Search Results (2,589)

TiO₂ nanotubes were synthesized using the anodization method on Ti foils and decorated with PbS nanoparticles by the SILAR method at different cycle numbers (10, 15, 20, 25, and 30). These samples were characterized using SEM, TEM, XRD, and microhardness tests. Morphologically, the PbS nanoparticles were evenly dispersed on TiO₂ nanotubes in the shape of small spheres. With an increase in the number of cycles, the size and shape of the nanoparticles increased. This also affected the structure and crystallinity of the PbS NPs, as the crystallite size of PbS increased. The in-depth analysis of the tribological characteristics of the coatings conducted using the scratch test allowed us to evaluate the adhesion of the coatings, a crucial aspect in determining their effectiveness and durability. Furthermore, we found that the wear resistance of the coatings increased with the number of PbS cycles up to 15 cycles. However, for the samples with higher size distribution and crystallite size, such as those with more than 15 cycles, the microhardness continued to decrease. This indicates that the addition of PbS can improve the durability of TiO₂ coatings, making them a potential candidate for advanced surface coatings in demanding engineering applications. Electrochemical measurements were conducted to assess the corrosion resistance of the samples. The electrochemical impedance spectra (EIS) results revealed that the PbS/TiO₂ coatings with 15 deposition cycles exhibited the most effective corrosion resistance, with a dense and uniform distribution of PbS nanoparticles forming a compact barrier that effectively protects against corrosion. The charge transfer resistance (R_ct) and the absorption capacitance (Q_ab) values were higher for the 15-cycle sample (4.49 Ω·cm² and 0.9 Fsⁿ⁻¹cm⁻², respectively). Full article

Binder Jetting 3D Printing (BJ3DP) offers an effective pathway for the rapid fabrication of complex high-entropy alloy (HEA) components. In this study, the macroscopic characteristics, microstructural evolution and mechanical properties of Al_0.5Cr_0.9FeNi_2.5V_0.2 HEA green parts prepared via BJ3DP were investigated under various sintering conditions. Results showed that the relative density of the sintered parts increased significantly with temperature, transitioning from a low density (<90%) at 1300–1330 °C to near-fully dense (~98%) at 1340–1350 °C. Consequently, the mechanical properties were remarkably improved. The yield strength (σ_0.2) increased from 300 MPa to 710 MPa (a 136% increase), and the ultimate tensile strength (σ_b) rose from 310 MPa to 780 MPa (a 148% increase) as sintering temperature rose from 1300 °C to 1350 °C. Microstructural analysis revealed that at lower sintering temperatures, the alloy exhibited high porosity and a non-coherent structure composed of an FCC matrix and Cr-rich BCC phase, with Al/Ni intermetallic compounds distributed around pores. Conversely, at the final sintering stage, pore closure was achieved, and a coherent structure consisting of an FCC matrix and scale-like L1₂ precipitates was formed. Optimal mechanical properties (tensile strength ≥ 700 MPa) were achieved when sintering at 1340 °C, primarily attributed to densification and precipitation strengthening. Full article

The wide-scale deployment of radars, distributed across a platform and across multiple platforms for reliable 360° situational awareness (SA), introduces the challenge of radar interference. Interference can broadly be categorised as self-interference (between radars mounted on the same platform) and mutual interference (signals received from radars on other platforms). Both types of interference impede the reliability of SA delivered by such systems, particularly in dense environments where numerous radars operate simultaneously within the same frequency band. This work presents a comprehensive evaluation of a multi-modal beamforming approach that combines unfocused synthetic aperture radar with the traditional Multiple-Input, Multiple-Output beamformer to enhance radar resolution and suppress interference. Additionally, various aspects of sensor configurations defining hardware and software capabilities of state-of-the-art radars are discussed, and a systematic analysis of signal-to-interference-plus-noise ratio at each step of the processing is presented. Extensive simulations and experimental results in both automotive and maritime environments are shown to validate the effectiveness of the proposed approach. Full article

