Search Results (63)

Leucoptera malifoliella has become a severe leaf-mining pest in Chinese apple orchards, especially under expanding organic and green cultivation practices, with effective management hindered by insufficient contemporary ecological data. To fill this gap, this 2023–2025 study conducted in Shijiazhuang, Hebei, combined field monitoring, morphological analysis, flight mill assays, and parasitoid release trials to clarify the moth’s phenology, develop rapid pupal sexing methods, quantify adult flight capacity, and assess Trichogramma dendrolimi biocontrol potential. The results showed five annual generations (overwintering as pupae), peak damage in July–August, and marked generational overlap. A reliable pupal sexing method was established via genital opening morphology. Adult flight peaked at 3 days post-emergence (max distance: 1.223 km), with no sexual dimorphism. Timely T. dendrolimi releases boosted parasitism rates, achieving 23.4–49.6% control efficacy during peak damage, with the parasitism rate positively correlated with efficacy. This study confirms the moth’s potential for generational increase under climate warming and medium-distance dispersal capacity, validating Trichogramma’s utility and laying a scientific foundation for precise, regionally coordinated ecological management. Full article

In recent years, the rapid development of commercial satellite projects, such as low-Earth orbit (LEO) communication and remote sensing constellations, has driven the satellite industry toward low-cost, rapid development, and large-scale deployment. Commercial off-the-shelf (COTS) components have been widely adopted across various commercial satellite platforms due to their advantages of low cost, high performance, and plug-and-play availability. However, the space environment is complex and hostile. COTS components were not originally designed for such conditions, and they often lack systematically flight-verified protective frameworks, making their reliability issues a core bottleneck limiting their extensive application in critical missions. This paper focuses on COTS solid-state drives (SSDs) onboard the Jilin-1 KF satellite and presents a full-lifecycle reliability practice covering component selection, system design, on-orbit operation, and failure feedback. The core contribution lies in proposing a full-lifecycle methodology that integrates proactive design—including multi-module redundancy architecture and targeted environmental stress screening—with on-orbit data monitoring and failure cause analysis. Through fault tree analysis, on-orbit data mining, and statistical analysis, it was found that SSD failures show a significant correlation with high-energy particle radiation in the South Atlantic Anomaly region. Building on this key spatial correlation, the on-orbit failure mode was successfully reproduced via proton irradiation experiments, confirming the mechanism of radiation-induced SSD damage and providing a basis for subsequent model development and management decisions. The study demonstrates that although individual COTS SSDs exhibit a certain failure rate, reasonable design, protection, and testing can enhance the on-orbit survivability of storage systems using COTS components. More broadly, by providing a validated closed-loop paradigm—encompassing design, flight verification and feedback, and iterative improvement—we enable the reliable use of COTS components in future cost-sensitive, high-performance satellite missions, adopting system-level solutions to balance cost and reliability without being confined to expensive radiation-hardened products. Full article

Early detection of faults in artificial satellites is crucial for the success of missions, but it is hindered by the scarcity of data on faults and the limitations of traditional monitoring methods. As an alternative, technological trends such as data mining have emerged. This study presents a literature review to provide an in-depth examination of the landscape of data mining applications for early fault detection in satellites. Following the PRISMA protocol, the available scientific corpus from seven scientific databases was reviewed, and 52 primary studies were selected from an initial set of 2726 records published between 2011 and 2024. The results indicate that this is a rapidly expanding field, with an annual growth rate of 35.72%, characterized by a significant geopolitical concentration of research and funding led by China. From a methodological point of view, unsupervised approaches (~40%) predominate, a response to the lack of labeled in-flight data. However, supervised and hybrid models demonstrate superior performance, achieving F1 scores above 97% when selected or simulated data are available. A misalignment was identified in the domain, although research clearly favors the EPS due to the availability of data. Operational statistics, however, confirm that the ADCS system is the primary cause of mission failure. It is essential to note that the limited availability of public datasets and models, with less than 15% of studies providing access, is the main obstacle to reproducibility and progress. The primary conclusion of this work is that the field is expanding, and all stakeholders must contribute to its continued growth. Key actions include establishing public benchmarks that enable transparent evaluation, exploring physics-based models that account for uncertainty to address data scarcity, and concerted efforts to bridge the transfer gap from academic satellite operations to the real world. Full article

