Search Results (30,769)

Infrared thermography enables non-contact monitoring of induction motor thermal behavior, but absolute temperature alone may not distinguish faults with similar surface heating. This paper presents a proof-of-concept case study on the explainable thermographic diagnosis of three-phase induction motors using transient thermal signatures. Two faults were imposed on the same Siemens 1LA2080-4AA10 squirrel-cage motor: loss of forced ventilation (hereafter, cooling failure) and a resistive-bank-induced phase unbalance condition denoted in the test bench as 50% phase unbalance. The approach combines motor-specific regions of interest, transient thermal descriptors, hot area expansion, first-order thermal modeling, healthy baseline residuals, and two physically motivated indices: the Cooling Failure Index (CFI) and Phase Unbalance Thermal Index (PUTI). Cooling failure was analyzed from radiometric CSV data, whereas phase unbalance was evaluated from color-mapped thermal video through scale-based temperature reconstruction and is therefore interpreted as an estimated thermal signature. For the baseline self-reference consistency check, the residual-based fault flag remained false. Cooling failure increased the maximum radiometric temperature from 77.2 °C to 91.6 °C, with 43,399 pixels above 80 °C. Phase unbalance showed a localized stator-dominated rise without hot area expansion above 80 °C in the reconstructed sequence. The rule-based layer assigned high CFI to cooling failure and high PUTI to phase unbalance, supporting explainable case-study-based discrimination while avoiding claims of general classifier validation. Full article

Partial oxidation of methane (POM) is a thermodynamically favorable process for hydrogen and syngas production. Cerium oxide (CeO₂) was investigated as a textural promoter for nickel (Ni)-based catalysts in POM. In this study, CeO₂ was incorporated into Ni/Al₂O₃ catalysts with varying cerium loadings (1–3 wt.%) to examine its role as a structural and functional promoter. Comprehensive physicochemical characterization using BET, XRD, H₂-TPR, and TEM analyses indicated that incorporation of ceria influenced the textural and structural properties, leading to reduced Ni crystallite size from 10 nm to 2.9–3.3 nm, and modified metal-support interactions. The 2 wt.% CeO₂-modified Ni/Al₂O₃ (Ni/2Ce-Al) catalyst demonstrated superior catalytic performance, achieving 70% methane (CH₄) conversion and 64% hydrogen (H₂) yield at 650 °C with stable performance over 275 min time-on-stream with minimal deactivation. Temperature-programmed reduction studies revealed a non-monotonic trend in reduction behavior with an optimal 2 wt.% cerium loading exhibiting the lowest reduction temperature (865 °C). The H₂/CO ratio of 2.92 indicates favorable syngas composition under the conditions studied. Raman spectroscopy showed a decrease in the D/G intensity ratio from 1.55 to 1.33 with increasing cerium loading, indicating enhanced structural ordering and improved coke resistance. The improved catalytic performance may be associated with redox properties of ceria (Ce³⁺/Ce⁴⁺ cycling), its enhanced oxygen storage capacity, and modified Ni-support interactions which can contribute to improved resistance to carbon deposition and Ni sintering. Response surface methodology (RSM) was also successfully used to model the interaction of temperature, space velocity and feed ratio and to confirm the significant positive effect of temperature on conversion. The long-term performance of the optimized catalyst, over a 20 h period, demonstrated the excellent durability of the catalyst, resulting in a stable H₂ yield of ca. 87% and CH₄ conversion of ca. 90%. This work demonstrates that optimized cerium promotion on alumina-supported Ni catalysts can improve catalytic activity and stability, providing a potentially cost-effective, thermally stable catalyst system for industrial hydrogen and syngas production from (CH₄). Full article