Ammonia decomposition represents a promising route for carbon-free hydrogen production, provided that efficient and cost-effective catalysts are developed. In this study, lanthanum-based mixed oxide catalysts (LaNi, LaCo, and LaCe) were synthesized via a controlled co-precipitation method and systematically evaluated for catalytic ammonia decomposition under atmospheric pressure in the temperature range of 350–500 °C. Comprehensive characterization combining N₂ physisorption, XRD, SEM–EDX, TGA–DTG, XPS, and FTIR-pyridine adsorption revealed pronounced structure–property relationships. LaNi exhibited the highest surface area (31.11 m²·g⁻¹), well-developed mesoporosity, and a balanced Lewis/Brønsted acidity (C_L/C_B ≈ 0.82), leading to superior catalytic performance with NH₃ conversion reaching ~48% at 500 °C (GHSV = 50 h⁻¹). LaCo showed intermediate activity (~30% conversion), while LaCe displayed limited performance (<13%), most likely due to its dense morphology and low surface accessibility. Increasing gas hourly space velocity resulted in decreased ammonia conversion for all catalysts, highlighting the critical role of residence time. These findings demonstrate that the catalytic efficiency of lanthanum-based systems is governed by the synergistic interplay between surface area, mesoporous architecture, and acidity distribution, with LaNi emerging as the most promising catalyst among the investigated materials. Full article

With the expanding application of synthetic aperture radar (SAR) in ocean monitoring and port regulation, nearshore ship detection based on SAR image faces notable challenges arising from strong background scattering, dense target occlusion, and large pose variations. Therefore, this paper proposes a two-stage oriented detection network named EARS-Net to improve the accuracy of ship detection in complex nearshore environments. Specifically, a lightweight convolutional block attention module (CBAM) is embedded into the high-level semantic stages of ResNet50 to enhance discriminative ship features while suppressing interference from port infrastructures and shoreline structures. Then, the dynamic angle regression loss (DAL) is proposed, and the angle weight function is designed according to the ship direction distribution characteristics, which allocates higher regression weight to the ship target with larger tilt angle, improving the defect of insufficient positioning accuracy for large angle ships. Moreover, a training strategy that combines focal loss, multi-scale training, and rotated online hard example mining (ROHEM) is employed to alleviate sample imbalance and improve generalization in dense scenes. Experimental results on the nearshore subset of the SSDD show that EARS-Net achieves an average precision (AP) of 0.903 on the test set, demonstrating reliable detection capability under complex backgrounds and dense target distributions. These results validate the effectiveness of our method and highlight its potential as a practical engineering solution for enhancing port situational awareness and coastal security monitoring. Full article

The Taoping paleo-landslide poses a significant risk to local residents and critical infrastructure. However, traditional field surveys and deformation monitoring methods are often inadequate for capturing subtle, localized deformation characteristics—particularly at the head scarp and lateral margins—thereby limiting comprehensive assessments of slope instability. Surface strain data offer direct insights into internal stress redistribution during slope evolution and are essential for interpreting landslide mechanisms and forecasting failure. Given the current limitations in dense and wide-area strain monitoring technologies, this study proposes a novel method for modeling landslide strain fields based on Interferometric Synthetic Aperture Radar (InSAR) phase gradients. Using the phase gradient stacking approach, InSAR-derived phase gradients are transformed into strain-related parameters, enabling estimation of shear strain rates, principal strain rates, and their directional distributions. The application to the Taoping paleo-landslide reveals clear spatial patterns of compressive and tensile strain across the landslide body. Field investigations corroborate the InSAR-derived strain features through corresponding geomorphological evidence observed in both compressional and extensional zones. The proposed method enhances the understanding of landslide deformation behavior, supports evaluation of shear surface continuity and evolution, and offers a robust framework for early warning and risk mitigation in complex landslide-prone areas. Full article

As a key task in remote sensing analysis, semantic segmentation of remote sensing images (RSI) underpins many practical applications. Despite its importance, obtaining dense pixel-wise annotations remains labor-intensive and time-consuming. Unsupervised domain adaptation (UDA) offers a promising solution by utilizing knowledge from labeled source domains for unlabeled target domains, yet its effectiveness is often compromised by significant distribution shifts arising from variations in imaging conditions. To address this challenge, we propose a depth-aware adaptation network (DAAN), a novel two-branch network that explicitly leverages complementary depth information from a digital surface model (DSM) to enhance cross-domain remote sensing segmentation. Unlike conventional UDA methods that primarily focus on semantic features, DAAN incorporates depth data to build a more generalized feature space. This network introduces three key components: an adaptive feature aggregator (AFA) for progressive semantic-depth feature fusion, a gated prediction selection unit (GPSU) that selectively integrates predictions to mitigate the impact of noisy depth measurements, and misalignment-focused residual refinement (MFRR) module that emphasizes poorly aligned target regions during training. Experiments on the ISPRS and GAMUS datasets demonstrate the effectiveness of the proposed method. In particular, DAAN achieves an mIoU of 50.53% and an F1 score of 65.75% for cross-domain segmentation on ISPRS to GAMUS, outperforming models without depth information by 9.17% and 8.99%, respectively. These results demonstrate the advantage of integrating auxiliary geometric information to improve model generalization on unlabeled remote sensing datasets, contributing to higher mapping accuracy, more reliable automated analysis, and enhanced decision-making support. Full article

