Search Results (502)

Hydrogen peroxide (H₂O₂) is widely used in semiconductor cleaning and etching, where ultralow levels of metallic, anionic, organic, and particulate impurities must be strictly controlled. Industrially produced H₂O₂ therefore requires extensive downstream purification before it can meet electronic-grade specifications. Conventional purification routes based on distillation or rectification, adsorption, ion exchange, and final filtration are technically mature, but they remain constrained by substantial energy consumption, multiple treatment stages, chemical regeneration, secondary waste generation, and safety risks associated with H₂O₂ decomposition. This review critically evaluates membrane technologies for purifying and concentrating electronic-grade H₂O₂. Microfiltration and ultrafiltration are discussed as front-end clarification processes, nanofiltration as an intermediate impurity-load-reduction step, and reverse osmosis as the membrane process with the strongest direct experimental for ionic-impurity removal from concentrated H₂O₂. Pervaporation and membrane distillation are assessed as emerging water-removal technologies, although their industrial applicability remains insufficiently validated. Membrane material strategies, including oxidation-resistant polymers, inorganic and hybrid membranes, antioxidant-containing composites, and emerging MOF- and two-dimensional-material-based membranes, are also evaluated. Particular attention is paid to the limited direct evidence available for emerging materials and to the risks of H₂O₂ decomposition, material leaching, particle release, and deterioration of membrane selectivity. The available evidence indicates that membrane processes are currently more appropriately regarded as complementary clarification, purification, polishing, or concentration units rather than complete replacements for established industrial technologies. Future studies should prioritize long-term oxidative stability, ppb- and ppt-level impurity validation, low H₂O₂ loss, module-material compatibility, process safety, and continuous pilot-scale techno-economic assessment. Full article

The South Atlantic is a classic region of hotspot volcanism, with numerous intraplate magmatic structures, such as the Walvis Ridge, Rio Grande Rise, Fernando de Noronha Ridge, and Victoria-Trinda Ridge. These structures record mantle plume activity and plate tectonics since the breakup of the South America–Africa continent, but the spatiotemporal correlations of mantle end-members among different hotspot systems remain unclear. This paper uses the Unified Manifold Approximation and Projection Algorithm (UMAP) and Hierarchical Agglomeration Clustering Algorithm (HAC) to perform dimensionality reduction and cluster analysis on 288 basalt Sr-Nd-Pb isotope data from 12 major hotspot-derived seamount chains/rises in the South Atlantic, identifying three types of mantle end-members: EM-type, HIMU-type, and PREMA/FOZO-type. The results show that HIMU-type end-members are mainly distributed in St. Helena Island and its associated Guinea seamount chain; EMI-type end-members dominate the Walvis Ridge basement, Rio Grande Rise, and discovered seamount chain; and PREMA/FOZO-type end-members are mainly distributed in the Brazilian continental margin seamount chain. In terms of time series, EM-type magmatic activity began in the Early Cretaceous (~132 Ma), while HIMU-type hotspot activity appeared later (~82 Ma), and both were vertically superimposed on the Walvis Ridge basement. Based on the spatiotemporal distribution characteristics of mantle end-members in hotspots in the South Atlantic and Indian Oceans, this paper proposes a two-stage magmatism model for hotspots at the African margin: in the early stage, EM-type material, associated with continental delamination and ancient lithosphere recycling, preferentially melted, forming large-scale submarine plateaus or seamount chains; in the later stage, HIMU-type material, associated with the reactivation of subducted oceanic crust or Archean carbonated subcontinental lithospheric mantle (SCLM), melted and rose in weak areas of mature oceanic crust, forming smaller seamounts. This study provides a new perspective on the unified genetic mechanism of multiple hotspots in the South Atlantic and offers a reference for understanding the generation, evolution, and magmatic activity of hotspots during the breakup of Gondwana. Full article

