Search Results (400)

Grinding surface texture is random and feature information is weak, so it is difficult to extract effective features by deep learning network. In addition, the existing deep learning methods mostly adopt a large parameter model in grinding surface roughness recognition task, and the cost of deployment in embedded end is high. In order to solve these problems, a new lightweight network model GE-MobileNet (Ghost-ECA-MobileNetV3) is proposed. Based on MobileNetV3, a feature extractor is introduced into the shallow network part of the model to enhance the ability of the network to extract and suppress the surface texture feature and noise. At the same time, SE (Squeeze-and-Excitation) attention mechanism is replaced with ECA (Efficient Channel) attention mechanism with stronger performance. Finally, the deep network layer is removed to reduce the model size. The experimental results show that the accuracy of GE-MobileNet-based grinding surface roughness measurement model on test set is 94.97%, which is better than other networks. This study proves the effectiveness of the roughness measurement method based on GE-MobileNet. Full article

Space-based optical imaging detection serves as a crucial means for acquiring characteristic information of space objects, with the quality and resolution of images directly influencing the accuracy of subsequent missions. Addressing the scarcity of datasets in space-based optical imaging, this study introduces a method that combines an improved Phong model and higher-order spherical harmonics (HOSH) for the optical imaging computation of space objects. Utilizing HOSH to fit the light field distribution, this approach comprehensively considers direct sunlight, earthshine, reflected light from other extremely distant celestial bodies, and multiple scattering from object surfaces. Through spectral reflectance experiments, an improved Phong model is developed to calculate the optical scattering characteristics of space objects and to retrieve common material properties such as metallicity, roughness, index of refraction (IOR), and Alpha for four types of satellite surfaces. Additionally, this study designs two sampling methods: a random sampling based on the spherical Fibonacci function (RSSF) and a sequential frame sampling based on predefined trajectories (SSPT). Through numerical analysis of the geometric and radiative rendering pipeline, this method simulates multiple scenarios under both high-resolution and wide-field-of-view operational modes across a range of relative distances. Simulation results validate the effectiveness of the proposed approach, with average rendering speeds of 2.86 s per frame and 1.67 s per frame for the two methods, respectively, demonstrating the capability for real-time rapid imaging while maintaining low computational resource consumption. The data simulation process spans six distinct relative distance intervals, ensuring that multi-scale images retain substantial textural features and are accompanied by attitude labels, thereby providing robust support for algorithms aimed at space object attitude estimation, and 3D reconstruction. Full article

Objective: The purpose of this study was to compare the flexural strength, flexural modulus, hardness, and surface roughness of one brand each of 3D-printed and heat-cured acrylic resin materials after they were immersed in various disinfection solutions. Methods: The study included 160 specimens, consisting of 80 heat-cured and 80 3D-printed specimens. Forty specimens of each resin material type were prepared for flexural testing, while an additional forty specimens were designated for hardness and surface roughness assessments. Each collection of 40 specimens was subsequently randomized into four subgroups (n = 10) for immersion in either distilled water (control), 1% sodium hypochlorite, Superdent, or Kin Oro denture cleansers. Flexural test, hardness, and surface roughness assessments were performed. Data analysis was conducted using SPSS, with a level of significance set at p < 0.05. Results: Flexural strength and surface roughness did not differ significantly between the two resin types. Flexural modulus was significantly higher in the heat-cured resin among all the disinfectants (p = 0.000). The heat-cured resin had significantly higher microhardness than the 3D-printed resin among the disinfectants except for the Kin Oro group, and both resins showed a significant reduction in hardness after immersion in disinfectants compared to distilled water (p < 0.05). Conclusions: The heat-cured resin demonstrated higher flexural modulus and surface hardness compared to the 3D-printed resin. Flexural strength and surface roughness were comparable between the two materials. Both resins had their highest mechanical properties in distilled water. Full article

