Search Results (41)

To address the limitations of the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm in autonomous control tasks including low convergence efficiency, poor training stability, inadequate adaptability of confrontation strategies, and challenges in handling sparse reward tasks—this paper proposes an enhanced algorithm by integrating the Rainbow module. The proposed algorithm improves long-term reward optimization through prioritized experience replay (PER) and multi-step TD updating mechanisms. Additionally, a dynamic reward allocation strategy is introduced to enhance the collaborative and adaptive decision-making capabilities of agents in complex adversarial scenarios. Furthermore, behavioral cloning is employed to accelerate convergence during the initial training phase. Extensive experiments are conducted on the MaCA simulation platform for 5 vs. 5 to 10 vs. 10 UAV island capture missions. The results demonstrate that the Rainbow-MADDPG outperforms the original MADDPG in several key metrics: (1) The average reward value improves across all confrontation scales, with notable enhancements in 6 vs. 6 and 7 vs. 7 tasks, achieving reward values of 14, representing 6.05-fold and 2.5-fold improvements over the baseline, respectively. (2) The convergence speed increases by 40%. (3) The combat effectiveness preservation rate doubles that of the baseline. Moreover, the algorithm achieves the highest average reward value in quasi-rectangular island scenarios, demonstrating its strong adaptability to large-scale dynamic game environments. This study provides an innovative technical solution to address the challenges of strategy stability and efficiency imbalance in multi-agent autonomous control tasks, with significant application potential in UAV defense, cluster cooperative tasks, and related fields. Full article

Representatives of Dermestidae (skin, larder, and carpet beetles) play a crucial role as decomposers in global ecosystems, facilitating the recycling of animal and plant biomass to sustain nutrient cycling. Despite their widespread ecological presence and functional importance, the fossil record of their larval stages has remained sparse, with previous documentation limited to occasional discoveries. This study significantly expands the larval fossil record by identifying 36 amber-preserved specimens from the Cretaceous, Eocene, and Miocene time slices, obtained from deposits distributed globally. By challenging the historical view of larval fossil rarity, we reveal morphological changes in defensive setae over geological time, demonstrating that Cretaceous and later fossil larvae possess significantly longer absolute and relative setal lengths compared to their extant counterparts. These findings, bolstered by quantitative comparisons of setal and body dimensions across fossil and extant representatives, indicate evolutionary adaptations in defensive structures dating back at least 100 million years. Our results offer new insights into the paleobiology of the group Dermestidae, highlighting how the morphology of larvae potentially reflects historical ecological pressures and resources availability. This study emphasizes the importance of integrating fossil evidence with comparative morphology to elucidate the evolutionary trajectories and functional roles of larvae in ancient terrestrial ecosystems. Full article

Faced with challenges posed by sophisticated cyber attacks and dynamic characteristics of cyberspace, the autonomous cyber defense (ACD) technology has shown its effectiveness. However, traditional decision-making methods for ACD are unable to effectively characterize the network topology and internode dependencies, which makes it difficult for defenders to identify key nodes and critical attack paths. Therefore, this paper proposes an enhanced decision-making method combining graph embedding with reinforcement learning algorithms. By constructing a game model for cyber confrontations, this paper models important elements of the network topology for decision-making, which guide the defender to dynamically optimize its strategy based on topology awareness. We improve the reinforcement learning with the Node2vec algorithm to characterize information for the defender from the network. And, node attributes and network structural features are embedded into low-dimensional vectors instead of using traditional one-hot encoding, which can address the perceptual bottleneck in high-dimensional sparse environments. Meanwhile, the algorithm training environment Cyberwheel is extended by adding new fine-grained defense mechanisms to enhance the utility and portability of ACD. In experiments, our decision-making method based on graph embedding is compared and analyzed with traditional perception methods. The results show and verify the superior performance of our approach in the strategy selection of defensive decision-making. Also, diverse parameters of the graph representation model Node2vec are analyzed and compared to find the impact on the enhancement of the embedding effectiveness for the decision-making of ACD. Full article

Sialidases/neuraminidases remove terminal sialic acid residues from glycoproteins, glycolipids, and oligosaccharides. Our previous research has revealed the distribution of sialidase in non-clinical fungal isolates from different ecological niches, including Antarctica. Fungi adapted to extremely low temperatures possess defense mechanisms necessary for their survival such as the response against oxidative stress. The relationship between oxidative stress and sialidase synthesis has been studied extremely sparsely. The aim of the present study was to investigate the involvement of sialidase in the cell response of the Antarctic strain P. griseofulvum P29 against oxidative stress induced by long- and short-term exposure to low temperatures. The changes in growth temperatures for 120 h (long-term stress) affected biomass accumulation, glucose consumption, sialidase synthesis, and the activity of the antioxidant enzymes superoxide dismutase (SOD) and catalase (CAT). The short-term temperature downshift (6 h) caused oxidative stress, evidenced by changes in the levels of biomarkers, including lipid peroxidation, oxidatively damaged proteins, and the accumulation of reserve carbohydrates. Simultaneously, a sharp increase in SOD and CAT activity was found, which coincided with a significant increase in sialidase activity. This study marks the first demonstration of increased sialidase activity in filamentous fungi isolated from extreme cold environments as a response to oxidative stress. Full article