High-precision onboard orbit propagation, often unavailable due to high demands of onboard resources, is crucial for autonomous optical link pointing and acquisition in large-scale low Earth orbit satellite constellations. For such large-scale constellations, an advantageous feature for efficient and accurate orbit propagation is the correlation or similarity in the orbit perturbations experienced by multiple satellites. Yet, this correlation has not been fully utilized in existing orbit propagation methods. In this work, we propose a cross-referential orbit propagation framework that leverages multiple historical reference arcs from other satellites within the same constellation to improve prediction accuracy and reliability. The framework achieves error reduction comparable to that of ensemble learning by aggregating the predictions from a single lightweight model under varying reference inputs, thereby preserving a simple and compact model architecture. To ensure the generalizability of this compact model, we further introduce a network architecture termed the Compressive Decoder for Orbit Propagation (CDOP). The CDOP predicts low-dimensional representations of the propagated orbits, from which the full time series are subsequently decoded. By incorporating modules from pre-trained compressive autoencoders, the CDOP mitigates overfitting while maintaining a low inference cost. The proposed method is validated on simulated Walker constellations with different geometries. The results demonstrate an average 24 h position error of approximately 200 m, with an inference cost 30 times lower than that of a reduced-dynamic numerical propagator. The framework is computationally lightweight, generalizes well across different initial conditions, and is well suited for onboard deployment in autonomous optical link acquisition. Full article

Hybrid RF-FSO-THz communication systems are promising candidates for future Internet of Things (IoT) and 6G networks because they combine the robustness of radio frequency links, the high-capacity potential of Free-Space Optical communications, and the ultra-wideband capabilities of terahertz transmission. Adaptive channel selection in such systems depends on multiple correlated environmental and physical-layer variables, including distance, rain intensity, humidity, visibility, turbulence strength, signal-to-noise ratio, channel capacity, and energy-efficiency metrics. This paper presents a physics-supported benchmark framework for supervised adaptive channel selection in hybrid RF-FSO-THz systems and systematically investigates the impact of linear and nonlinear dimensionality-reduction techniques on predictive performance, statistical robustness, computational complexity, and physical interpretability. A multi-scenario dataset comprising 5000 samples was generated using calibrated RF, FSO, and THz propagation models under clear, rain, fog, and worst-case environmental conditions. Principal Component Analysis (PCA) and Kernel PCA were evaluated together with Random Forest, Support Vector Machines (SVMs), XGBoost, Gradient Boosting (GB), Multi-Layer Perceptron (MLP), Logistic Regression, and Decision Trees. The results demonstrate that PCA preserves nearly all predictive capabilities while reducing the original 33-dimensional feature space by approximately 81.8%, maintaining accuracies close to 97–98% with the best-performing classifiers. Statistical significance analysis confirms that PCA introduces only modest degradations, whereas Kernel PCA consistently reduces the predictive performance while increasing memory requirements and inference latency. Additional environmental-only validation experiments indicate that adaptive channel selection remains highly learnable even when only pre-selection environmental descriptors are available, partially mitigating concerns regarding self-consistency bias. Overall, the results suggest that PCA provides an advantageous compromise among predictive accuracy, computational efficiency, statistical robustness, and physical interpretability for supervised adaptive channel selection in physics-supported hybrid wireless communication systems. Full article

Investigating the spatial correlation characteristics and configurational pathways of ecological space service value (ESSV) is of importance for alleviating urban ecological pressure. This study, focusing on the northern slope of the Tianshan Mountains in China, employs the modified value equivalent method, gravity model, and configurational analysis model to elucidate the spatiotemporal evolution mechanisms of ESSV. The results demonstrate that: (1) The extent of ecological space decreased sharply (328.25 km²), primarily converting to other ecological space. Among these, the grassland ecological space experienced the largest reduction (215.34 km²), whereas the decline in forest ecological space was relatively modest (58.85 km²). (2) ESSV showed a fluctuating but overall increasing trend, with ΔESSV dominated by negative changes. Spatially, the pattern was characterized by higher values in the west, lower values in the east, and a contiguous high-value area in the central region. (3) The network of ESSV exhibited multiple connections and multiple cores, with the strength of network linkages continuously strengthening and showing a trend of expansion from the central region toward the south and north. (4) High ESSV depends on the configurational effects of multidimensional resilience factors. Several configurational modes were identified, including single-core resilience-driven and multi-dimensional resilience synergy-driven modes. Full article