The evolution toward prospective sixth-generation (6G) wireless networks is expected to significantly increase user density, bandwidth demand, and architectural complexity, reinforcing the need for scalable multiple-input multiple-output (MIMO) deployments. In this context, two fundamentally different design strategies have emerged: scaling up centralized antenna arrays and scaling out distributed cooperative infrastructures. This paper presents a system-level comparative benchmark of scale-up and scale-out MIMO architectures under identical operating conditions of three representative downlink deployments: centralized Massive MIMO, centralized XL-Massive MIMO, and distributed Cell-Free MIMO. All architectures are assessed under identical urban channel conditions, transmit power, bandwidth, and traffic assumptions, considering sub-6 GHz (3.5 GHz) and millimeter-wave (28 GHz) frequency bands as proxies for 5G and prospective 6G operation. A unified Monte Carlo simulation framework is employed to jointly evaluate aggregate throughput, spectral efficiency, coverage performance, interference behavior, and energy efficiency over a wide range of user densities and service radii. The results highlight the distinct architectural trade-offs between centralized and distributed deployments: XL-Massive MIMO maximizes aggregate throughput and spatial reuse in dense hotspot scenarios, whereas Cell-Free MIMO provides superior coverage uniformity and improved energy efficiency in wide-area deployments. By isolating the impact of architectural scaling under consistent assumptions, the presented benchmark offers quantitative guidance for 6G network design and deployment planning. Full article

To address the challenge of surrounding rock instability in deep-buried tunnels crossing fractured fault zones, this study focuses on the Xigu Tunnel of the Lanzhou–Hezuo Railway. A combination of laboratory triaxial tests, an optimized multi-source advanced geological prediction workflow, and a site-specific parameter-weakened Mohr–Coulomb numerical simulation is employed to systematically reveal the physical–mechanical properties, spatial distribution, and deformation response of fractured rock masses under excavation-induced disturbance. The triaxial test results show that the average peak strength of the surrounding rock reaches 149.04 MPa; however, significant variability is observed among samples, and the failure mode exhibits a typical brittle–shear composite feature. The measured cohesion and internal friction angle are 20.57 MPa and 49.91°, respectively, indicating high intrinsic strength of individual rock blocks. Nevertheless, due to the presence of densely developed joints and crushed structures, the overall mass is loose and highly sensitive to dynamic disturbances such as blasting and excavation, revealing a unique mechanical paradox of high-strength rock blocks with low overall rock mass stability in deep-buried fractured zones. Joint TSP (Tunnel Seismic Prediction Ahead) and ground-penetrating radar (GPR) prediction reveals decreased P-wave velocity, increased Poisson’s ratio, and intensive seismic reflection interfaces; a quantitative index system for identifying the boundaries of narrow deep-buried fractured zones is proposed based on these geophysical characteristics. Combined with geological face mapping, these results confirm the existence of a highly fractured zone approximately 130 m in width, characterized by well-developed joints, heterogeneous mechanical properties, and localized risks of blockfall and groundwater ingress. The developed numerical model, with parameters weakened based on triaxial test and geological prediction data, effectively reproduces the deformation law of the fractured zone, and the simulation results agree well with field monitoring data, with peak displacement concentrated at section DK4 + 595, thus accurately identifying the center of the fractured belt as a key engineering validation result of the integrated technical framework. During construction, based on the identified spatial characteristics of the fractured zone and the proposed targeted support insight, enhanced dynamic monitoring and targeted support measures at the fractured zone center are required to ensure structural safety and long-term stability of the tunnel. This study develops an integrated engineering-oriented technical framework for deep-buried tunnels crossing narrow fractured zones, and provides novel mechanical insights and quantitative identification indices for such complex geological engineering scenarios. Full article