This study addresses the problems of multi-source data redundancy, insufficient feature capture timing, and delayed risk warning in the prediction of gas concentration in fully mechanized coal-mining operations by constructing a three-pronged technical approach that integrates feature dimensionality reduction, hybrid modeling, and intelligent early warning. First, sparse kernel principal component analysis (SKPCA) is used to accomplish the feature decoupling of multi-source monitoring data, and its optimal dimensionality reduction performance is verified using long-term and short-term neural networks (LSTMs). Second, an innovative TCN–Transformer hybrid architecture is proposed. The transient fluctuation characteristics of gas concentration are captured using causal dilation convolution, while a multi-head self-attention mechanism is used to analyze the cross-scale correlation of geological mining parameters. A flood optimization algorithm (FLA) is used to establish a hyperparameter collaborative optimization framework. Compared to TCN-LSTM, CNN-GRU, and other hybrid models, the hybrid model proposed in this study exhibits superior point prediction performance, with a maximum R² of 0.980988. Finally, a dynamic confidence interval is established using the locally weighted kernel density estimation (LWD-KDE) method with an optimized bandwidth, and an unsupervised early warning mechanism for the risk of gas concentration fluctuations in coal mines is constructed. The results provide a comprehensive approach to preventing and controlling gas disasters in fully mechanized mining operations. This research effectively promotes the transformation and upgrading of coal-mine-safety-monitoring systems to an active defense paradigm. Full article

This review systematically explores the application of machine learning (ML) models in the context of Intrusion Detection Systems (IDSs) for modern network security, particularly within 5G environments. The evaluation is based on the 5G-NIDD dataset, a richly labeled resource encompassing a broad range of network behaviors, from benign user traffic to various attack scenarios. This review examines multiple machine learning (ML) models, assessing their performance across critical metrics, including accuracy, precision, recall, F1-score, Receiver Operating Characteristic (ROC), Area Under the Curve (AUC), and execution time. Key findings indicate that the K-Nearest Neighbors (KNN) model excels in accuracy and ROC AUC, while the Voting Classifier achieves superior precision and F1-score. Other models, including decision tree (DT), Bagging, and Extra Trees, demonstrate strong recall, while AdaBoost shows underperformance across all metrics. Naive Bayes (NB) stands out for its computational efficiency despite moderate performance in other areas. As 5G technologies evolve, introducing more complex architectures, such as network slicing, increases the vulnerability to cyber threats, particularly Distributed Denial-of-Service (DDoS) attacks. This review also investigates the potential of deep learning (DL) and Deep Transfer Learning (DTL) models in enhancing the detection of such attacks. Advanced DL architectures, such as Bidirectional Long Short-Term Memory (BiLSTM), Convolutional Neural Networks (CNNs), Residual Networks (ResNet), and Inception, are evaluated, with a focus on the ability of DTL to leverage knowledge transfer from source datasets to improve detection accuracy on sparse 5G-NIDD data. The findings underscore the importance of large-scale labeled datasets and adaptive security mechanisms in addressing evolving threats. This review concludes by highlighting the significant role of ML and DTL approaches in strengthening network defense and fostering proactive, robust security solutions for future networks. Full article

Unmanned Aerial Vehicles (UAVs) are widely used in defense applications. Multiple UAVs equipped with jamming sources can form a sparse array. The sparse array can be quickly deployed and achieve an extensive range of effective jamming. At present, the jamming power in the target area is mainly calculated through the superposition of node waveforms. The algorithm needs to sequentially calculate the angle of the target position relative to each node to obtain the corresponding gain and then calculate the path loss by the transmission model to obtain the total jamming power, which has high algorithmic complexity and needs to recount the power every time after adjusting the position of the node. An equivalent transmitted power estimation algorithm based on the pattern multiplication theorem is proposed in this paper, which regards the sparse array as a whole directional jammer. The power in the target area can be estimated according to the jammer gain, the equivalent transmitted power, and the jamming distance. In the calculation of jamming power, the proposed array-based algorithm reduces the complexity by 50% compared with the waveform superposition algorithm, and the estimation variance of the jamming power is less than 1.4%. Full article