Water scarcity and increasing energy demand are critical challenges that often characterize Mediterranean regions, especially islands such as Crete. A sustainable solution for a combined water and energy supply lies in the domain of hybrid renewable energy systems. This research study evaluates a large-scale wind-driven desalination system with pumped-hydro energy storage for the Regional Unit of Rethymno, Crete, focusing on climate-driven demand and air temperature variation. The proposed system integrates wind energy production, seawater desalination, pumped-hydro storage, and water supply both for domestic and for irrigation purposes. Four scenarios, each with increasing air temperature, are examined in order to assess their effect on water demand and system performance. The analysis evaluates electricity allocation, the production of desalinated water, domestic and irrigation coverage, as well as the economic performance of the system. The results indicate that domestic water demand is almost fully covered in all four scenarios, reaching nearly 99.9%, while irrigation water coverage decreases from 82% under present conditions to 67% under higher-temperature scenarios. Wind-generated electricity is mainly used for water-related processes, with a constant share supplied to the grid. The economic assessment indicates that the system can operate under break-even conditions using realistic water and electricity prices. Full article

Coal mining in areas with high groundwater levels often induces land subsidence and water accumulation, leading to the formation of artificial secondary wetlands. Reclaimed wetlands may provide important opportunities for regional biodiversity recovery. Taking Shuoxi Lake Wetland in Huaibei City as a case study, this research aims to reveal the characteristics of waterbird diversity in artificial wetlands after ecological reclamation in a coal mining subsidence area and to identify their key environmental drivers, thereby providing a scientific basis for optimizing the habitat service functions of such wetlands. Based on habitat identification and classification of the reclaimed wetland, waterbird diversity was surveyed, and redundancy analysis (RDA), Mantel tests, and ridge regression models were used to identify the major environmental factors influencing the distribution of different waterbird groups and to quantify their relative contributions. The results showed that after ecological reclamation, a total of 28 waterbird species belonging to 7 families and 6 orders were recorded in the artificial wetland of the coal mining subsidence area. Redundancy analysis (RDA) indicated that wader assemblages were more sensitive to vegetation cover (VC), distance to water bodies (DTW), and distance to buildings (DTB), whereas waterfowl assemblages were mainly affected by distance to buildings (DTB), area (A), and distance to water bodies (DTW), and showed no significant response to vegetation heterogeneity. Mantel tests further confirmed significant spatial correlations between waterbird assemblages and area (A), distance to major roads (DTR), distance to buildings (DTB), water depth (WD), and distance to water bodies (DTW). Ridge regression analysis showed that, under conditions in which anthropogenic disturbance was minimized, vegetation cover (VC) and water depth (WD) were the main positive drivers of wader diversity, whereas perimeter to area ratio (PAR) was the main negative driver. Waterfowl diversity was mainly negatively affected by perimeter to area ratio (PAR) and distance to water bodies (DTW). These findings suggest that appropriately regulating water depth, increasing vegetation cover, and reducing patch fragmentation and anthropogenic disturbance are key measures for enhancing the habitat service functions of artificial secondary wetlands in mining areas. These management strategies provide an important reference for wetland rehabilitation in other coal mining subsidence areas. Full article

In this paper, the nonlinear vibration (NV) response of carbon nanotube-reinforced composite (CNTRC) marine panels (MPs) fitted with symmetric multi-vibration absorbers (MVAs) subjected to steady, velocity-dependent hydrodynamic loads is investigated. To model actual marine conditions more realistically, the lift and drag forces varying with flow velocity were taken into account using experimentally supported Matveev-based formulations for a specific ship. Within the shell, three carbon nanotube (CNT) distribution schemes are considered: one uniformly distributed (UD) CNT configuration and two functionally graded (FG) CNT patterns, namely FG-V and FG-X. The analytical framework is further constructed using classical shell theory (CST) by incorporating geometric nonlinear terms, and the Galerkin technique is employed to obtain a reduced-order model. Thereafter, the NV response of the CNTRC-MPs is predicted through the P-T method, which relies on the joint application of the piecewise constant argument and Taylor series expansion. The results indicate that symmetric MVAs can effectively suppress NV behavior and significantly decrease the maximum NV amplitude of the panel. Moreover, the effectiveness of the proposed configuration is shown to depend on both the absorber characteristics and the reinforcement pattern of CNTs. The study demonstrates that the use of symmetric absorber systems offers a practical and efficient passive vibration-control solution for advanced marine composite panels. Full article