The rapid growth of web-based services has intensified the need for reliable mechanisms to distinguish malicious Uniform Resource Locators (URLs) from legitimate ones. Phishing campaigns, malware distribution networks, and defacement operations increasingly rely on deceptive web addresses to compromise unsuspecting users and critical infrastructure. Accurate URL classification plays a critical role in mitigating phishing attacks, malware distribution, and other cyber threats. This study presents a machine learning framework for detecting malicious URLs in cybersecurity applications. This study presents a comprehensive empirical evaluation of multiple machine learning and deep learning approaches for URL classification under two experimental settings: training with the complete feature set and training with a reduced subset obtained through Particle Swarm Optimization (PSO). The framework incorporates advanced feature engineering techniques that capture domain-specific characteristics of malicious URLs. Seventeen classifiers, encompassing traditional ensemble methods, neural architectures, and hybrid stacking configurations, were evaluated on a publicly available dataset of 651,191 URL samples retrieved from Kaggle. The PSO reduced the original ten-feature space to seven discriminative features, representing a 30% dimensionality reduction. Experimental results demonstrate that all-feature models consistently outperformed their PSO-reduced counterparts, with Random Forest achieving the highest classification accuracy of 91.90% and an F1-score of 0.9165. The findings offer empirical grounding for the design of computationally efficient URL threat detection systems and provide actionable directions for future research in adversarial machine learning and real-time cybersecurity pipelines. Full article

Deterministic radio wave propagation models provide high accuracy in complex outdoor environments but remain computationally impractical for large-scale network planning and spectrum management. In contrast, empirical and hybrid models offer low complexity at the expense of reduced accuracy, systematic bias, and limited terrain sensitivity. This paper proposes a unified delta learning framework that enhances fast baseline propagation models by learning a data-driven correction toward a deterministic Parabolic Equation Modeling (PEM) reference. A key novelty lies in a compact, physics-informed feature representation that replaces the full terrain profile with an 18-dimensional vector combining local geometric descriptors, global terrain characteristics, and baseline responses, enabling accurate correction with low-dimensional input. The study also provides the first systematic investigation of delta-based correction across multiple widely used propagation models. The framework is evaluated for free-space propagation, ITU-R P.1546, ITU-R P.1812, and ITU-R P.452 using ridge regression, kernel ridge regression, gradient boosting regression trees, and a neural network model. Model performance is assessed in terms of error reduction, bias mitigation, robustness across learning algorithms, and profile-level generalization to previously unseen propagation paths within the considered terrain categories. Results show substantial error reduction, with up to twofold improvement for simpler baseline models and consistent gains for hybrid models, while preserving computational efficiency. Full article

In complex urban environments, Global Navigation Satellite System (GNSS) signals are easily affected by building blockage and multipath effects, which may degrade positioning quality or even cause GNSS denial. As a result, conventional integrated navigation systems suffer from accumulated errors due to insufficient global constraints. To address this problem, a multi-source integrated positioning method incorporating semantic information is proposed. Fixed traffic lights are selected as semantic landmarks, and an object detection network is used to extract the center pixel coordinates and detection confidence of the landmarks. Then, by combining depth information, camera pose, and the prior global coordinates of fixed semantic landmarks, a semantic target inversion model is established to transform two-dimensional image information into three-dimensional position estimates in the world coordinate system. Semantic factors are further constructed and incorporated into backend factor graph optimization. To determine the weighting of semantic factors, the influences of pixel localization error, depth estimation error, camera pose error, and prior coordinate error of fixed semantic landmarks on semantic observations are analyzed, and a noise covariance model for semantic factors is established. Finally, an unmanned ground vehicle experimental platform is built to validate and analyze the proposed factor graph algorithm. The experimental results show that, under GNSS-degraded conditions, the algorithm with semantic factors can provide supplementary global constraints for the system and effectively suppress accumulated positioning errors. In Experiment 1, compared with the algorithm without semantic factors, the maximum absolute trajectory error is reduced by 46.26%. To further verify the applicability of the proposed method in more complex scenarios, Experiment 2 is conducted on a longer route with multiple semantic landmarks and a more severe GNSS-degraded interval. The results show that the proposed method reduces the maximum APE from 6.5432 m to 3.4778 m, corresponding to a reduction of approximately 46.85%. These results demonstrate that the proposed semantic factor can improve the robustness of multi-source fusion localization in GNSS-degraded environments. Full article