Background/Objectives: Changes in sclerostin expression regulated by SOST in osteocytes during spaceflight may be associated with bone loss; however, the underlying mechanisms remain unclear. The aim of this study was to clarify the relationship between SOST expression and bone loss by identifying the gene expression differences between osteocytes with high and low SOST expressions. Methods: We used the NASA GeneLab Database OSD-324, which is a microarray of data about the Ocy454 osteocytic cell line cultured for 2, 4, and 6 days during spaceflight, and the GSE102958 microarray in the Gene Expression Omnibus. We also analyzed the characteristics of SOST gene expression in osteocytes during spaceflight using merged datasets. Results: The findings of Gene Set Enrichment Analysis (GSEA) revealed that five gene sets related with H3K27me3 significantly upregulated with NES > 2.0 during spaceflight compared with ground controls. We also found 77 and 617 differentially expressed genes (DEGs) in flight 6d vs. low and high SOST expression, respectively. We used the transcriptional regulatory relationships unraveled by the sentence-based text-mining (TRRUST) database to determine the most significant upstream transcription factor (TF) of genes downregulated in osteocytes during spaceflight compared with those expressing abundant SOST. We detected that TF Sp7 is the most significant, with FDR < 0.01. Moreover, the GSEA findings indicated that the hypoxic pathway is prolonged in osteocytes during spaceflight compared to those at ground level. Conclusions: The gene expression profiles of osteocytes during spaceflight and in comparatively immature osteocytes with low SOST expression were similar. Furthermore, early osteocyte maturation inhibited by downregulated Sp7 during spaceflight extended the hypoxic response. Full article

In the unmanned aerial vehicles (UAVs) flight control and navigation guidance system, trajectory prediction serves as a critical foundational component, with its accuracy and reliability directly influencing the system performance of the UAVs. However, existing research has predominantly focused on optimizing algorithm efficiency, failing to fully consider the impact of the UAV’s flight status on its trajectory. This has resulted in significant discrepancies between predicted results and actual trajectories in complex scenarios. Therefore, this paper proposes a trajectory prediction algorithm that integrates the UAVs’ behavioral intentions. Firstly, a behavioral intention recognition model is constructed using the Support Vector Machine (SVM) to accurately discriminate the UAV’s motion patterns and output the probability distribution of its future actions, thereby integrating semantic-level intention information into the prediction process. Secondly, the Bidirectional Gated Recurrent Unit (Bi-GRU) is employed to mine the spatial-temporal correlation features from trajectory data. Additionally, an attention mechanism is introduced to capture key information of sequence, enhancing the model’s ability to represent complex motion trends. The results of simulation experiments demonstrate that this algorithm exhibits significant advantages in terms of trajectory prediction accuracy and scene adaptability, providing more practical technical support for intelligent navigation and safety control of UAVs. Full article

Unmanned aerial vehicles (UAVs) have increasingly proven to be flexible tools for mapping mine terrain, offering expedient and precise data compared to alternatives. Photogrammetric outputs are particularly beneficial in open pit operations and waste dump areas, since they enable cost-effective and reproducible digital terrain models. Meanwhile, UAV-based LiDAR has proven invaluable in situations where uniform ground surfaces, dense vegetation, or steep slopes challenge purely photogrammetric solutions. Recent advances in machine learning and deep learning have further enhanced the capacity to distinguish critical features, such as vegetation and fractured rock surfaces, thereby reducing the likelihood of accidents and ecological damage. Nevertheless, scientific gaps remain to be researched. Standardization around flight practices, sensor selection, and data verification persists as elusive, and most mining sites still rely on limited, multi-temporal surveys that may not capture sudden changes in slope conditions. Complexity lies in devising strategies for rehabilitated dumps, where post-mining restoration efforts involve vegetation regrowth, erosion mitigation, and altered land use. Through expanded sensor integration and refined automated analysis, approaches could shift from information gathering to ongoing hazard assessment and environmental surveillance. This evolution would improve both safety and environmental stewardship, reflecting the emerging role of UAVs in advancing a more sustainable future for mining. Full article