The extensive interconnection and intelligent collaboration of multi-source heterogeneous devices in the industrial Internet environment have significantly improved the efficiency of industrial production and resource utilization. However, at the same time, the deployment characteristics of open-network architecture and the promotion of the concept of deep integration of OT/IT have led to an exponential growth of attacks on the industrial Internet. At present, most of the detection methods for industrial internet attacks use deep learning. However, due to the black-box characteristics caused by the complex structure of deep learning models, the explainability of industrial internet detection results generated based on deep learning is low. Therefore, we proposed an industrial internet intrusion response method xIIRS based on explainable deep learning. Firstly, an explanation method was improved to enhance the explanation by approximating and sampling the historical input and calculating the dynamic weighting for the sparse group lasso based on the evaluation criteria for the importance of features between and within feature groups. Then, we determined the defense rule scope based on the obtained explanation results and generated more fine-grained defense rules to implement intrusion response in combination with security constraints. The proposed method was experimented on two public datasets, TON_IoT and Gas Pipeline. The experimental results show that the explanation effect of xIIRS is better than the baseline method while achieving an average malicious traffic blocking rate of about 95% and an average normal traffic passing rate of about 99%. Full article

Detecting targets in remote sensing imagery, particularly when identifying sparsely distributed materials, is crucial for applications such as defense, mineral exploration, agriculture, and environmental monitoring. The effectiveness of detection and the precision of the results are influenced by several factors, including sensor configurations, platform properties, interactions between targets and their background, and the spectral contrast of the targets. Environmental factors, such as atmospheric conditions, also play a significant role. Conventionally, target detection in remote sensing has relied on statistical methods that typically assume a linear process for image formation. However, to enhance detection performance, it is critical to account for the geometric and spectral variabilities across multiple imaging platforms. In this research, we conducted a comprehensive target detection experiment using a unique benchmark multi-platform hyperspectral dataset, where man-made targets were deployed on various surface backgrounds. Data were collected using a hand-held spectroradiometer, UAV-mounted hyperspectral sensors, and airborne platforms, all within a half-hour time window. Multi-spectral space-based sensors (i.e., Worldview and Landsat) also flew over the scene and collected data. The experiment took place on 23 July 2021, at the Rochester Institute of Technology’s Tait Preserve in Penfield, NY, USA. We validated the detection outcomes through receiver operating characteristic (ROC) curves and spectral similarity metrics across various detection algorithms and imaging platforms. This multi-platform analysis provides critical insights into the challenges of hyperspectral target detection in complex, real-world landscapes, demonstrating the influence of platform variability on detection performance and the necessity for robust algorithmic approaches in multi-source data integration. Full article

Nematocysts, specialized stinging cells in cnidarians, play a crucial role in both defense and prey capture, containing venomous, coiled tubes within a capsule. While box jellyfish are recognized as a medical threat, information on the nematocysts of species like Gershwinia thailandensis and Morbakka sp. from Thai waters remains sparse. This study explores the types and morphology of nematocysts found in the tentacles of these species using light and scanning electron microscopy. We identified three nematocyst types: club-shaped microbasic p-mastigophores, oval isorhizas, and oval microbasic p-rhopaloids. Notably, significant differences in capsule sizes were observed, especially in the microbasic p-mastigophores and isorhizas. The discharge tubules tapered from the proximal to the distal ends, featuring arrow-shaped spines in a helical pattern. A distinct lancet structure was present in both microbasic p-mastigophores and p-rhopaloids. These findings suggest that variations in nematocyst size and morphology may be linked to evolutionary adaptations, functional roles, and venom toxicity. Further research into venom discharge mechanisms could offer valuable insights into the ecological and medical importance of these cnidarians. Full article

Remote Sensing (RS) and Geographic Information Science (GIS) techniques are powerful tools for spatial data collection, analysis, management, and digitization within cultural heritage frameworks. Despite their capabilities, challenges remain in automating data semantic classification for conservation purposes. To address this, leveraging airborne Light Detection And Ranging (LiDAR) point clouds, complex spatial analyses, and automated data structuring is crucial for supporting heritage preservation and knowledge processes. In this context, the present contribution investigates the latest Artificial Intelligence (AI) technologies for automating existing LiDAR data structuring, focusing on the case study of Sardinia coastlines. Moreover, the study preliminary addresses automation challenges in the perspective of historical defensive landscapes mapping. Since historical defensive architectures and landscapes are characterized by several challenging complexities—including their association with dark periods in recent history and chronological stratification—their digitization and preservation are highly multidisciplinary issues. This research aims to improve data structuring automation in these large heritage contexts with a multiscale approach by applying Machine Learning (ML) techniques to low-scale 3D Airborne Laser Scanning (ALS) point clouds. The study thus develops a predictive Deep Learning Model (DLM) for the semantic segmentation of sparse point clouds (<10 pts/m²), adaptable to large landscape heritage contexts and heterogeneous data scales. Additionally, a preliminary investigation into object-detection methods has been conducted to map specific fortification artifacts efficiently. Full article

