Search Results (1,659)

In this paper, we investigate the non-uniform m-dimensional Moran walk $(Z_n^{\left(1\right)},\dots ,Z_n^{\left(m\right)})$, where each component process ${\left(Z_n^{\left(j\right)}\right)}_{1\le j\le m}$, either increases by one unit or resets to zero at each step. Using probability generating functions, we analyze key statistical properties of the walk, with particular emphasis on the mean and variance of its final altitude. We further establish closed-form expressions for the limiting distribution of the process, as well as for the mean and variance of each component. These results extend classical findings on one- and two-dimensional Moran models to the general m-dimensional setting, thereby providing new insights into the asymptotic behavior of multi-component random walks with resets. Full article

The increasing demand for reliable, sensitive, and cost-effective gas sensors drives ongoing research in this field. Ideal gas sensors must demonstrate high sensitivity and selectivity, stability, rapid response and recovery times, energy efficiency, and affordability. One-dimensional (1D) metal oxide semiconductors (MOSs) are prominent candidates due to their excellent sensing properties and straightforward fabrication processes. The sensing efficacy of 1D MOSs is heavily dependent on their surface area and porosity, which influence gas interaction and detection efficiency. Polymeric templates serve as effective tools for enhancing these properties by enabling the creation of uniform, porous nanostructures with high surface area, thereby improving gas adsorption, sensitivity, and dynamic response characteristics. This review systematically examines the role of polymeric templates in the construction of 1D MOSs for gas sensing applications. It discusses critical factors influencing polymer template selection and how this choice affects key microstructural parameters, such as grain size, pore distribution, and defect density, essential to sensor performance. The recent literature highlights the mechanisms through which polymer templates facilitate the fine-tuning of nanostructures. Future research directions include exploring novel polymer architectures, developing scalable synthesis methods, and integrating these sensors with emerging technologies. Full article

This article presents a novel approach to looking at steady-state thermoelastic boundary value problems in isotropic elastic plates with curvilinear holes using a complex variable approach and rational conformal mappings. The physical domain with a non-circular opening is mapped conformally to the unit disk. A thermoelastic potential combines the temperature distribution, which is determined by the Laplace equation with Neumann boundary conditions. Gaursat functions, which are shown as truncated power series, show the complicated stress and displacement fields. They are found by putting boundary constraints at certain collocation points. This procedure presents us with a linear system that can be solved using the least squares method. The method is applied in an annular shape that is exposed to a radial temperature gradient. This experiment shows how changes at the boundary affect the distribution of stress. According to numerical simulations, stress distributions are more uniform when boundaries are smoother, but stress concentrations increase with the size of geometric disturbances. The suggested approach remarkably captures the way geometry and thermal effects interact in two-dimensional thermoelasticity. It is a reliable tool for researching intricate, heated elastic domains. Full article

This paper focuses on the design of a three-dimensional trajectory tracking controller for underactuated quadrotor-like autonomous underwater vehicles (QAUVs) subject to actuator saturation. A hand position method with a signum function is proposed to handle the under-actuation of QAUVs, while avoiding trajectory tracking in the opposite direction. The dynamic surface control (DSC) technique is integrated to eliminates the complexity explosion problem of standard backstepping. An auxiliary dynamic system is employed to handle input saturation. By using Lyapunov stability theory and phase plane analysis, it is proved that the proposed control law ensures that the QAUVs converge to the desired position with arbitrarily small errors, while guaranteeing the uniform ultimate boundedness of the whole closed-loop system. Comparative simulation results verify the effectiveness of the proposed control law. Full article

High-performance electrical contact materials are crucial for electric power systems, new energy vehicles, and rail transportation, as their properties directly impact the reliability and safety of electronic devices. Enhancing these materials not only improves energy efficiency but also offers notable environmental and economic advantages. However, traditional composite contact materials often suffer from poor dispersion of the reinforcing phase, which restricts further performance improvement. Graphene (G), with its unique two-dimensional structure and exceptional electrical, mechanical, and tribological properties, is considered an ideal reinforcement for metal matrix composites. Yet, its tendency to agglomerate poses a significant challenge to achieving uniform dispersion. To overcome this, the study introduces a dual approach: modulation of the zeta potential (ζ) in the silver-plated liquid to enhance G’s dispersion stability, and concurrent optimization of the composite electrodeposition process. Experimental results demonstrate that this synergistic strategy enables the uniform distribution of G within the silver matrix. The resulting silver–graphene (Ag-G) composite coatings exhibit outstanding overall performance at both micro and macro levels. This work offers a novel and effective pathway for the design of advanced electrical contact materials with promising application potential. Full article