The correlation between aerodynamic data obtained from ground and flight tests is crucial in developing aerospace vehicles. This paper proposes methods for modelling this correlation that combine feature extraction and symbolic regression. The neighborhood component analysis (NCA) method is utilized to extract features from the high-dimensional state space and then symbolic regression (SR) is applied to find the concise optimal expression. First, a simulation example of the NASA Twin Otter aircraft is used to validate the NCA and the SR tool developed by the research team in modeling the aerodynamic coefficient deviation between ground and flight due to an unpredictable inflight icing failure. Then, the method and tool are applied to real flight tests of two types of aerospace vehicles with different configurations. The final optimized mathematical models show that the two vehicles’ pitching moment coefficient deviations are related to the angle of attack (AOA) only. The mathematical model built using NCA and the SR tool demonstrates higher fitting accuracy and better generalization performance for flight test data than other typical data-driven methods. The mathematical model delivers a multi-fold enhancement in fitting accuracy over data-driven methods for all fight cases. For UAV flight test data, the average root mean square error (RMSE) of the mathematical model demonstrates a maximum improvement of 37% in accuracy compared to three data-driven methods. For XRLV flight test data, the prediction accuracy of the mathematical model shows an enhancement exceeding 80% relative to Gaussian kernel SVM and Gaussian process data-driven models. The research verifies the feasibility and effectiveness of the data feature extraction combined with the symbolic regression method in mining the correlation law between ground and flight deviations of aerodynamic characteristics. This study provides valuable insight for modeling problems with finite data samples and explicit physical meanings. Full article

With the increasing mining depth, the stability of open-pit mine slopes has become an increasingly important concern. This study focuses on an open-pit mine in Southwest China and utilizes unmanned aerial vehicle (UAV) technology to gather data from these high and steep slopes. First, high-precision digital surface models and digital orthophoto data are collected using UAV terrain-following flight technology. However, two major challenges arise when applying geographic information systems (GISs) to this issue. The first challenge is that the extreme steepness of the slopes causes overlapping lithological layers at the same location, which GISs cannot resolve. The second challenge is that GISs cannot assess the influence of faults on landslides by calculating three-dimensional spatial distances. To overcome these issues, this study proposes the construction of a detailed 3D geological model for the entire mining area. This model allows for a more precise analysis of the lithology and fault spatial distances. A GIS is then applied to analyze the slope, curvature, and slope direction. Multi-source data fusion is employed to link spatial coordinates and create a dataset for further analysis. Five machine learning models for landslide prediction are compared using this dataset. Based on these comparisons, a high-precision random forest and slope boosting coupled method is developed to enhance the landslide prediction accuracy. Finally, a numerical simulation of a regional focus area is conducted, simulating the excavation process of an open-pit mine and analyzing the timing, location, and state of potential landslides. The results indicate that combining machine learning and multi-source data fusion provides a highly accurate, efficient, and straightforward method for landslide prediction in the high and steep slopes of open-pit mines. Full article

Airline customers will often complain to the relevant authorities if they encounter an unpleasant flight experience. The specific complaint information can directly reflect the various service problems encountered, so conducting in-depth research on public air transport passenger complaints can reveal important details for improving service. Therefore, by analyzing the passenger complaint data of relevant civil aviation departments in China, we propose a method for identifying key topics of passenger complaints based on text mining. We organically integrate sentiment analysis, topic modeling and association rule mining. A new complaint text analysis framework is constructed, which provides new perspectives and ideas for complaint text analysis and related application fields. First, we calculate the sentiment orientation of the complaint text based on the sentiment dictionary method and filter complaint texts with strong negative sentiment. Then, we compare the two topic modeling methods of LDA (Latent Dirichlet Allocation) and LSA (Latent Semantic Analysis). Finally, we select the better LDA method to extract the main topics hidden in the passenger complaint text with high negative emotional intensity. We use the Apriori algorithm to mine the association rules between the complaint topic words and the service problem classification labels on the complaint text. We use the FP-growth algorithm to mine the association rules between the complaint subject words and the service problem classification labels on the complaint text. By comparing the Apriori algorithm with the FP-growth algorithm, the results of mining the support, confidence and promotion of the association rules show that the Apriori algorithm is more efficient. Finally, we analyze the causes of specific service problems and suggest improvement strategies for airlines and airports. Full article