The role of wall roughness in heat and mass transfer for fully developed viscous flows in square microchannels is investigated here. Since the roughness, which is the key geometrical feature to be investigated, introduces high velocity gradients at the wall, the effect of the viscous dissipation is considered. A fully developed flow in the forced convection regime is assumed. This assumption allows the two-dimensional treatment of the problem; thus, the velocity and temperature fields are simulated on the microchannel cross-section. The boundary roughness is modeled by randomly throwing points around the nominal square cross-section perimeter and by connecting those points to generate a simple polygon. This modification of the nominal square shape of the cross-section influences the velocity and temperature fields, which are computed by employing a finite element method solver. The heat and mass transfer is studied by calculating the Nusselt and the Poiseuille numbers as a function of roughness amplitude at the boundary. Each Nusselt and Poiseuille number is obtained by employing an averaging procedure over a sample of a thousand cases. Full article

This study investigates the optimization of electric discharge machining (EDM) parameters for gunmetal using copper electrodes in two different dielectric environments, which are conventional EDM oil and EDM oil infused with Al₂O₃ nanoparticles. A Taguchi L27 orthogonal array design was used to evaluate the effects of current, voltage, and pulse-on time on Material Removal Rate (MRR), Electrode Wear Rate (EWR), and surface roughness (Ra, Rq, and Rz). Analysis of Variance (ANOVA) was used to statistically evaluate the influence of each parameter on machining performance. In addition, machine learning models including Linear Regression, Ridge Regression, Support Vector Regression, Random Forest, Gradient Boosting, and Neural Networks were implemented to predict performance outcomes. The originality of this research is not only rooted in the introduction of new models; rather, it is also found in the comparative analysis of various machine learning methodologies applied to the performance of electrical discharge machining (EDM) utilizing Al₂O₃-enhanced dielectrics. This investigation focuses specifically on gunmetal, a material that has not been extensively studied within this framework. The nanoparticle-enhanced dielectric demonstrated improved machining performance, achieving approximately 15% higher MRR, 20% lower EWR, and 10% improved surface finish compared to conventional EDM oil. Neural Networks consistently outperformed other models in predictive accuracy. Results indicate that the use of nanoparticle-infused dielectrics in EDM, coupled with data-driven optimization techniques, enhances productivity, tool life, and surface quality. Full article

Magnesium alloys, like AZ31, possess a desirable low weight and high specific strength, which make them favorable for aerospace and auto applications, yet their difficulty to machine limits their broader implementation for the industry. Electrical discharge machining (EDM) is an effective technology for machining difficult-to-machine materials, particularly when the materials are reinforced with ceramic and graphene-based fillers. This study examines the impact of reinforcement percentage (R) and different electrical discharge machining (EDM) parameters such as current (I), pulse on time (T_on) and pulse off time (T_off) on the material removal rate (MRR) and surface roughness (SR) of AZ31/B₄C/GNPs composites. The combined reinforcement range varies from 2 wt.% to 4 wt.%. The Taguchi design (L27) is utilized to conduct the experiments in this study. ANOVA of the experimental data indicated that current (I) significantly affects MRR and SR, exhibiting the greatest contribution of 44.93% and 51.39% on MRR and SR, respectively, among the variables analyzed. The surface integrity properties of EDMed surfaces are examined using SEM under both higher and lower material removal rate settings. Diverse machine learning techniques, including linear regression (LR), polynomial regression (PR), Random Forest (RF), and Gradient Boost Regression (GBR), are employed to construct an efficient predictive model for outcome estimation. The built models are trained and evaluated using 80% and 20% of the total data points, respectively. Statistical measures (MSE, RMSE, and R²) are utilized to evaluate the performance of the models. Among all the developed models, GBR exhibited superior performance in predicting MRR and SR, achieving high accuracy (exceeding 92%) and lower error rates compared to the other models evaluated in this work. This work demonstrated the synergy between techniques in optimizing EDM performance for hybrid composites using a statistical design and machine learning strategies that will facilitate greater use of hybrid composites in high-precision engineering applications and advanced manufacturing sectors. Full article

Autonomous planetary rovers require robust path planning over rough 3D terrains, where traditional metrics such as path length, number of nodes, and planning time do not adequately capture path quality. Rapidly Exploring Random Trees (RRT) and its asymptotically optimal variant, RRT*, are widely used sampling-based algorithms for non-holonomic mobile robots but are limited when traversing uneven 3D terrain. This study proposes 3D-RRT*, a simplified, terrain-aware extension of Traversability-Based RRT*, designed to maintain high path quality while reducing planning time. The performance of 3D-RRT* is evaluated using metrics that are both practical and meaningful in the context of planetary rover path planning: path smoothness, path flatness, path length, and planning time. Exploration of a simulated Martian surface demonstrates that 3D-RRT* significantly improves path quality compared to standard RRT and RRT*, achieving smoother, safer, and more efficient routes for planetary rover missions. Full article