This paper presents an analytical investigation of the free vibration behavior of fiber metal laminate (FML) beams with three types of boundary conditions, considering the temperature-dependent properties and the interfacial slip. In the proposed model, the non-uniform temperature field is derived based on one-dimensional heat conduction theory using a transfer formulation. Subsequently, based on the two-dimensional elasticity theory, the governing equations are established. Compared with shear deformation theories, the present solution does not rely on a shear deformation assumption, enabling more accurate capture of interlaminar shear effects and higher-order vibration modes. The relationship of stresses and displacements is determined by the differential quadrature method, the state-space method and the transfer matrix method. Since the corresponding matrix is singular due to the absence of external loads, the natural frequencies are determined using the bisection method. The comparison study indicates that the present solutions are consistent with experimental results, and the errors of finite element simulation and the solution based on the first-order shear deformation theory reach 3.81% and 3.96%, respectively. At last, the effects of temperature, the effects of temperature degree, interface bonding and boundary conditions on the vibration performance of the FML beams are investigated in detail. The research results provide support for the design and analysis of FML beams under high-temperature and vibration environments in practical engineering. Full article

Electromagnetic Railguns Face Severe Ablation and Melting Risks Due to Extremely High Transient Thermal Loads During High-Speed Launching, Directly Impacting Launch Reliability and Service Life. To address this thermal management challenge, this study proposes and validates the effectiveness of spray cooling technology. Leveraging its high heat transfer coefficient, exceptional critical heat flux (CHF) carrying capacity, and strong transient cooling characteristics, it is particularly suitable for the unsteady thermal control during the initial launch phase. An experimental platform was established, and a three-dimensional numerical model was developed to systematically analyze the dynamic influence mechanisms of nozzle inlet pressure, flow rate, spray angle, and spray distance on cooling performance. Experimental results indicate that the system achieves maximum critical heat flux (CHF) and rail temperature drop at an inlet pressure of 0.5 MPa and a spray angle of 0°. Numerical simulations further reveal that a 45° spray cone angle simultaneously achieves the maximum temperature drop and optimal wall temperature uniformity. Key parameter sensitivity analysis demonstrates that while increasing spray distance leads to larger droplet diameters, the minimal droplet velocity decay combined with a significant increase in overall momentum markedly enhances convective heat transfer efficiency. Concurrently, increasing spray distance effectively improves rail surface temperature uniformity by optimizing the spatial distribution of droplet size and velocity. Full article

Plastic waste, particularly polyethylene terephthalate (PET), poses a growing environmental challenge. This study investigates the feasibility of incorporating recycled PET into clay bricks as a sustainable alternative in construction. Bricks were fabricated with 0%, 5%, 10%, and 15% PET content. Clay characterization included particle size distribution, Atterberg limits, and moisture content. Physical and mechanical tests evaluated dimensional variability, void percentage, warping, water absorption, suction, unit compressive strength ($f_b^{\prime }$), and prism compressive strength ($f_m^{\prime }$). Statistical analysis (Shapiro–Wilk, p < 0.05) validated the results. PET addition improved physical properties—reducing water absorption, suction, and voids—while slightly compromising mechanical strength. The 15% PET mix showed the best overall performance ($f_b^{\prime }$ = 24.00 kg/cm²; $f_m^{\prime }$ = 20.40 kg/cm²), with uniform deformation and lower absorption (18.7%). Recycled PET enhances key physical attributes of clay bricks, supporting its use in eco-friendly construction. However, reduced compressive strength limits its structural applications. Optimizing PET particle size, clay type, and firing conditions is essential to improve load-bearing capacity. Current formulations are promising for non-structural uses, contributing to circular material strategies. Full article

The long-term stability of grout-reinforced fractured rock masses in acidic groundwater environments after tunnel fires is critical for the safe operation of underground engineering. In this study, grouting reinforcement tests were performed on fractured diorite specimens using a high-strength fast-anchoring agent (HSFAA), and their mechanical degradation and microstructural evolution mechanisms were investigated under coupled high-temperature (25–1000 °C) and acidic corrosion (pH = 2) conditions. Multi-scale characterization techniques, including uniaxial compression strength (UCS) tests, X-ray computed tomography (CT), scanning electron microscopy (SEM), three-dimensional (3D) topographic scanning, and X-ray diffraction (XRD), were employed systematically. The results indicated that the synergistic thermo-acid interaction accelerated mineral dissolution and induced structural reorganization, resulting in surface whitening of specimens and decomposition of HSFAA hydration products. Increasing the prefabricated fracture angles (0–60°) amplified stress concentration at the grout–rock interface, resulting in a reduction of up to 69.46% in the peak strength of the specimens subjected to acid corrosion at 1000 °C. Acidic corrosion suppressed brittle disintegration observed in the uncorroded specimens at lower temperature (25–600 °C) by promoting energy dissipation through non-uniform notch formation, thereby shifting the failure modes from shear-dominated to tensile-shear hybrid modes. Quantitative CT analysis revealed a 34.64% reduction in crack volume (V_ca) for 1000 °C acid-corroded specimens compared to the control specimens at 25 °C. This reduction was attributed to high-temperature-induced ductility, which transformed macroscale crack propagation into microscale coalescence. These findings provide critical insights for assessing the durability of grouting reinforcement in post-fire tunnel rehabilitation and predicting the long-term stability of underground structures in chemically aggressive environments. Full article