This article concentrates on the practical prescribed-time leader-following consensus problem of second-order multi-agent systems (MASs) under disconnected time-varying topologies. Firstly, a new practical prescribed-time stability criterion is presented, where the time-varying scaling function is related to connected moments and the derivative inequality is increasing in the disconnected intervals. Secondly, inspired by the backstepping design framework, the practical prescribed-time control protocol is designed, including the distributed velocity estimator, virtual velocity and dynamic event-triggered controller. Thirdly, some consensus conditions for achieving leader-following consensus are established and the Zeno behavior is excluded. Finally, simulation results on unmanned aerial vehicle (UAV) formation tracking are provided to demonstrate the effectiveness of the proposed method. Full article

This paper presents a numerical-computational analysis of ${\mathsf{\ell }}_2$-regularized logistic learning for binary intrusion detection under heterogeneous datasets, class imbalance, and cross-dataset shift. Rather than proposing a new intrusion detection architecture, the study examines how numerical conditioning, feature scaling, feature set design, threshold selection, false negative behavior, false alarm behavior, and distribution shift affect operational detection performance. Experiments were conducted on CICIDS2017, UNSW-NB15, and CIRA-CIC-DoHBrw-2020 using reproducible train–validation–test protocols over five fixed random seeds. The numerical audit showed that standard scaling reduced the spectral condition number of traffic feature matrices by several orders of magnitude across datasets and feature configurations. However, scaling did not produce uniformly monotonic predictive gains: in some cases, raw feature optimization achieved comparable or higher F1-score, whereas scaled preprocessing produced more controlled false alarm behavior. In-domain experiments showed that dataset-specific features may improve ranking metrics such as area under the receiver-operating-characteristic curve (AUROC) or area under the precision–recall curve (AUPR) without necessarily improving thresholded operational metrics. Cross-dataset transfer experiments revealed strong source–target asymmetry, with transferred thresholds producing either near-zero positive detection or excessive false alarms. Additional robustness experiments with Random Forest and XGBoost improved in-domain F1-score and false negative rate (FNR), but did not eliminate off-domain degradation, with high FNR persisting under direct cross-dataset transfer. Finally, a Kolmogorov–Smirnov-based distribution shift analysis showed that in-domain discrepancies were small, whereas cross-dataset discrepancies were consistently large under common standardized traffic features. These findings suggest that numerical stability, ranking quality, thresholded detection performance, false negative and false alarm behavior, and distribution shift should be analyzed jointly when evaluating intrusion detection models. Full article

To ensure the safety and stability of single point mooring (SPM) systems operating in shallow waters, this paper investigates the influences of shallow-water effects on mooring systems under different water depth-to-draft ratios. For the pile-founded column single point mooring system in shallow sea areas, based on the numerical calculation method verified by model tests, frequency domain and time domain calculations are carried out to study the specific impact of shallow water effects on the hydrodynamic parameters of the hull, and the critical water depth-to-draft ratios applicable to the two second-order wave load calculation methods (Newman approximation and Pinkster approximation) are analyzed. At the same time, the specific impact of shallow water effects on the dynamic response of the mooring system under three different hull loading conditions at the same and different water depth-to-draft ratios is studied, and the critical water depth conditions for bottom contact in each loading condition are summarized. The results show that the shallow water effect has a significant impact on the hydrodynamic parameters such as RAO of the hull response, especially in the low-frequency response region. There are obvious differences between the Newman approximation and the Pinkster approximation methods. In shallow water conditions, the Pinkster approximation method has a more accurate calculation effect, and when the water depth-to-draft ratio reaches a certain critical value, the calculation results of the two approximation methods are basically consistent. For the same and different water depth-to-draft ratio conditions, the amplitude of the hull motion in the full-load draft state is greater than the other two loading conditions, but the response results of the cable tension are opposite. The research results reveal the specific influences of shallow-water effects on pile-supported single-point mooring (SPM) systems, which can provide references for the safety and stability design of mooring systems and bear great engineering significance for advancing the deployment of single-point mooring systems in shallow water regions. Full article

