Search Results (4,585)

In recent years, with the continuous development of information technology, network security issues have become increasingly prominent. Intrusion detection has garnered significant attention in the field of network security protection due to its ability to detect anomalies in a timely manner. However, existing intrusion detection methods often fail to effectively capture spatiotemporal correlations in traffic and struggle with imbalanced, high-dimensional feature spaces—problems that become even more pronounced under complex network environments—ultimately leading to low identification accuracy and high false-positive rates. To address these challenges, this paper proposes a spatiotemporal correlation-based intrusion detection method that utilizes feature space transformation and Euclidean distance. Specifically, the method first considers the relationship between the characteristics of different operating systems and attack behaviors through feature space transformation and integration. Then, it constructs a graph structure between samples using Euclidean distance and captures the spatiotemporal correlations between samples by combining graph convolutional networks with bidirectional gated recurrent unit networks. Through this design, the model can deeply mine the spatial and temporal features of network traffic, thereby improving classification accuracy and detection efficiency for network attacks. Experimental results show that the proposed model significantly outperforms existing intrusion detection approaches across multiple evaluation metrics, including accuracy, weighted precision, weighted recall, and weighted F1 score. Full article

Virtual commands serve as the essential framework for establishing interaction between the virtual pilot and the MCP in autopilot scenarios. Conventional proportional-integral-derivative (PID) controllers are insufficient to ensure accurate flight trajectories due to system hysteresis. To overcome this limitation, a quaternary correlation prediction compensation PID (QCPC-PID) approach is introduced for computing virtual heading commands in autopilot tasks. The method integrates multi-feature statistics, entropy-based predictive compensation, and quaternary correlations. First, flight trajectory error statistics are dynamically calculated using signed error distances to assess deviation levels. Second, a predictive structure based on information entropy is applied to enhance PID compensation. Third, quaternary correlation dependence is established to generate virtual heading commands. The findings confirm the effectiveness of the method in improving flight convergence. The incorporation of predictive structures and quaternary correlations is critical for achieving predictive compensation during PID tuning, thereby reducing flight trajectory deviations. The quaternary correlation prediction compensation method ensures superior performance of PID control in modeling heading adjustment behavior under autopilot conditions. Full article

The conical pick is an essential component of roadheaders used for cutting rock. During the rock-breaking process, these picks interact with the rock, resulting in self-rotation, which enhances the wear uniformity of conical picks, thereby prolonging their service life. Since the phenomenon of self-rotation is generated passively by random contact forces with the rock surface, it is challenging to quantitatively measure the extent of self-rotatory speed. In order to investigate the correlation between the self-rotatory speed of conical picks and wear, this article establishes various self-rotatory speeds for vertical rock-breaking wear experiments involving conical picks. It analyzes the relationship between quantitative parameters, such as the equivalent stress and wear, through simulation. The results of the study indicate that the optimal self-rotatory speed of the conical pick is 16 rpm when it is rotated vertically to break the rock, resulting in minimal wear. When the equivalent stress and Mohr–Coulomb safety factor are optimized, it is essential to consider the changes in normal force and the variation in the area affected by the safety factor. This leads to an increase in wear as the cutting distance increases, indicating that a higher self-rotatory speed does not necessarily improve the wear performance of conical picks. Full article

Background/Objective: Precise preprocedural planning is essential for the safety and efficacy of structural heart interventions. Conventional imaging modalities, while informative, do not allow for direct and accurate visualization, limiting procedural predictability. We aimed to develop and validate a high-resolution micro-computed tomography (microCT)-based reverse modeling workflow that integrates digital reconstructions of metallic cardiac devices into patient imaging datasets, enabling accurate, patient-specific virtual simulation for procedural planning. Methods: Clinical-grade transcatheter heart valves, septal defect occluders, patent ductus arteriosus occluders, left atrial appendage closure devices, and coronary stents were scanned using microCT (36.9 μm resolution). Agreement was assessed by intra-class correlation coefficients (ICC) and Bland–Altman analyses. Device geometries were reconstructed into 3D stereolithography files and virtually implanted within multislice CT datasets using dedicated software. Results: Devices were successfully reverse-modeled with high geometric fidelity, showing negligible dimensional deviations from manufacturer specifications (mean ΔDistance range: −0.20 to +0.20 mm). Simulated measurements demonstrated excellent concordance with postprocedural imaging (ICC 0.90–0.96). The workflow accurately predicted clinically relevant parameters such as valve-to-coronary distances and implantation depths. Notably, preprocedural simulation identified a case at high risk of coronary obstruction, confirmed clinically and managed successfully. Conclusions: The microCT-based reverse modeling workflow offers a rapid, reproducible, and clinically relevant method for patient-specific simulation in structural heart interventions. By preserving anatomical fidelity and providing detailed device–tissue spatial visualization, this approach enhances preprocedural planning accuracy, risk stratification, and procedural safety. Its resource-efficient digital nature facilitates broad adoption and iterative simulation. Full article