Variations in vegetation indices derived from multispectral images and digital terrain models from satellite imagery have been successfully used for reclamation and hazard management in former mining areas. However, low spatial resolution and the lack of sufficiently detailed information on surface morphology have restricted such studies to large sites. This study investigates the application of small, unmanned aerial vehicles (UAVs) equipped with multispectral sensors for land cover classification and vegetation monitoring. The application of UAVs bridges the gap between large-scale satellite remote sensing techniques and terrestrial surveys. Photogrammetric terrain models and orthoimages (RGB and multispectral) obtained from repeated mapping flights between November 2023 and May 2024 were combined with an ALS-based reference terrain model for object-based image classification. The collected data enabled differentiation between natural forests and areas affected by former mining activities, as well as the identification of variations in vegetation density and growth rates on former mining areas. The results confirm that small UAVs provide a versatile and efficient platform for classifying and monitoring mining areas and forested landslides. Full article

The paper presents a method for modeling and estimating the orientation of a launch cart in the magnetic suspension system of an innovative UAV catapult. The catapult consists of stationary tracks lined with neodymium magnets, generating a trough-shaped magnetic field. The cart levitates above the tracks, supported by four containers housing high-temperature YBCO superconductors cooled with liquid nitrogen. The Meissner effect, characterized by the expulsion of magnetic fields from superconductors, ensures stable hovering of the cart. The main research challenge was to determine the cart’s orientation relative to the tracks, with a focus on the pitch angle, which is critical for collision-free operation and system efficiency. A dedicated measurement stand equipped with Hall sensors and Time-of-Flight (ToF) distance sensors was developed. Hall sensors mounted on the cart’s supports captured magnetic field data at specific points. To model the tracks, the CRISP-DM (Cross Industry Standard Process for Data Mining) methodology was employed—a structured framework consisting of six stages; from problem understanding and data preparation to model evaluation and deployment. This approach guided the analysis of data-driven models and facilitated accurate pitch angle estimation. Evaluation metrics, including mean squared error (MSE), were used to identify and select the optimal models. The final model achieved an MSE of 0.084°, demonstrating its effectiveness for precise orientation control. Full article

Feature selection (FS) constitutes a critical stage within the realms of machine learning and data mining, with the objective of eliminating irrelevant features while guaranteeing model accuracy. Nevertheless, in datasets featuring a multitude of features, choosing the optimal feature poses a significant challenge. This study presents an enhanced Sand Cat Swarm Optimization algorithm (MSCSO) to improve the feature selection process, augmenting the algorithm’s global search capacity and convergence rate via multiple innovative strategies. Specifically, this study devised logistic chaotic mapping and lens imaging reverse learning approaches for population initialization to enhance population diversity; balanced global exploration and local development capabilities through nonlinear parameter processing; and introduced a Weibull flight strategy and triangular parade strategy to optimize individual position updates. Additionally, the Gaussian–Cauchy mutation strategy was employed to improve the algorithm’s ability to overcome local optima. The experimental results demonstrate that MSCSO performs well on 65.2% of the test functions in the CEC2005 benchmark test; on the 15 datasets of UCI, MSCSO achieved the best average fitness in 93.3% of the datasets and achieved the fewest feature selections in 86.7% of the datasets while attaining the best average accuracy across 100% of the datasets, significantly outperforming other comparative algorithms. Full article