The dynamic characteristics and buffeting response of long-span single-pylon cable-stayed bridges are not fully understood after cable failure occurs in coastal wind environments. This study investigates how the location, number, and pattern of cable failures affect structural performance. A three-dimensional finite element model of a 280 m main-span bridge was established using the aerodynamic coefficients extracted from wind tunnel tests. Modal analyses and nonlinear time-domain simulations were conducted. The results show that frequency reduction concentrates in lower-order vertical bending modes, with the first and second modes being the most sensitive. Variations in frequency are closely related to the failure location of stay cables, with the largest reduction at the mode antinode. Unilateral multiple failures induce bending–torsion coupling, whereas symmetric bilateral failures only lower frequencies. Under wind loads, the failure of stay cables results in the redistribution of static internal forces, primarily to the adjacent stay cables on the same side. This phenomenon is enhanced as the number of failed cables increases. The change in buffeting internal forces results in a non-monotonic trend, and the shorter cables near the pylon are more sensitive. Cable failure, which occurs at different phases of the buffeting process, significantly influences the structure's transient response. The scenario in which the structure is subjected to wind loads after cable failure results in the largest variation amplitude. Full article

Background/Objectives: Differentiating mediastinal lymphadenopathy associated with lung cancer from sarcoidosis remains a clinical challenge because of overlapping imaging findings. This study evaluated whether CT-derived radiomic features, alone and in combination with clinical variables, could support the non-invasive differentiation of these two entities. Methods: In this retrospective single-center study, 204 histopathologically confirmed mediastinal lymph nodes were analyzed. A total of 107 radiomic features were extracted from manually segmented contrast-enhanced thoracic CT images. Multiple feature selection methods, dimensionality reduction techniques, and machine learning classifiers were evaluated using patient-level five-fold cross-validation. Additional clinical-only, combined clinical–radiomic, one-node-per-patient sensitivity, and exploratory interobserver feature stability analyses were performed. Results: Among radiomics-only models, LASSO achieved the highest ROC–AUC of 0.9108, whereas ElasticNet achieved the highest accuracy of 81.20%. The clinical-only ensemble model using age, sex, and smoking status showed strong performance, with an accuracy of 94.92% and an ROC–AUC of 0.9733. Selected combined clinical–radiomic models showed numerically higher performance; the combined correlation-filtered ensemble model achieved the highest accuracy of 97.78% and an ROC–AUC of 1.0000. Clinical integration also yielded more compact feature subsets in some methods, as combined LASSO selected 9.6 variables while improving ROC–AUC from 0.9108 to 0.9667 compared with radiomics-only LASSO. In the one-node-per-patient sensitivity analysis, clinical-only and combined models retained high performance, whereas radiomics-only LASSO showed reduced performance. Exploratory interobserver analysis showed moderate reproducibility for only a subset of radiomic features. Conclusions: CT-derived radiomic features may provide complementary information for differentiating mediastinal lymphadenopathy associated with lung cancer from that associated with sarcoidosis. However, clinical variables were highly informative, and the incremental value of radiomics should be interpreted cautiously. Further multicenter studies with external validation, standardized segmentation protocols, and clinically balanced cohorts are required before routine clinical implementation can be recommended. Full article