Background/Objectives: Combining orthognathic surgery with orthodontic therapy is a crucial approach for correcting severe dentofacial deformities that orthodontics alone cannot address. This study aimed to quantify skeletal, dental, and soft tissue alterations following orthognathic surgery and to assess correlations among cephalometric parameters to improve understanding of treatment outcomes. Methods: A prospective observational study was conducted on 25 Romanian patients (44% female and 56% male; median age, 28 years) who underwent orthognathic surgery. Standardized pre- and postoperative lateral cephalometric radiographs were analyzed using WebCeph 2.0.0 software. The evaluated parameters included the SNA angle (sella–nasion–point A, indicating maxillary position), SNB angle (sella–nasion–point B, indicating mandibular position), ANB angle (maxillo-mandibular relationship), Pog-N-Perp (distance from pogonion to the nasion-perpendicular line), U1–NA° (inclination of the upper incisor relative to the maxillary base), L1–NB° (inclination of the lower incisor relative to the mandibular base), nasolabial angle, and facial convexity. Statistical analyses included paired t-tests and correlation analysis. Results: Significant anterior repositioning of the maxilla was observed, with SNA increasing from 83.6° to 86.3° (p = 0.019). The SNB angle remained stable, while ANB increased toward normalized sagittal relationships (0.9° to 3.0°, p = 0.060). Soft tissue analysis revealed a slight increase in the nasolabial angle (102° to 105°) and improved facial convexity. Strong correlations were found between skeletal parameters (SNB and ANB, r = −0.852, p < 0.001) and between skeletal and dental variables (ANB and L1–NB°, r = 0.652, p < 0.001), confirming coordinated skeletal–soft tissue adaptation. Conclusions: Orthognathic surgery significantly enhances skeletal balance and facial harmony, particularly through maxillary advancement. The integration of virtual surgical planning and interdisciplinary collaboration improves accuracy, predictability, and patient-centered outcomes in surgical orthodontics. Full article

Frequent failures in wind turbines underscore the critical need for accurate and efficient online monitoring and early warning systems to detect abnormal conditions. Given the complexity of monitoring numerous components individually, subsystem-level monitoring emerges as a practical and effective alternative. Among all subsystems, the gearbox is particularly critical due to its high failure rate and prolonged downtime. However, achieving both efficiency and accuracy in gearbox condition monitoring remains a significant challenge. To tackle this issue, we present a novel adaptive condition monitoring method specifically for wind turbine gearbox. The approach begins with adaptive feature selection based on correlation analysis, through which a quantitative indicator is defined. With the utilization the selected features, graph-based data representations are constructed, and a self-supervised contrastive residual graph neural network is developed for effective data mining. For online monitoring, a health index is derived using distance metrics in a multidimensional feature space, and statistical process control is employed to determine failure thresholds. This framework enables real-time condition tracking and early warning of potential faults. Validation using SCADA data and maintenance records from two wind farms demonstrates that the proposed method can issue early warnings of abnormalities 30 to 40 h in advance, with anomaly detection accuracy and F1 score both exceeding 90%. This highlights its effectiveness, practicality, and strong potential for real-world deployment in wind turbine monitoring applications. Full article

Ultra-high dimensional longitudinal data feature screening procedures are widely studied, but most require model assumptions. The screening performance of these methods may not be excellent if we specify an incorrect model. To resolve the above problem, a new model-free method is introduced where feature screening is performed by sample splitting and data aggregation. Distance correlation is used to measure the association at each time point separately, while longitudinal correlation is modeled by a specific cumulative distribution function to achieve efficiency. In addition, we extend this new method to handle situations where the predictors are correlated. Both methods possess excellent asymptotic properties and are capable of handling longitudinal data with unequal numbers of repeated measurements and unequal intervals between repeated measurement time points. Compared to other model-free methods, the two new methods are relatively insensitive to within-subject correlation, and they can help reduce the computational burden when applied to longitudinal data. Finally, we use some simulated and empirical examples to show that both new methods have better screening performance. Full article

