Search Results (50)

Xenodens calminechari is a highly derived mosasaurid from the latest Maastrichtian Phosphates of the Oulad Abdoun Basin, Morocco. Originally described based on a single maxilla, Xenodens differs from all known squamates in its closely packed, bladelike marginal teeth and modified tooth implantation and replacement. Xenodens’ relationships and anatomy remain poorly understood, and a recent study suggested that the holotype represents a composite, and furthermore that the animal might represent a juvenile of Carinodens. Evidence from a new referred specimen of Xenodens and CT scans corroborate the original description of Xenodens. Scans of the holotype and referred specimen of Xenodens reveal highly derived tooth implantation; interdental ridges are reduced in the posterior part of the jaw and teeth implant in a groove, with adjacent roots contacting and fusing. Tooth roots bear large, deep replacement pits, as is typical of derived mosasaurids, but in posterior teeth the replacement pits merge lingually to create a single large pit for two teeth. We provide an updated diagnosis of Xenodens, detailing unusual features of its tooth anatomy, implantation and replacement. Differences between Xenodens and Carinodens are numerous and no intermediate morphologies exist; furthermore, the size overlap between Carinodens and Xenodens indicates that Xenodens cannot represent a juvenile Carinodens. Xenodens highlights the remarkable diversity of mosasaurids, as well as the exceptional range of ecological niches occupied by this highly successful group of marine reptiles before their extinction. Full article

Dental enamel, the final product of amelogenesis, is a highly mineralized bioceramic that becomes acellular and non-regenerating after tooth eruption. This paper reviews literature that explores inorganic phosphate (Pi) transport during the process of enamel formation or amelogenesis. Evidence from transcriptomics, immunolocalization, and physiology implicates ameloblast-specific sodium-dependent Pi uptake by type III sodium–phosphate cotransporters SLC20A1 (PiT1) and SLC20A2 (PiT2), and by type IIb sodium–phosphate cotransporter SLC34A2 (NaPi-IIb) with stage-specific basal (proximal) or apical (distal) enrichment, and pH-dependent expression. Controlled Pi efflux to the enamel space has been partly attributed to xenotropic and polytropic retrovirus receptor (XPR1) mediated Pi export during maturation-stage amelogenesis. These amelogenesis-specific Pi fluxes operate within a polarized cellular framework in which Ca²⁺ delivery and extrusion, together with bicarbonate-based buffering regulated by cystic fibrosis transmembrane conductance regulator (CFTR), Solute carrier family 26 (SLC26) exchangers, anion exchanger 2 (AE2), and electrogenic sodium bicarbonate cotransporter 1 (NBCe1), at-least partially contribute to cellular Pi activity, and neutralize protons generated as the extracellular hydroxyapatite-based enamel matures. Disruption of phosphate handling reduces crystal growth and final mineral content of enamel, and produces hypomineralized or hypomature enamel with opacities, post-eruptive breakdown, and greater caries susceptibility. This review integrates multi-modal findings to appraise established features of ameloblast Pi handling, define constraints imposed by pH control and Ca²⁺ transport, and identify gaps in ion transporter topology and trafficking dynamics. Full article

Dental caries remains one of the most prevalent global diseases, and the early detection of occlusal lesions is critical because demineralization often begins deep within pits and fissures where conventional visual–tactile or radiographic inspection cannot detect it. SmarTooth, a newly introduced fluorescence device that irradiates enamel with a 655 nm laser and records the reflected intensity, may provide more objective, quantitative diagnoses. This study assessed its diagnostic performance against the International Caries Detection and Assessment System (ICDAS). We examined 1421 occlusal surfaces from 153 adults, scored each surface with ICDAS codes 0–4, and recorded SmarTooth peak values. Spearman’s rank correlation was used to test the association between codes and peak values; one-way ANOVA with Tukey’s post hoc was used to compare mean values across codes; and sensitivity, specificity, and the area under the receiver operating characteristic curve (AUROC) were calculated at three diagnostic thresholds: D1 (0 vs. 1–4), D2 (0–2 vs. 3–4), and D3 (0–3 vs. 4). The SmarTooth values rose with lesion severity and correlated moderately with ICDAS (r = 0.495, p < 0.001). The AUROC ranged from 0.69 to 0.82, with the best accuracy observed at D2 (cut-off: 7.0; AUC: 0.82; sensitivity: 78.3%; specificity: 77.4%). These findings suggest that SmarTooth can complement ICDAS scoring as an adjunctive tool, potentially enhancing diagnostic accuracy and supporting early intervention for occlusal caries in general dental practice. Full article