Although new AI systems have been developed based on the integration of information from multiple sources under conditions of uncertainty, classical probabilistic models are unable to provide structured, interpretable, and reliable representations in the presence of contextual and order effects. Specifically, the fundamental principles of Kolmogorov’s assumptions underlying the modeling overlook certain common violations in real-world decision-making processes, such as non-commutativity, contextual dependence among agents, and interaction effects between information sources characterized by experiential heterogeneity. A Positive Operator-Valued Measure (POVM) formalism defined on a Hilbert space of latent states forms the basis of this article’s structured epistemic representation framework to support the reliability and interpretability of the black box in AI. The resulting model generalizes the classical epistemic quadruplet: Probability, Plausibility, Credibility, and Possibility within a single geometric framework in which three essential non-classical effect mechanisms emerge—(i) the non-commutativity of information acquisition, (ii) the contextuality arising from incompatible observational frameworks, and (iii) the interference interactions between information acquisition channels. We propose the concept of a quantum-inspired fusion operator (QI-Happenability), which introduces symmetric and antisymmetric feedback interaction terms based on the estimation of ordered residuals. An analysis of the proposed framework is then performed using real data, validating the model on the Efron et al. diabetes regression dataset (N = 442; ten standardized physiological predictors; continuous disease progression target), available in scikit-learn, which showed a 17.4% reduction in mean absolute error (MAE) compared to traditional models and significant improvements over polynomial machine learning baselines, as well as ensemble machine learning methods, within a rigorous cross-validation protocol. Contextual analysis indicates that 68% of cases violate classical bounds (CHSH inequality, p < 0.001), empirically confirming the non-classical structured representation in multi-source data. The results confirm the proposed approach as a simpler, more interpretable, and more reliable alternative to black-box models: this work demonstrates how the use of structured epistemic representations in reasoning under uncertainty preserves formal interpretability while retaining useful semantic information. By linking quantum cognition and applied AI, this work could help lay the groundwork for a new generation of interpretable and reliable decision-making systems. Full article

A spatially localized vertex set may contain many internal wedges, yet the same vertices can acquire additional external neighbors and form substantially more wedges in the full graph. We quantify this effect in heavy-tailed geometric random graphs through a wedge-retention ratio that compares wedges whose two endpoints remain inside an angular window with all wedges centered at the same window vertices. Unlike conventional triangle-based clustering coefficients, this ratio does not involve triangle counts. Under the scale and dominance conditions studied here, the infinite-variance regime allows high-strength vertices to dominate the wedge counts because each vertex contributes quadratically in its degree. We prove that the ratio is asymptotically a degree-square-weighted average of squared kernel-mass ratios, where each ratio is the expected fraction of a vertex’s neighbors lying inside the window. For a standard shrinking window of half-width $\Delta =N^{-s}$, $0<s<1$, the ratio remains of order one. Under additional linear-strength assumptions, a dominant set of vertices with strengths of order N yields a ratio of order ${\Delta }^2$; a finite-hub conditional construction shows that these assumptions are mutually compatible. Consequently, order-${\Delta }^2$ behavior is not a generic consequence of shrinking the window but requires a specific linear-strength mechanism. Full article