In this study, we utilize Gaofen-2 satellite remote sensing images to optimize and enhance the extraction of feature information from rural compounds, addressing key challenges in high-resolution remote sensing analysis: traditional methods struggle to effectively capture long-distance spatial dependencies for scattered rural compounds. To this end, we implement the RS³Mamba+ deep learning model, which introduces the Mamba state space model (SSM) into its auxiliary branching—leveraging Mamba’s sequence modeling advantage to efficiently capture long-range spatial correlations of rural compounds, a critical capability for analyzing sparse rural buildings. This Mamba-assisted branch, combined with multi-directional selective scanning (SS2D) and the enhanced STEM network framework (replacing single 7 × 7 convolution with two-stage 3 × 3 convolutions to reduce information loss), works synergistically with a ResNet-based main branch for local feature extraction. We further introduce a multiscale attention feature fusion mechanism that optimizes feature extraction and fusion, enhances edge contour extraction accuracy in courtyards, and improves the recognition and differentiation of courtyards from regions with complex textures. The feature information of courtyard utilization status is finally extracted using empirical methods. A typical rural area in Weifang City, Shandong Province, is selected as the experimental sample area. Results show that the extraction accuracy reaches an average intersection over union (mIoU) of 79.64% and a Kappa coefficient of 0.7889, improving the F1 score by at least 8.12% and mIoU by 4.83% compared with models such as DeepLabv3+ and Transformer. The algorithm’s efficacy in mitigating false alarms triggered by shadows and intricate textures is particularly salient, underscoring its potential as a potent instrument for the extraction of rural vacancy rates. Full article

Acoustic emission (AE) is a passive non-destructive testing (NDT) method that allows for online monitoring of oil tank bottom corrosion without production shutdown. However, AE signals are susceptible to ambient noise interference, causing the AE inspection system to mistakenly identify noise as corrosion signals, which significantly reduces AE inspection performance. Therefore, it is important to distinguish between AE signals caused by corrosion and those caused by noise. To address this, an AE inspection platform for vertical atmospheric tank corrosion is established. Six common noise sources in field AE inspections, including mechanical vibration and friction, fluid and raining disturbance, external impacts, and oil leakage are simulated. The impacts of these noises on AE location events are analyzed. Variational mode decomposition (VMD) and dispersion entropy (DE) are used to extract multi-domain features of AE signals. An improved distance evaluation (IDE) algorithm is then introduced to obtain a highly correlated feature subset. A support vector machine (SVM) model optimized by the bald eagle search (BES) algorithm is proposed to identify different noise sources. Field experiments demonstrate that for mechanical friction, external impacts, and effective corrosion signals, the proposed method achieves identification accuracy of 92.95% and 94.00% in the training and test sets, respectively. This proves the reliability of the BES-SVM model, which uses multi-domain features for AE source identification in oil tank bottom corrosion inspections. Moreover, the impacts of the optimization algorithm, feature selection algorithm, and feature type on AE source identification are further investigated. Full article

In horizontal well technology, hydraulic fracturing has been established as an essential technique for enhancing hydrocarbon production. However, the complex architecture of fracture networks challenges conventional monitoring methods. Micro-seismic monitoring, recognized for its superior resolution and sensitivity, enables precise fracture morphology characterization. This study advances diagnostic capabilities through integrated field–laboratory investigations and multi-domain signal processing. Hydraulic fracturing experiments under varied geological conditions generated critical micro-seismic datasets, with quantitative analyses revealing asymmetric propagation patterns (total length 312 ± 15 m, east wing 117 m/west wing 194 m) forming a 13.37 × 10⁴ m³ stimulated reservoir volume. Spatial event distribution exhibited density disparities correlating with geophone offsets (west wing 3.8 events/m vs. east 1.2 events/m at 420–794 m distances). Advanced time–frequency analyses and inversion algorithms differentiated signal characteristics demonstrating logarithmic SNR (Signal-to-Noise Ratio)–magnitude relationships (SNR 0.49–4.82, R² = 0.87), with near-field events (<500 m) showing 68% reduced magnitude variance compared to far-field counterparts. Coupled numerical simulations confirmed stress field interactions where fracture trajectories deviated 5–15° from principal stress directions due to prior-stage stress shadows. Branch fracture networks identified in Stages 4/7/9/10 with orthogonal/oblique intersections (45–65° dip angles) enhanced stimulation reservoir volume (SRV) by 37–42% versus planar fractures. These geometric parameters—including height (20 ± 3 m), width (44 ± 5 m), spacing, and complexity—were quantitatively linked to micro-seismic response patterns. The developed diagnostic framework provides operational guidelines for optimizing fracture geometry control, demonstrating how heterogeneity-driven signal variations inform stimulation strategy adjustments to improve reservoir recovery and economic returns. Full article