BioFlx crowns represent an innovative hybrid resin polymer-based alternative for pediatric full-coverage restorations, addressing the clinical dilemma between durable-but-unaesthetic stainless steel crowns (SSCs) and technique-sensitive zirconia crowns. This narrative review synthesizes current evidence of BioFlx crowns’ mechanical properties, clinical performance, and material characteristics through a comprehensive literature search across PubMed, Scopus, and Web of Science from August through September 2025. The search identified 18 studies comprising four randomized controlled trials, two case reports/series, and twelve in vitro studies. In vitro analyses demonstrated favorable stress distribution under physiological loads (≤311 N) with notable brand-dependent performance variations. NuSmile BioFlx exhibited greater wear than zirconia, but superior wear resistance compared to SSCs, while Kids-e-Dental BioFlx crowns demonstrated less crown wear relative to zirconia, with both brands causing less antagonist wear than zirconia. BioFlx showed intermediate fracture resistance, comparable surface roughness to SSCs but higher than zirconia, and intermediate marginal gaps. Resin cements demonstrated superior retention compared to manufacturer-recommended glass ionomer and resin-modified glass ionomer cements. Clinical studies with a 12 month follow-up demonstrated 92–98% retention rates compared to 100% for SSCs, with significantly higher patient satisfaction and reduced plaque accumulation versus SSCs. However, a failure rate of 6.7% was observed. Color change values were lower than those of zirconia crowns; however, they remained clinically unacceptable (ΔE > 3.3), and stain resistance was lower than that of SSCs. Marginal integrity remained clinically acceptable, though some anatomic form deterioration occurred over time. Case reports highlighted clinical utility in nickel-allergic patients and for masking silver diamine fluoride discoloration. BioFlx crowns represent a clinically valuable esthetic alternative in pediatric dentistry, though evidence remains limited by recent market introduction, brand-specific performance variations (NuSmile vs. Kids-e-Dental), anterior tooth applicability constraints, and contraindications in bruxism and for the Hall technique. Future randomized controlled trials with ≥2 year follow-up periods are imperative to establish long-term performance. Until such evidence emerges, BioFlx crowns represent a viable clinical option for esthetically sensitive cases and nickel-allergic patients when applied with rigorous case selection. Full article

As smart cities evolve, integrating new technologies into school transportation is becoming increasingly important to ensure student comfort and safety. Monitoring and enhancing comfort during daily commutes can significantly influence well-being and learning readiness. However, most existing research addresses isolated factors, which limits the development of comprehensive and scalable solutions. This study presents the design and implementation of a low-cost, generalized IoT-based system for monitoring comfort in school transportation. The system processes multiple environmental and operational signals, and these data are transmitted to a cloud computing platform for real-time analysis. Signal processing incorporates standardized metrics, such as root mean square (RMS) values from ISO 2631-1 for vibration assessment. In addition, machine learning techniques, including a Random Forest classifier and ensemble-based models, are applied to classify ride comfort levels using both road roughness and environmental variables. The results show that stacked multisensor fusion achieved a significant improvement in classification performance compared with vibration-only models. The platform also integrates route visualization with commuting time per student, providing valuable information to assess the impact of travel duration on school mobility. Full article

In this paper, the vibration of a wheel running on a light rail equipped with rail dampers that use a mixed damping system (rubber–oil) is investigated under the excitation of random roughness on the rolling surfaces, to demonstrate the influence of such rail dampers on the dynamic behaviour at the wheel–rail interface. For this purpose, a model is adopted in which a rigid wheel moves at constant speed over a rail modelled as an infinite Timoshenko beam, supported by elastic foundations with an internal degree of freedom that represents the behaviour of the rail pads, sleepers, and ballast. The rail dampers are represented as two-mass oscillators, while the internal friction in the elastic components of the wheel–rail system is modelled using hysteretic damping. To obtain the time series of the rail and wheel displacements, as well as the wheel–rail contact force, the convolution theorem is applied in a heuristic manner, making use of the relationship between Green’s functions in the time and frequency domains through direct and inverse Fourier transforms. The results show that (a) rail dampers primarily affect rail dynamics and the wheel–rail contact force over a relatively wide frequency range, while having little influence on wheel motion; (b) rail dampers are highly effective in reducing rail vibration and the wheel–rail contact force when the rail pads are stiff, but considerably less effective when soft rail pads are used; and (c) they may slightly amplify the contact force at the lower edge of their effective frequency range. Full article