Firefighters must locate victims reliably to carry out rescue operations within burning structures during urban firefighting events. Low visibility, reduced oxygen levels, weakened structural rigidity, and dense smoke make it difficult to locate victims. In addition to these challenges, victims may be unconscious and unable to report their locations to firefighters. This research work explores the Double Deep Q-Network (Double DQN), Dueling Deep Q-Network (Dueling DQN), and Dueling Double Deep Q-Network (D3QN) agents for an unmanned aerial vehicle (UAV) to navigate around a structure and locate trapped victims within it. The UAV’s position, Light Detection and Ranging (LiDAR), and infrared camera data are utilized as inputs for the Deep Q-Networks. The PER is used to store transitions and sample them according to priority for training. Python’s Pygame library is used in this research to create a simulated environment in which infrared camera and LiDAR data are simulated. The performance of the UAV agent is evaluated using cumulative maximum reward, reward distribution histogram, Temporal Difference (TD) error over time, and number of successful episodes. Among the three DQN UAV agents, the Dueling DQN and Double DQN have potential for real-world applications in firefighting. Full article

The increasing demands of application conditions urgently call for process innovations in high-performance 7xxx aluminum alloys. This study investigated the effect of differential speed rolling (DSR) on the microstructural evolution and mechanical properties of 7075 aluminum alloy subjected to DSR with a total reduction of 60%, followed by isothermal aging at 120 °C for 24 h. The results show that DSR promotes the development of grain refinement, defect accumulation, and deformation texture, while the corresponding strengthening effect exhibits a non-monotonic dependence on speed ratio. Among all conditions, the DSR2.0 sample exhibits the most favorable microstructure, characterized by the highest kernel average misorientation (KAM) value, the strongest deformation texture, and the finest as well as most densely distributed intragranular η′ precipitates. Accordingly, the DSR2.0 sample achieves the optimal strength–ductility balance, with a yield strength, ultimate tensile strength, elongation, and hardness of 582.26 MPa, 648.43 MPa, 10.75%, and 199.8 HV, respectively. Specifically, the deterioration in the properties of the DSR2.5 sample is attributed to localized recovery, shear inhomogeneity and coarsening of precipitates. The differential speed ratio enables effective optimization of the 7075 aluminum alloy by regulating the evolution of grains, dislocations, precipitate phases, and texture, among which precipitation strengthening is the dominant calculated contribution. Therefore, an appropriate differential speed ratio is key to achieving performance optimization. Full article

The current study proposes an improved DenseNet-based Sequential Multimodal Biometric Authentication System, involving face and ear modality for better human identification. The architecture is composed of three convolutional layers and two dense layers, which are optimized for obtaining the discriminative spatial representations in 200 × 200 pixel facial and ear images. Evaluation is performed based on strict 5-fold subject disjoint cross-validation data to ensure the unbiased assessment. The model proposed attained a steady classification accuracy of 97.1 ± 0.79%, and balanced values for Precision, Recall and F1-score under controlled validation conditions, while the Performance analysis including False Acceptance (FAR), False Rejection (FRR) and Equal Error Rate (EER) showed that the EER found is around 1.05% at the optimum operating value. Comparative experiments between parallel feature concatenation and sequential verification techniques show that the sequential framework yields decreased FAR, when compared to the parallel framework, without having a detrimental effect on overall accuracy, while the Statistical validation by analysis of variance shows that the incremental architectural improvements have a significant impact on performance improvements. Findings of this analysis show a “score distribution” that both “single-trait and traditional multifactor systems” exceed the presentation of a novel method for Nex-G authentication solutions. This study advances biometric security by demonstrating how multimodal fusion may address the increasing global demand for robust and privacy-aware authentication methods, thereby setting a standard for intelligent multimodal recognition systems. Full article

In recent years, electric cargo (e-cargo) bikes have been increasingly adopted as a sustainable alternative for urban logistics and last-mile delivery, particularly in densely populated areas where traditional vehicles face traffic congestion and access limitations. This study aims to develop a representative driving cycle for e-cargo bikes based on real-world cycling data. An instrumented Long John-type e-cargo bike was used to collect naturalistic data from four different riders covering a total of 50 km along a predefined route in the city center of Florence, selected in collaboration with the Italian postal service provider (i.e., Poste Italiane) to reflect typical delivery operations. The driving cycle was generated using a Markov chain Monte Carlo (MCMC) method, modeling the stochastic transitions of vehicle speed and acceleration values. The resulting driving cycle, defined as the Florence cargo bike driving cycle (FCBDC), achieved an error of 2.1% on the Speed Acceleration Probability Distribution (SAPD) root sum square difference; although minor losses in peak acceleration values were observed due to data smoothing and discretization, the synthesized driving cycle effectively reproduces the dynamic characteristics of e-cargo bike riding. While the study is limited to a single route and is equivalent to simulated postman behavior, it provides valuable insights to guide the future development and optimization of e-cargo bikes for sustainable mobility operations. Full article