This article aims to investigate the effects of operational factors on methylene blue (MB) decolorization in a visible light advanced oxidation process (AOP). Response surface methodology was applied to evaluate variable interactions and identify the best operational conditions of the process. The response surface analysis of the expected increase in methylene blue decolorization at different levels of the variable parameters was employed. The experiment evaluated the influence of the following parameters: pH, glucose dose, Na₂S₂O₈ (SPS) concentration, and initial dye concentration. The model confirmed that the optimal conditions for MB decolorization in the visible light-driven advanced oxidation process were: SPS dose = 30 mM, glucose dose = 230 mM, pH = 4. The highest decolorization level was obtained at a concentration of C_0[MB] = 5 ppm (R = 63%). The kinetics of the process followed pseudo-first-order (R² = 97–99%). The radical scavenger test showed that both sulfate and hydroxy radicals are involved in MB decolorization. The test confirmed that the proposed method is effective even at a broad range of MB concentrations (1–10 ppm). The analysis showed that the proposed method is highly efficient across various dye concentrations, which is particularly significant from a practical perspective. Summarizing, this study fills a research gap in the optimization of methylene blue decolorization within the scope of advanced oxidation processes driven by visible light and glucose, demonstrating high efficiency across a range of different environmental variables. The study showed that the SPS/Vis/glucose process can serve as a valuable alternative to conventional decolorization methods. Full article

Industrial maintenance and fault-diagnosis systems generate textual records, such as maintenance work orders, service requests, causal analyses, and troubleshooting solutions. These records contain domain-specific named entities that provide valuable knowledge for intelligent monitoring, fault diagnosis, maintenance decision support, and industrial knowledge graph construction. However, in practical industrial environments, maintenance records are strongly associated with specific equipment types, production processes, fault modes, and enterprise-specific terminology. As a result, entity schemas vary across systems, new entity types emerge with equipment updates, and high-quality annotation requires substantial domain expertise. These factors make it difficult to obtain sufficient labeled samples for each industrial entity type. Under such low-resource conditions, conventional supervised named entity recognition (NER) models tend to suffer from unstable entity boundary detection and biased entity representations. To address these challenges, this paper proposes a boundary-aware knowledge graph-guided adaptive prototype correction framework for few-shot NER in industrial maintenance information systems. The proposed framework first introduces a boundary-aware span detection mechanism to improve entity localization in noisy and irregular maintenance texts. A knowledge graph-guided adaptive prototype correction module is then designed to construct entity class prototypes from limited support examples, reducing prototype bias caused by sparse annotations. Experiments are conducted on two representative industrial datasets, MaintIE and CFDK, covering maintenance short texts and fault-diagnosis records. Experimental results show that the proposed framework achieves an average Micro-F1 improvement of 1.89 percentage points over the strongest compared baseline across 12 episodic settings on the two industrial datasets: three MaintIE coarse-grained settings, six MaintIE fine-grained settings, and three CFDK settings. The ablation and sensitivity analyses further indicate that boundary-aware span modeling and KG-guided prototype correction jointly contribute to low-resource entity classification. This study provides a data-efficient information extraction solution for AI-enabled industrial knowledge acquisition, fault diagnosis, and maintenance decision support. Full article

This study presents an experimentally evaluated reduced-order maneuvering framework for a trimaran Autonomous Underwater Vehicle (AUV) by integrating CFD-derived hydrodynamic coefficients with experimentally characterized thruster forces for low-speed pivot turn prediction. Unlike previous studies that primarily focused on hydrodynamic coefficient identification or computational fluid dynamics (CFD)-based hydrodynamic analyses, the proposed approach evaluates the capability of independently derived hydrodynamic parameters to reproduce experimentally observed maneuvering behavior within a computationally efficient three-degrees-of-freedom (3DOF) dynamic model. The maneuvering formulation incorporates nonlinear hydrodynamic derivatives adopted from a previously published virtual Planar Motion Mechanism (PMM) investigation together with experimentally measured thrust characteristics. Numerical simulations were performed in MATLAB using a planar 3DOF maneuvering model, while an experimental evaluation was conducted through a representative pivot turn maneuver in a controlled pool environment using video-based trajectory tracking. The simulated and experimental trajectories exhibited consistent turning behavior, yielding Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values of 0.0992 m and 0.0905 m, respectively. Although discrepancies were observed in the heading response during the later stages of the maneuver owing to simplified hydrodynamic assumptions and unmodeled nonlinear effects, the proposed framework successfully reproduced the dominant trajectory and yaw characteristics of the investigated maneuver. These results indicate that the proposed reduced-order framework was able to reproduce the investigated low-speed pivot turn maneuver under the validated operating conditions, while broader experimental validation remains necessary to establish its general applicability. Full article