The evaluation of bioavailable forms of heavy metals in zones of anthropogenic pollution is the basis of ecological risk assessment. The characterization of the consequences of the operation of the Pechenganikel smelting plant was carried out using AAS and two methods of soil bioavailable forms of heavy metal extraction (3% nitric acid and ammonium acetate buffer with pH 4.8) along three directions from the plant, corresponding to the wind prevalence. Buffer extraction provided more significant correlations between Ni, Co, Cu, Pb, and Zn, compared to nitric acid application, indicating a negative correlation between soil Cu, Co, and the distance from the plant, while no significant correlations were recorded for nitric acid extracts. A higher significant correlation number arose between soil elements in buffer extracts along the N-E direction than the S-W one. In the former direction, the number of the mentioned correlations decreased according to the following sequence: Zn (6) > Cu (5) > Se and Co (4) > Ni and Fe (3); in nitric acid extract, only significant correlations of Cu, Zn, and Se with Co and Ni were recorded. Biocrust formation was revealed only along the N-E direction, characterized by unexpected high Se concentrations and intensive correlation between Zn and all the elements extracted by the buffer. Biocrust accumulated high levels of all the elements tested and showed antioxidant activity and polyphenol content significantly correlated with soil organic matter. The biocrust mineral content demonstrated a complex relationship with soil Fe, Cu (buffer extract), and Se, as well as Co and Zn (nitric acid extract). Application of linear mixed-effects modelling and transfer factor analysis indicate that biocrusts may serve as effective bioindicators of both absolute metal contamination and its bioavailable fractions. The results indicate the expediency of using both methods of soil extraction for assessing the ecological risk and soil–biocrust relationships. Full article

Current InVEST habitat quality assessments rely heavily on subjective expert judgment for parameter specification, introducing substantial uncertainty and limiting their regional applicability. To address this gap, we developed an objective, statistically rigorous framework for parameter derivation by integrating Principal Component Analysis (PCA), Structural Equation Modeling (SEM), and spatial analysis, supported by high-resolution biotope mapping. The methodology was applied to Gochang-gun, South Korea, where nine threat factors were analyzed using empirical data from 6633 sampling points. PCA identified threat groupings, SEM quantified habitat–threat relationships for sensitivity derivation, and variogram analysis determined maximum influence distances, while 1:5000 scale biotope maps incorporating 14 ecological indicators replaced conventional land cover classifications. These empirically derived parameters were directly incorporated into the InVEST Habitat Quality model, replacing default or expert-based values. As a result, the biotope-based InVEST HQ implementation achieved exceptional performance (R² = 0.892) with crops emerging as the dominant threat factor (sensitivity = 1.000, weight = 34.1%). Compared to the land use/land cover (LULC)-based approach using conventional parameterization, the biotope–PCA–SEM model demonstrated higher predictive accuracy (AUC = 0.805 vs. 0.755), stronger correlations with independent conservation indicators (protected area correlation: 0.457 vs. 0.201), and clearer ecological gradients across UNESCO Biosphere Reserve zones. This framework eliminates subjective bias while maintaining regional specificity, establishing a transferable foundation for evidence-based conservation planning. By demonstrating substantial improvements over conventional parameterization, the study highlights the inadequacy of transferred parameters and provides an objective standard for advancing InVEST applications worldwide. Full article