In greenhouse and plant factory production, improper design of the ventilation system and increasing scales will lead to a stagnant airflow zone, which could inhibit plant growth and induce physiological disease, such as tipburn. To increase the airflow within the plant canopy, simplify the equipment complexity, and improve operation convenience, a cultivation system was designed to provide a constant airflow within the plant canopy by integrating ventilation ducts with cultivation tanks. A three-dimensional computational fluid dynamics (ANSYS Fluent 2021R2) model was developed and validated through simulating the airflow distribution within the plant canopy under different intake air velocities. According to the simulated results, an intake air velocity of 10 m s⁻¹ showed better airflow uniformity, and the proportion of the suitable zone reached the highest value of 83% at an intake air velocity of 20 m s⁻¹. To validate the practical effectiveness of cultivation, a cultivation experiment was conducted. Five different canopy air velocities were set at 0 (CK), 0.35 (T₁), 0.5 (T₂), 0.65 (T₃), and 0.8 (T₄) m s⁻¹, respectively. The results showed that the photosynthetic and transpiration rate, as well as the fresh and dry weights of lettuce plants (Lactuca sativa cv. ‘Tiberius’), increased by 17.8%, 21.7%, 29.6%, and 29.9%, respectively, under treatment T₄ compared to those under the control, while the canopy air temperature and relative humidity decreased by 1.3 °C and 3.2%, respectively. The above results indicate that the newly designed cultivation system can be considered an effective system for improving lettuce plant growth and its canopy environment. Full article

Titanium dioxide (TiO₂), owing to its excellent photocatalytic performance and environmental friendliness, holds great potential in the remediation of water pollution. In this study, we introduce a green and facile strategy to fabricate TiO₂-based hybrid aerogels, in which the peptide FQFQFIFK first self-assembles into peptide nanofibers (PNFs), followed by in situ biomineralization of TiO₂ on the PNFs. The TiO₂-loaded PNFs are then combined with graphene oxide (GO) via π–π interactions and integrated with microcrystalline cellulose (MCC) to construct a stable three-dimensional (3D) porous framework. The resulting GO/MCC/PNFs-TiO₂ aerogels exhibit high porosity, low density, and good mechanical stability. Photocatalytic experiments show that the aerogels efficiently degrade various organic dyes (methylene blue, rhodamine B, crystal violet, and Orange II) and antibiotics (e.g., tetracycline) under visible-light irradiation, achieving final degradation efficiencies higher than 90%. The excellent performance is attributed to the synergistic effect of the ordered interface provided by the PNF template, the stabilization and uniform dispersion facilitated by GO, and the mechanically robust 3D scaffold constructed by MCC. This work provides an efficient and sustainable strategy for designing functional hybrid aerogels and lays a foundation for their application in water treatment and environmental remediation. Full article

Silicon carbide (SiC) is a promising semiconductor material for electronics and photonics. Ultrafast laser processing of SiC enables three-dimensional nanostructuring, enriching and expanding the functionalities of SiC devices. However, challenges arise in delivering uniform, high-aspect-ratio (length-to-width) nanostructures due to difficulties in confining light energy at the nanoscale while simultaneously regulating intense photo modifications. In this study, we report the controllable growth of long-distance, high-straightness, and high-parallelism multifilament structures in SiC using ultrafast laser processing. The mechanism is the formation of femtosecond multifilaments through the nonlinear effects of clamping equilibrium, which allow highly confined light to propagate without diffraction in parallel channels, further inducing high-aspect-ratio nanostripe-like photomodifications. By employing an elliptical Gaussian beam—rather than a circular one—and optimizing pulse durations to stabilize multifilaments with regular positional distributions, the induced multifilament structures can reach a length of approximately 90 μm with a minimum linewidth of only 28 nm, resulting in an aspect ratio of over 3200:1. Raman tests indicate that the photomodified regions consist of amorphous SiC, amorphous silicon, and amorphous carbon, and photoluminescence tests reveal that silicon vacancy color centers could be induced in areas with lower light power density. By leveraging femtosecond multifilaments for diffraction-less light confinement, this work proposes an effective method for manufacturing deep-subwavelength, high-aspect-ratio nanostructures in SiC. Full article