This paper deals with the use of the singular boundary method for analysis of time-harmonic acoustic scattering by penetrable obstacles. There are many formulations of boundary-type meshless methods, distinguished mainly by the manner in which the singularity of the fundamental solution at the source point is treated. The article presents the singular boundary method (SBM), a boundary-only, integration-free collocation technique in which the source points are placed directly on the physical boundary, so that no volume mesh, no auxiliary boundary and no element connectivity are required. The scattered field in the exterior and the transmitted field inside each obstacle are each represented by a single layer of fundamental solutions. The determination of the origin intensity factors (OIFs), which replace the singular self-interaction of the single-layer fundamental solution and of its normal derivative, is the crucial part of the method. A closed-form OIF is employed for the Dirichlet boundary condition and a purely geometric OIF based on the signed boundary curvature is employed for the Neumann boundary condition, so that only two boundary operators are required instead of the four operators used in the direct formulation. The accuracy of the method has been compared with the analytical Mie series and the third-order deltaBEM. Third-order convergence and reasonable accuracy, when compared to the exact solution, are obtained throughout. Full article

This study analyzes a low-cost potassium feldspar mineral from Chihuahua, Mexico, for removing cationic dyes (methylene blue and rhodamine 6G) from water. The raw mineral, characterized by Rietveld refinement as a polymineralic composite of sanidine (49 vol%), muscovite (27 vol%), calcite (16 vol%), and anorthoclase (7 vol%), demonstrated significant dual functionality. In darkness, it acted as an effective adsorbent, achieving 98% and 76% removal of MB and R6G, respectively, after 120 min, with adsorption behavior fitting the Langmuir isotherm. Under solar irradiation, the mineral facilitated photocatalytic degradation, evidenced by a faster intensity decrease and a shift in the absorption bands, and the near-complete decolorization of the dyes. The degradation kinetics were significantly accelerated in a synergistic advanced oxidation process (AOP) with added hydrogen peroxide (H₂O₂), achieving 98% degradation for MB and 93% degradation for R6G within 15 min, compared with 97% for MB and 65% for R6G under sunlight irradiation alone. Kinetic analysis revealed that the process consistently followed a pseudo-second-order model, indicating a surface-controlled mechanism dependent on dye concentration and the availability of active sites. Additional fitting with the Elovich and Avrami models suggested heterogeneous surface behavior and multistep degradation pathways, implying that the overall process involved concurrent adsorption, surface-mediated catalytic reactions, and oxidative degradation driven by photogenerated reactive species. Additionally, the scavenger tests revealed that the dominant reactive species depended on the presence of ${\mathrm{H}}_2{\mathrm{O}}_2$: ${\mathrm{O}}_2^{•-}$ radicals prevailed in the peroxide-free system, whereas $•\mathrm{OH}$ radicals dominated under ${\mathrm{H}}_2{\mathrm{O}}_2$- assisted conditions. Photoluminescence spectroscopy analysis provided mechanistic insights, tracking the evolution of dye monomers, dimers, and aggregates, confirming structural degradation of the dyes and revealing the formation of specific fluorescent intermediates. Together, these findings highlight the mineral’s potential as an abundant, eco-friendly material for solar-driven wastewater treatment. Full article