Background: This study employed a markerless motion capture system to quantify the external game load of elite 3 × 3 basketball players and evaluated its association with game performance. Methods: Twenty-four female 3 × 3 basketball games from the 2024 Paris Olympic Games were analyzed, involving 32 players from eight national teams. A markerless motion capture system was used to collect six categories of external load metrics during games, and 22 types of technical statistics were gathered to determine performance. Collected data were standardized according to live game time (min⁻¹). Repeated-measures correlation analysis was applied to examine the relationships between external load and performance, while mixed-effects models were used to compare external load differences between better- and worse-performing groups (classified by Player Value). Results: The correlations between external load and performance indicators were trivial to small. Accelerations (ACC) were significantly associated with the greatest number of performance indicators (e.g., points, rebounds, 1-point made, key assists), while rebounds were significantly correlated with the largest number of external load metrics (e.g., total distance, low-intensity active distance, high-intensity active distance); however, all correlations remained at the small level (r = 0.16–0.24). No significant differences in external load were observed between players of differing performance groups (p > 0.05). Conclusions: In elite 3 × 3 basketball, external load reflects players’ involvement and effort rather than serving as a primary determinant of game performance. This study provides new empirical evidence on the characteristics of 3 × 3 basketball, suggesting that coaches and strength and conditioning practitioners should adopt a comprehensive perspective when evaluating performance, with external load being more suitable for training regulation and fatigue monitoring. Full article

Background/Objectives: This study aimed to objectively assess fatigue levels using facial expressions. Methods: This study included 25 female nurses aged between 30 and 50 years. We compared their subjective and objective fatigue levels after a night shift, when accumulated fatigue was assumed, with those after a day off, when recovery was expected. Fatigue levels were subjectively assessed using questionnaires and were also quantified by the Visual Analog Scale (VAS). Objective evaluations included (1) the degree of eye-opening, (2) the maximum distance and speed of facial skin movement by tracking changes in coordinate values of facial markers on the skin during intentional smiling, and (3) analysis of high-frequency (HF) components and the low frequency-to-high frequency (LF/HF) ratio in heart rate variability (HRV). Results: After a night shift, compared to after a day off, subjective assessments of mental and physical fatigue in the questionnaires and VAS values of own fatigue were significantly elevated. Concurrently, objective evaluations revealed that the degree of eye-opening, along with the maximum movement distance and speed of the lower eyelid, cheek, and mouth corners during intentional smiling, were significantly reduced. Furthermore, the HF component, an index of parasympathetic activity, significantly decreased, whereas the LF/HF ratio, an index of sympathetic activity, significantly increased. Additionally, significant correlations were observed between subjective VAS estimation of fatigue levels and each objective parameter examined. Conclusions: Measuring facial parameters is an effective method for objectively assessing facial expressions of fatigue, and these changes are mediated through reduced parasympathetic nervous activity and increased sympathetic nervous activity during fatigue. Full article

This study presents nanofractionation analytics coupled with in vivo profiling of zebrafish embryo paralysis and lethality in response to toxins in cone snail venoms. The focus of this study is on the development of this approach using venoms of Conus marmoreus, Conus ebraeus, and Conus bandanus. In brief, cone snail venoms were separated using reversed-phase chromatography following high-resolution nanofractionation on microplates with parallel mass spectrometry, enabled via a post-column flow split. All collected fractions were dried overnight, followed by assays on zebrafish embryos. For the paralysis assessment, we monitored swimming behavior and swimming distance and found that exposure to cone snail toxins led to paralysis and decreased movement and swim distance. To correlate the masses of eluted toxins with their paralyzing effects and potency, we compared the fractionation retention time versus normalized swimming distance. This allowed identification of the masses of toxins with paralyzing bioactivity, which were predominantly conopeptides. To assess lethality, zebrafish embryos were exposed to fractionated toxins for 24 h, after which they were inspected. The lethal doses and correlated toxins were identified by comparing retention times of fractionation versus the lethal dose values calculated for each fraction. We found that the most lethal venom was from C. bandanus, displaying the largest number of lethal peptides, followed by C. marmoreus and C. ebraeus. On the other hand, the most paralytic venom was from C. ebraeus, presenting a higher number of peptides with non-lethal paralytic effects, followed by C. bandanus and C. marmoreus. This study provides a pipeline to rapidly identify paralytic and lethal cone snail venom toxins using the zebrafish embryo model. Full article