Coastal vegetated ecosystems, including tidal marshes, vegetated dunes, and shrub- and forest-dominated wetlands, can mitigate hurricane impacts such as coastal flooding and erosion by increasing surface roughness and reducing wave energy. Land cover maps can be used as input to improve simulations of surface roughness in advanced hydro-morphological models. Consequently, there is a need for efficient tools to develop up-to-date land cover maps that include the accurate distribution of vegetation types prior to an extreme storm. In response, we developed the RUSH tool (Rapid remote sensing Updates of land cover for Storm and Hurricane forecast models). RUSH delivers high-resolution maps of coastal vegetation for near-real-time or historical conditions via a Jupyter Notebook application and a graphical user interface (GUI). The application generates 3 m spatial resolution land cover maps with classes relevant to coastal settings, especially along mainland beaches, headlands, and barrier islands, as follows: (1) open water; (2) emergent wetlands; (3) dune grass; (4) woody wetlands; and (5) bare ground. These maps are developed by applying one of two seasonal random-forest machine learning models to Planet Labs SuperDove multispectral imagery. Cool Season and Warm Season Models were trained on 665 and 594 reference points, respectively, located across study regions in the North Carolina Outer Banks, the Mississippi Delta in Louisiana, and a portion of the Florida Gulf Coast near Apalachicola. Cool Season and Warm Season Models were tested with 666 and 595 independent points, with an overall accuracy of 93% and 94%, respectively. The Jupyter Notebook application provides users with a flexible platform for customization for advanced users, whereas the GUI, designed with user-experience feedback, provides non-experts access to remote sensing capabilities. This application can also be used for long-term coastal geomorphic and ecosystem change assessments. Full article

Soil–structure contacts often govern deformation and stability in foundations and buried infrastructure. Rubber waste is used in soil mixtures to enhance geotechnical performance and promote environmental sustainability. This study investigates the peak and residual shear strength of sand–steel interfaces, where the sand is mixed with recycled rubber. It also develops predictive machine learning (ML) models based on the experimental data. Two silica sands, medium and coarse, were mixed with two rubber gradations; however, Rubber B was included only in limited comparative tests at a fixed content. Ring-shear tests were performed against smooth and rough steel plates under normal stresses of 25 to 200 kPa to capture the full τ–δ response. Nine input variables were considered: median particle size (D₅₀), regularity index (RI), porosity (n), coefficients of uniformity (C_u) and curvature (C_c), rubber content (RC), applied normal stress (σ_n), normalised roughness (R_n), and surface hardness (HD). These variables were used to train multiple linear regression (MLR) and random forest regression (RFR) models. The models were trained and validated on 96 experimental data points derived from ring-shear tests across varied material and loading conditions. The machine learning models facilitated the exploration of complex, non-linear relationships between the input variables and both peak and residual interfacial shear strength. Experimental findings demonstrated that particle size compatibility, rubber content, and surface roughness significantly influence interface behaviour, with optimal conditions varying depending on the surface type. Moderate inclusion of rubber was found to enhance strength under certain conditions, while excessive content could lead to performance reduction. The MLR model demonstrated superior generalisation in predicting peak strength, whereas the RFR model yielded higher accuracy for residual strength. Feature importance analyses from both models identified the most influential parameters governing the shear response at the sand–steel interface. Full article