The Hypersonic Glide Vehicle (HGV) has become a focal point in military competitions among nations. Predicting the real-time trajectory of an HGV is of significant importance for aerospace defense interception and assessing enemy combat intentions. Existing prediction methods are limited by the need for large data samples and poor general applicability. To address these challenges, this paper presents a novel trajectory forecasting approach based on the Sparse Association Structure Model (SASM). The SASM can uncover the relationship among known data, transfer associative relationships to unknown data, and improve the generalization of the model. Firstly, a trajectory database is established for different maneuvering modes based on the six-degree-of-freedom motion equations and models of attack and bank angles of the HGV. Subsequently, three trajectory parameters are selected as prediction variables according to the maneuvering characteristics of the HGV. A parameters prediction model based on the SASM is then constructed to predict trajectory parameters. The SASM model demonstrates high accuracy and generalization in forecasting the trajectories of three different HGV types. Experimental results show a 50.35% reduction in prediction error and a 48.7% decrease in average processing time compared to the LSTM model, highlighting the effectiveness of the proposed method for real-time trajectory forecasting. Full article

This comprehensive review investigates the pivotal role of reactive oxygen species (ROS) in cataract formation and evaluates the potential of antioxidant therapies in mitigating this ocular condition. By elucidating the mechanisms of oxidative stress, the article examines how ROS contribute to the deterioration of lens proteins and lipids, leading to the characteristic aggregation, cross-linking, and light scattering observed in cataracts. The review provides a thorough assessment of various antioxidant strategies aimed at preventing and managing cataracts, such as dietary antioxidants (i.e., vitamins C and E, lutein, and zeaxanthin), as well as pharmacological agents with antioxidative properties. Furthermore, the article explores innovative therapeutic approaches, including gene therapy and nanotechnology-based delivery systems, designed to bolster antioxidant defenses in ocular tissues. Concluding with a critical analysis of current research, the review offers evidence-based recommendations for optimizing antioxidant therapies. The current literature on the use of antioxidant therapies to prevent cataract formation is sparse. There is a lack of evidence-based conclusions; further clinical studies are needed to endorse the use of antioxidant strategies in patients to prevent cataractogenesis. However, personalized treatment plans considering individual patient factors and disease stages can be applied. This article serves as a valuable resource, providing insights into the potential of antioxidants to alleviate the burden of cataracts. Full article

LiDAR sensors have been shown to generate data with various common corruptions, which seriously affect their applications in 3D vision tasks, particularly object detection. At the same time, it has been demonstrated that traditional defense strategies, including adversarial training, are prone to suffering from gradient confusion during training. Moreover, they can only improve their robustness against specific types of data corruption. In this work, we propose LiDARPure, which leverages the powerful generation ability of diffusion models to purify corruption in the LiDAR scene data. By dividing the entire scene into voxels to facilitate the processes of diffusion and reverse diffusion, LiDARPure overcomes challenges induced from adversarial training, such as sparse point clouds in large-scale LiDAR data and gradient confusion. In addition, we utilize the latent geometric features of a scene as a condition to assist the generation of diffusion models. Detailed experiments show that LiDARPure can effectively purify 19 common types of LiDAR data corruption. Further evaluation results demonstrate that it can improve the average precision of 3D object detectors to an extent of 20% in the face of data corruption, much higher than existing defence strategies. Full article

Coral reefs are effective natural flood barriers that protect adjacent coastal communities. As the need to adapt to rising sea levels, storms, and environmental changes increases, reef restoration may be one of the best tools available to mitigate coastal flooding along tropical coastlines, now and in the future. Reefs act as a barrier to incoming short-wave energy but can amplify low-frequency infragravity waves that, in turn, drive coastal flooding along low-lying tropical coastlines. Here, we investigate whether the spacing of reef restoration elements can be optimized to maximize infragravity wave energy dissipation while minimizing the number of elements—a key factor in the cost of a restoration project. With this goal, we model the hydrodynamics of infragravity wave dissipation over a coral restoration or artificial reef, represented by a canopy of idealized hemispherical roughness elements, using a three-dimensional Navier–Stokes equations solver (OpenFOAM). The results demonstrate that denser canopies of restoration elements produce greater wave dissipation under larger waves with longer periods. Wave dissipation is also frequency-dependent: dense canopies remove wave energy at the predominant wave frequency, whereas sparse canopies remove energy at higher frequencies, and hence are less efficient. We also identify an inflection point in the canopy density–energy dissipation curve that balances optimal energy losses with a minimum number of canopy elements. Through this work, we show that there are an ideal number of restoration elements per across-shore meter of coral reef flat that can be installed to dissipate infragravity wave energy for given incident heights and periods. These results have implications for designing coral reef restoration projects on reef flats that are effective both from a coastal defense and costing standpoint. Full article