Distributed seismic sensor networks integrated into the Internet of Things (IoT) infrastructure enable continuous condition monitoring of large-scale engineering structures. During long-term operation, however, measurement channels are subject to sensitivity drift, increased noise, and pulse artifacts that statistically mimic real vibration events. Related deep-learning techniques for noisy and ill-posed inverse problems have demonstrated the value of combining principled physical priors with deep models. Although the application domain differs, the underlying methodological insight—that constrained, physics-aware feature mappings can stabilize learning under noisy and partially observed conditions—directly motivates the use of a parameterized quantum circuit as a nonlinear feature transformer in the present work, where Hilbert space mapping serves as an analogous structural prior for the latent representation. Three principal fault modes are considered in this work, corresponding to the dominant degradation mechanisms observed in long-term seismic instrumentation: sensor drift, increased noise, and sensor failure. Each fault mode produces a distinct signature in the windowed feature space; the proposed model is trained to discriminate between them based on the latent CNN-LSTM-VQC representation. We propose a hybrid quantum-inspired deep-learning model (QC-DL) for the detection and diagnosis of channel-degradation anomalies. The architecture combines a 1D-CNN+LSTM feature extractor with a parameterized variational quantum circuit (VQC) used as a nonlinear feature transformer. All quantum experiments were performed on the QPanda3 CPUQVM simulator. The data were split chronologically prior to windowing to avoid information leakage. On real-world labeled accelerometric data with four operating modes (normal/drift/high-noise/failure), the QC-DL model achieved a macro-averaged F1 score of approximately 0.69 and per-class AUC values in the range 0.88–0.99. The mean early-detection latency was 1.6 s versus 2.1 s for the CNN-LSTM baseline (~24% reduction). An ablation study against a parameter-matched classical MLP showed that the gain is modest and not solely attributable to additional nonlinearity. The reported p-values (p = 0.70, p = 0.29) do not establish statistical significance. The results support the feasibility of hybrid quantum-inspired deep learning for sensor-channel verification, while highlighting the need for evaluation on real NISQ hardware. This paper proposes a hybrid quantum-inspired approach for detecting and diagnosing such anomalies in the time series of distributed seismic networks. The architecture combines a classical temporal feature extraction module based on one-dimensional convolutional layers and a recurrent long short-term memory (LSTM) network, which generates a latent window representation of the signal, with a parameterized variational quantum circuit used as a nonlinear feature processor in a hybrid computational circuit. Experimental validation was performed on real-world labeled data with multiple sensor degradation modes. The evaluation was organized in a scoring framework aligned with autonomous operation through window ranking and threshold alarm generation. In the experiments, the proposed model provided a macro-averaged F1 score of approximately 0.69 and area under the receiver operating characteristic (AUC) curve values in the range of 0.88–0.99 across classes, outperforming baseline deep models. The average early detection latency was 1.6 s versus 2.1 s for the baseline recurrent model (a 24% reduction). An ablative comparison with a control model based on a classical multilayer perceptron of comparable dimension confirmed that the improvement is not limited to the addition of additional nonlinearity. The obtained results indicate the potential of quantum-supported deep learning for improving the reliability of long-term vibration monitoring and verifying the correctness of sensor channels in distributed seismic networks. Full article

Structured Query Language injection (SQLi) remains a persistent threat to web applications due to the obfuscation, diversity, and evolving structure of malicious payloads, which limit the effectiveness of conventional rule and signature-based Web Application Firewalls (WAFs). Although prior studies have reported high detection performance using individual feature extraction methods or offline classification models, limited work has addressed deployment-oriented SQLi prevention through an integrated real-time inspection framework. This paper proposes a Machine Learning (ML)-based SQLi detection and prevention framework that combines hybrid feature representation, supervised dimensionality reduction, Genetic Algorithm (GA)-based hyperparameter optimization, and real-time WAF validation. Multiple public SQLi datasets were merged, cleaned, and deduplicated to improve exposure to diverse query patterns. SQL queries were encoded using Term Frequency–Inverse Document Frequency (TF-IDF), Word2Vec, and FastText features, which were fused and transformed through a Supervised Autoencoder into a compact discriminative representation. GA was then employed to optimize multiple classifiers, including Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), and Multi-Layer Perceptron (MLP). The MLP achieved the best overall performance, with an accuracy of 0.998681. The optimized model was deployed within a lightweight Flask-based WAF for real-time Hypertext Transfer Protocol (HTTP) request inspection and malicious input blocking. SQLMap v1.8.4-based robustness testing and runtime analysis demonstrate that the proposed framework provides effective SQLi prevention with practical deployment efficiency beyond conventional offline benchmark evaluation. Full article