Accurate recognition of estrus behavior in sows is of great importance for achieving scientific breeding management, improving reproductive efficiency, and reducing labor costs in modern pig farms. However, due to the evident spatiotemporal continuity, stage-specific changes, and ambiguous category boundaries of estrus behaviors, traditional methods based on static images or manual observation suffer from low efficiency and high misjudgment rates in practical applications. To address these issues, this study follows a video-based behavior recognition approach and designs three deep learning model structures: (Convolutional Neural Network combined with Long Short-Term Memory) CNN + LSTM, (Three-Dimensional Convolutional Neural Network) 3D-CNN, and (Convolutional Neural Network combined with Temporal Convolutional Network) CNN + TCN, aiming to achieve high-precision recognition and classification of four key behaviors (SOB, SOC, SOS, SOW) during the estrus process in sows. In terms of data processing, a sliding window strategy was adopted to slice the annotated video sequences, constructing image sequence samples with uniform length. The training, validation, and test sets were divided in a 6:2:2 ratio, ensuring balanced distribution of behavior categories. During model training and evaluation, a systematic comparative analysis was conducted from multiple aspects, including loss function variation (Loss), accuracy, precision, recall, F1-score, confusion matrix, and ROC-AUC curves. Experimental results show that the CNN + TCN model performed best overall, with validation accuracy exceeding 0.98, F1-score approaching 1.0, and an average AUC value of 0.9988, demonstrating excellent recognition accuracy and generalization ability. The 3D-CNN model performed well in recognizing short-term dynamic behaviors (such as SOC), achieving a validation F1-score of 0.91 and an AUC of 0.770, making it suitable for high-frequency, short-duration behavior recognition. The CNN + LSTM model exhibited good robustness in handling long-duration static behaviors (such as SOB and SOS), with a validation accuracy of 0.99 and an AUC of 0.9965. In addition, this study further developed an intelligent recognition system with front-end visualization, result feedback, and user interaction functions, enabling local deployment and real-time application of the model in farming environments, thus providing practical technical support for the digitalization and intelligentization of reproductive management in large-scale pig farms. Full article

The irregular and highly non-uniform spatial distribution inherent to dynamic three-dimensional (3D) point clouds (DPCs) severely hampers the extraction of reliable temporal context, rendering inter-frame compression a formidable challenge. Inspired by two-dimensional (2D) image and video compression methods, existing approaches attempt to model the temporal dependence of DPCs through a motion estimation/motion compensation (ME/MC) framework. However, these approaches represent only preliminary applications of this framework; point consistency between adjacent frames is insufficiently explored, and temporal correlation requires further investigation. To address this limitation, we propose a hierarchical ME/MC framework that adaptively selects the granularity of the estimated motion field, thereby ensuring a fine-grained inter-frame prediction process. To further enhance motion estimation accuracy, we introduce a dual-attention-based KNN block-matching (DA-KBM) network. This network employs a bidirectional attention mechanism to more precisely measure the correlation between points, using closely correlated points to predict inter-frame motion vectors and thereby improve inter-frame prediction accuracy. Experimental results show that the proposed DPC compression method achieves a significant improvement (gain of 70%) in the BD-Rate metric on the 8iFVBv2 dataset. compared with the standardized Video-based Point Cloud Compression (V-PCC) v13 method, and a 16% gain over the state-of-the-art deep learning-based inter-mode method. Full article

Soundscape monitoring has become an increasingly important tool for studying ecological processes and supporting habitat conservation. While many recent advances focus on identifying species through supervised learning, there is growing interest in understanding the soundscape as a whole while considering patterns that extend beyond individual vocalizations. This broader view requires unsupervised approaches capable of capturing meaningful structures related to temporal dynamics, frequency content, spatial distribution, and ecological variability. In this study, we present a fully unsupervised framework for analyzing large-scale soundscape data using deep learning. We applied a convolutional autoencoder (Soundscape-Net) to extract acoustic representations from over 60,000 recordings collected across a grid-based sampling design in the Rey Zamuro Reserve in Colombia. These features were initially compared with other audio characterization methods, showing superior performance in multiclass classification, with accuracies of 0.85 for habitat cover identification and 0.89 for time-of-day classification across 13 days. For the unsupervised study, optimized dimensionality reduction methods (Uniform Manifold Approximation and Projection and Pairwise Controlled Manifold Approximation and Projection) were applied to project the learned features, achieving trustworthiness scores above 0.96. Subsequently, clustering was performed using KMeans and Density-Based Spatial Clustering of Applications with Noise (DBSCAN), with evaluations based on metrics such as the silhouette, where scores above 0.45 were obtained, thus supporting the robustness of the discovered latent acoustic structures. To interpret and validate the resulting clusters, we combined multiple strategies: spatial mapping through interpolation, analysis of acoustic index variance to understand the cluster structure, and graph-based connectivity analysis to identify ecological relationships between the recording sites. Our results demonstrate that this approach can uncover both local and broad-scale patterns in the soundscape, providing a flexible and interpretable pathway for unsupervised ecological monitoring. Full article