to common techniques we used a multilayer veto system designed to tag cosmic-ray induced neutron background. Over an effective time of 52 days, a background level of 8.6 χ 10[sup -7] counts keV[sup -1] cm[sup -2] s[sup -1] was reached in a laboratory at above ground level. This is the lowest background level achieved at surface level. In this paper we present the experimental setup, show simulations of the neutron-induced background, and demonstrate the process to identify background signals in the data. Finally, prospects to reach lower background levels down to 10[sup -7] counts keV[sup -1] cm[sup -2] s[sup -1] are discussed. [ABSTRACT FROM AUTHOR]

Maritime area detection technology applies equipment such as high-orbit satellites, gateway ships and Unmanned Aerial Vehicles to detection. In this scenario, real-time uploading and analysis of maritime data is crucial. In the existing scenario, UAV data are gathered to the gateway ship and uploaded to the shore-based cloud via the high-orbit satellite, because the communication distance of the high-orbit satellite is far, and when the uploaded data volume is large or the access to the equipment increases, the propagation delay of the uploading of the data from the gateway ship to the satellite and the forwarding of the data from the satellite to the shore-based cloud is longer, and the processing delay of the shore-based cloud is increased, and the efficiency of the data transmission and communication will be affected as well. Aiming at the problem of increasing delay caused by communication limitations in maritime area detection, this paper proposes a maritime area detection scheme based on cloud-side collaboration. The scheme solves the problem of communication limitation from the following two aspects. First, the edge computing nodes are deployed on the ship side of the gateway, and the optimal offloading ratio is sought through game theory to offload a part of the tasks from the center cloud to the edge cloud for processing, which improves the efficiency of processing data and thus reduces the data transmission latency and data processing delay. Secondly, low-orbit (LEO) satellites are introduced to provide communication services, because low-orbit satellites have low orbital altitude and short propagation delay, which can transmit the data at the gateway ship to the shore-based cloud more quickly and improve the data transmission efficiency. Finally, it is also verified by designing experiments that the proposed scheme adopts the optimal offloading ratio and has a lower total delay than the original scheme, thus proving the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]