Similar to a web search engine, we have developed a computer-based acoustic search engine tailored for the critical scenario of high-speed aircraft ground explosion monitoring, addressing the long-standing challenge of real-time localization for such high-impact events. Unlike conventional acoustic source localization techniques, our method uniquely resolves the separation and localization of multiple aliasing events, which are prevalent in high-speed aircraft explosion scenarios due to complex shock wave propagation and overlapping signatures. We first calculate the waveforms of all possible acoustic sources over 2D grids. Then, a dimensionality reduction method and fast search technology are applied to the database. Once a high-speed aircraft ground explosion occurs, the real-time system returns detection feedback by matching real-time data with the pre-established search database. Different from other artificial intelligence (AI)-based approaches, the acoustic search engine can handle multiple aliased acoustic events in real time and does not require any prior information or input parameters—a key advantage for emergency response to high-speed aircraft explosions where predefined parameters are often unavailable. Both synthetic tests and field data applications (using actual acoustic records from high-speed aircraft ground explosion experiments) demonstrate the method’s credibility in detecting and localizing multiple acoustic sources. Full article

Ground-penetrating radar is widely used in non-destructive pavement evaluation, but the occurrence of multiple hyperbolic signatures in radargrams of cracked pavements remains insufficiently characterized, particularly for top-down and bottom-up cracking. This study investigates the occurrence of detectable hyperbolas in numerical GPR radargrams by comparing two crack models under a controlled two-dimensional numerical design. Model A represents top-down cracking, and Model B represents bottom-up cracking. For each model, four parametric studies were performed by varying crack width, crack depth, asphalt-layer thickness, and granular-layer thickness, yielding 32 simulations in total. All cases were modeled in gprMax2D at 2300 MHz and processed in MATLAB through radargram pre-processing, central A-scan candidate detection, lateral tracking of hyperbolic events, and final classification based on stable retained trajectories. Model A was predominantly characterized by 3H responses, whereas Model B was predominantly characterized by 2H responses, with no 3H case observed. In Model A, crack-width increase was associated with the strongest occurrence change, whereas in Model B, greater asphalt-layer thickness was associated with a reduction from 2H to 1H. The first apex TWT provided a complementary discriminator between the two models. These findings provide controlled numerical reference trends that may support the interpretation of hyperbola occurrence in GPR-based pavement crack assessment. Full article

Cognitive stress detection based on electrocardiogram (ECG) is challenged by the high dimensionality of multichannel analysis, redundancy between heart rate variability (HRV) and morphological descriptors, and variability in classifier performance. We developed and evaluated a cognitive stress classification framework based on a standardized ECG acquisition protocol, the integration of HRV and morphological descriptors extracted from 12 leads, and an adaptive feature selection strategy using a binary genetic algorithm with explicit penalization of dimensionality. Seventy healthy students aged 18–25 years participated, and cognitive stress was induced using a task based on PMA-R Factor R. The initial dataset included 27 descriptors per lead, and the proposed dimensionality reduction method was compared with two reference schemes: no dimensionality reduction and conventional principal component analysis (PCA) with a 99% cumulative explained variance threshold. Performance was assessed over 30 data splits using five classifiers: logistic regression, linear support vector machine (SVM), radial basis function SVM (SVM-RBF), k-nearest neighbors (KNN), and decision tree. The best trade-off between parsimony and predictive performance was achieved with $\lambda =0.05$, yielding a compact subset of 11 features on average and a mean AUC of 0.830. In the final comparison, the adaptive strategy achieved the best overall performance with SVM-RBF (AUC $=0.830\pm 0.047$; specificity $=0.814\pm 0.115$), outperforming both the full feature set and PCA. These findings indicate that penalized genetic selection validated across multiple classifiers is an effective strategy for identifying compact, discriminative, and robust feature subsets for ECG-based cognitive stress classification. Full article