With the increasing use of reinforced concrete segments in large-scale tunnels, engineering projects have placed higher mechanical demands on concrete, and the choice of concrete materials significantly influences these mechanical properties. This study is based on the preliminary mix design for the concrete used in the Second Undersea Tunnel Project, with the mass content of nano-SiO₂ (NS) (1–3%), the volume content of steel fibers (SF) (0.5–1.5%) and the volume content of polypropylene fibers (PPF) (0.05–0.25%) as independent variables and using compressive strength (Y₁), splitting tensile strength (Y₂), and toughness index (Y₃) as response variables. Using the Box–Behnken response surface design method, response surface models for each parameter were established and analyzed. The effects of NS, SF, and PPF on the mechanical properties of the concrete were investigated. Combining the MOPSO algorithm and the entropy-weighted TOPSIS method, a multi-objective cooperative optimization study was conducted. Finally, a microstructural analysis of the optimal NSDHFRC was performed. The results indicate that Y₁, Y₂, and Y₃ all initially increase and then decrease with increasing NS content; Y₁ and Y₃ increase with increasing SF content. However, when the SF content exceeds a certain level, the fiber spacing becomes too dense, weakening the effective bridging effect between fibers, resulting in a decrease in Y₂ at excessively high SF contents; PPF can suppress crack formation within a certain content range, but its effect on Y₁ is relatively weak. Due to agglomeration and water absorption, both Y₂ and Y₃ decrease when the PPF content is too high. It was determined that the optimal solution occurs when the mass fraction of NS is 2.15%, and the volume fractions of SF and PPF are 1.37% and 0.063%, respectively, with Y₁, Y₂, and Y₃ being 69.94 MPa, 5.49 MPa, and 1.99, respectively. Experimental verification confirmed that the relative error is within 5%. A microscopic analysis of the optimal solution revealed that an appropriate amount of NS refines the concrete structure through physical and chemical reactions, improves the interface transition zone, and enhances the bond strength between the fibers and the matrix. Meanwhile, PPF and SF distribute stress, respectively delaying the propagation of microcracks and macrocracks during different loading stages. These findings provide a reference for practical engineering applications. Full article

Super-resolution microscopy has transformed biological imaging by enabling nanoscale visualization of cellular structures beyond the diffraction limit. However, its effective application in highly dense molecular environments still poses challenges. This is the case for 3D PhotoActivated Localization Microscopy (PALM) achieved through astigmatism in bacterial cells. The limited volume of a single bacterium highly increases the probability of the intensity profiles emitted by single chromophores to overlap, thus strongly decreasing the number of localizations, leading to dramatic undersampling. Dual-color 3D super-resolution in Escherichia coli is achieved through a combination of PALM with Expansion Microscopy (Ex-PALM). PALM provides high specificity through photoactivable (PA) fusion proteins and high localization precision, while ExM physically expands the specimen and separate densely packed molecules. This hybrid approach enables dual-color 3D single-molecule localization with about 3 nm spatial resolution, thus allowing one to measure distances down to the molecular scale. This is achieved by optimizing ExM protocols in bacteria to achieve a 4-fold isotropic expansion, by minimizing both chromatic aberrations and signal crosstalk, and by improving single-molecule sensitivity through highly selective inclined illumination. The method is applied to measure the spatial distribution of HisF and HisH proteins, involved in E. coli histidine biosynthesis. By tagging each protein with a photoactivable fluorescent protein, Ex-PALM reveals that after being synthetized, they co-localize in the bacterial volume with an average 3D distance of 19 nm. By combining labeling specificity with Ex-PALM, an effective method is developed for studying molecular organization in prokaryotes and in high-density samples in general, such as cell organelles or molecular condensates, with broad applications in microbiology, synthetic biology, and cellular biophysics. Full article