To address the challenge of predicting lubrication states and reliability caused by the uncertainty of gear materials and structural parameters, a lubrication reliability analysis method considering the randomness of gear parameters is proposed. Firstly, a nonlinear dynamic model of a gear pair is established to derive the dynamic meshing force. The geometric and kinematic analyses are then performed to determine time-varying equivalent curvature radius and entrainment velocity. The minimum film thickness during meshing is further calculated. Considering gear parameters as random variables, a gear lubrication reliability model is formulated. Monte Carlo Simulation method is employed to accurately analyze the dynamic response, dynamic meshing force, equivalent curvature radius, entrainment velocity, probability distribution of minimum film thickness, and gear lubrication failure probability. Additionally, a specialized wear test device is designed to investigate the evolution of tooth surface roughness with wear and to forecast trends in gear lubrication failure probability as wear progresses. The results indicate that the uncertainty in gear parameters have minimal impact on the equivalent curvature radius and entrainment velocity, but significantly affect the dynamic meshing force. The gear speed and root mean square roughness are critical factors affecting lubrication reliability, and the early wear of the teeth enhances the lubrication reliability. The present work provides valuable insights for the design, maintenance, and optimization of high-performance gear systems in practical engineering applications. Full article

Sea surface roughness (SSR) retrieval is a frontier topic in the field of ocean remote sensing, and SSR retrieval based on multi angle, passive, visible spectrum remote sensing images has been proven to have potential applications. Traditional multi angle retrieval models ignored the nonlinear relationship between radiation and digital signals, resulting in low accuracy in SSR retrieval using visible spectrum remote sensing images. Therefore, we analyze the transmission characteristics of signals and random noise in sea surface imaging, establish signals and noise transmission models for typical sea surface imaging visible spectrum remote sensing systems using Complementary Metal Oxide Semiconductor (CMOS) and Time Delay Integration-Charge Coupled Device (TDI-CCD) sensors, and propose a model for SSR retrieval using multi angle passive visible spectrum remote sensing images. The proposed model can effectively suppress the noise behavior in the imaging link and improve the accuracy of SSR retrieval. Simulation experiments show that when simulating the retrieval of multi angle visible spectrum images obtained using CMOS or TDI-CCD imaging systems with four SSR levels of 0.02, 0.03, 0.04, and 0.05, the proposed model relative errors using two angles are decreased by 4.0%, 2.7%, 2.3%, and 2.0% and 6.5%, 4.3%, 3.7%, and 3.2%, compared with the relative errors of the model without considering noise behavior, which are 7.0%, 6.7%, 7.8%, and 9.0% and 9.5%, 8.3%, 9.0%, and 10.2%. When using more fitting data, the relative errors of the model were decreased by 5.0%, 2.7%, 2.5%, and 2.0% and 7.0%, 5.0%, 4.3%, and 3.2%, compared with the relative errors of the model without considering noise behavior, which are 8.5%, 7.0%, 8.0%, and 9.4%, and 10.0%, 8.7%, 9.3%, and 10.0%. Full article

Background/Objectives: Self-adhesive flowable resins (SAFR) entered the market, eliminating the adhesive system application due to their self-adhesive technology. Guided by the PICO framework (Population, Intervention, Comparison, Outcome), the aim was to conduct a systematic review of clinical studies to compare the clinical performance of Self Adhesive Flowable Resin (SAFRs) with conventional flowable resins used for direct restorations. Methods: The protocol of this systematic review was registered in the International Prospective Register of Systematic Reviews (CRD42023394297) and followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guideline. Five databases (PubMed, Embase, Web of Science, Scopus, and Cochrane Library) were searched from inception to July 2025. Nine randomized clinical trials were included, totaling 493 restorations in 232 patients. Clinical performance was assessed using USPHS or FDI criteria, with follow-up periods ranging from 6 months to 5 years. Data were pooled using a random-effects meta-analysis to calculate risk differences (RD) and 95% confidence intervals (CI) for marginal adaptation, retention, marginal staining, post-operative sensitivity, color stability, surface roughness, secondary caries, and anatomical form. Results: Meta-analysis showed no significant differences between SAFRs and CFRCs for in terms of: marginal adaptation (RD = 0.01; 95% CI: −0.02 to 0.04; p = 0.53; I² = 0%), retention (RD = 0.00; 95% CI: −0.02 to 0.03; p = 0.81; I² = 0%), marginal staining (RD = 0.01; 95% CI: −0.01 to 0.02; p = 0.51; I² = 0%), and post-operative sensitivity (RD = −0.01; 95% CI: −0.03 to 0.02; p = 0.62; I² = 0%). The certainty of the evidence for all outcomes was rated as moderate to high according to the GRADE assessment. Conclusions: SAFR restorations demonstrated comparable clinical performance to conventional resins; however, heterogeneity in follow-up duration and the scarcity of long-term data (>5 years) warrant caution. Full article