Feature selection (FS) is a classic and challenging optimization task in most machine learning and data mining projects. Recently, researchers have attempted to develop more effective methods by using metaheuristic methods in FS. To increase population diversity and further improve the effectiveness of the beluga whale optimization (BWO) algorithm, in this paper, we propose a multi-strategies improved BWO (MSBWO), which incorporates improved circle mapping and dynamic opposition-based learning (ICMDOBL) population initialization as well as elite pool (EP), step-adaptive Lévy flight and spiral updating position (SLFSUP), and golden sine algorithm (Gold-SA) strategies. Among them, ICMDOBL contributes to increasing the diversity during the search process and reducing the risk of falling into local optima. The EP technique also enhances the algorithm′s ability to escape from local optima. The SLFSUP, which is distinguished from the original BWO, aims to increase the rigor and accuracy of the development of local spaces. Gold-SA is introduced to improve the quality of the solutions. The hybrid performance of MSBWO was evaluated comprehensively on IEEE CEC2005 test functions, including a qualitative analysis and comparisons with other conventional methods as well as state-of-the-art (SOTA) metaheuristic approaches that were introduced in 2024. The results demonstrate that MSBWO is superior to other algorithms in terms of accuracy and maintains a better balance between exploration and exploitation. Moreover, according to the proposed continuous MSBWO, the binary MSBWO variant (BMSBWO) and other binary optimizers obtained by the mapping function were evaluated on ten UCI datasets with a random forest (RF) classifier. Consequently, BMSBWO has proven very competitive in terms of classification precision and feature reduction. Full article

The mining industry faces significant challenges in production costs, environmental protection, and worker safety, necessitating the development of autonomous systems. This study presents the design and implementation of a robust rock centroid localization system for mining robotic applications, particularly rock-breaking hammers. The system comprises three phases: assembly, data acquisition, and data processing. Environmental sensing was accomplished using a Basler Blaze 101 three-dimensional (3D) Time-of-Flight (ToF) camera. The data processing phase incorporated advanced algorithms, including Bird’s-Eye View (BEV) image conversion and You Only Look Once (YOLO) v8x-Seg instance segmentation. The system’s performance was evaluated using a comprehensive dataset of 627 point clouds, including samples from real mining environments. The system achieved efficient processing times of approximately 5 s. Segmentation accuracy was evaluated using the Intersection over Union (IoU), reaching 95.10%. Localization precision was measured by the Euclidean distance in the XY plane (ED_XY), achieving 0.0128 m. The normalized error (e_norm) on the X and Y axes did not exceed 2.3%. Additionally, the system demonstrated high reliability with R² values close to 1 for the X and Y axes, and maintained performance under various lighting conditions and in the presence of suspended particles. The Mean Absolute Error (MAE) in the Z axis was 0.0333 m, addressing challenges in depth estimation. A sensitivity analysis was conducted to assess the model’s robustness, revealing consistent performance across brightness and contrast variations, with an IoU ranging from 92.88% to 96.10%, while showing greater sensitivity to rotations. Full article

Obstacle avoidance in UAV swarms is crucial for ensuring the stability and safety of cluster flights. However, traditional methods of swarm obstacle avoidance often fail to meet the requirements of frequent spatiotemporal dynamic changes in UAV swarms, especially in complex environments such as forest firefighting, mine monitoring, and earthquake disaster relief. Consequently, the trained obstacle avoidance strategy differs from the expected or optimal obstacle avoidance scheme, leading to decision bias. To solve this problem, this paper proposes a method of UAV swarm obstacle avoidance decision making based on the end-edge-cloud collaboration model. In this method, the UAV swarm generates training data through environmental interaction. Sparse rewards are converted into dense rewards, considering the complex environmental state information and limited resources, and the actions of the UAVs are evaluated according to the reward values, to accurately assess the advantages and disadvantages of each agent’s actions. Finally, the training data and evaluation signals are utilized to optimize the parameters of the neural network through strategy-updating operations, aiming to improve the decision-making strategy. The experimental results demonstrate that the UAV swarm obstacle avoidance method proposed in this paper exhibits high obstacle avoidance efficiency, swarm stability, and completeness compared to other obstacle avoidance methods. Full article