In the operation process of modern industrial equipment, as the core transmission component, the operation state of the gearbox directly affects the overall performance and service life of the equipment. However, the current gear operation is still faced with problems such as poor monitoring, a single detection index, and low data utilization, which lead to incomplete evaluation results. In view of these challenges, this paper proposes a shape and property integrated gearbox monitoring system based on digital twin technology and artificial intelligence, which aims to realize real-time fault diagnosis, performance prediction, and the dynamic visualization of gear through virtual real mapping and data interaction, and lays the foundation for the follow-up predictive maintenance application. Taking the QPZZ-ii gearbox test bed as the physical entity, the research establishes a five-layer architecture: functional service layer, software support layer, model integration layer, data-driven layer, and digital twin layer, forming a closed-loop feedback mechanism. In terms of technical implementation, combined with HyperMesh 2023 refinement mesh generation, ABAQUS 2023 simulates the stress distribution of gear under thermal fluid solid coupling conditions, the Gaussian process regression (GPR) stress prediction model, and a fault diagnosis algorithm based on wavelet transform and the depth residual shrinkage network (DRSN), and analyzes the vibration signal and stress distribution of gear under normal, broken tooth, wear and pitting fault types. The experimental verification shows that the fault diagnosis accuracy of the system is more than 99%, the average value of the determination coefficient (R²) of the stress prediction model is 0.9339 (driving wheel) and 0.9497 (driven wheel), and supports the real-time display of three-dimensional cloud images. The advantage of the research lies in the interaction and visualization of fusion of multi-source data, but it is limited to the accuracy of finite element simulation and the difficulty of obtaining actual stress data. This achievement provides a new method for intelligent monitoring of industrial equipment and effectively promotes the application of digital twin technology in the field of predictive maintenance. Full article

The large toothed double-roll crusher, as key crushing equipment for open pit mines, is very necessary to analyse its crushing performance at different feed particle sizes, compressive strengths of coal, and rotation speeds of toothed rollers. Firstly, a toothed double-roll crusher is taken as the research object in this paper; the coupling simulation model of the toothed double-roll crusher based on the DEM-MBD and Ab-T₁₀ breakage model is constructed. The validity of the coupling simulation model is verified through the actual measurement data. On this basis, the crushing performance under variable factors is analysed by integrating comprehensive tests. The results show that the rotation speed of the toothed roller is the main influence factor on the crushing performance of the toothed double-roll crusher when it works at 25~33.3 r/min. With the increase in compressive strength of coal, the productivity decreases, and this phenomenon disappears gradually at 33.3~42 r/min. Further, a 5–20% increase in the large size of the coal particles can improve 10% crushing quality with a discharge size lower than 300 mm and approximately 25% productivity of the toothed double-roll crusher. Finally, the power density is reduced as the mass percentage of large-sized coal particles increases, and this phenomenon is weakened with the increase in the compressive strength of coal. Full article

This paper studies the relevance of CWT (continuous wavelet transform) processing of vibration signals for improving the performance of CNN-based models that detect certain types of helical gearbox faults. Gear tooth damages, such as incipient and localized pitting and localized wear on helical pinion tooth flanks, combined with improper lubrication, are the faults under observation. Vibrations at the housing level for three rotating velocities of the AC motor and three load levels (for each velocity) are acquired with a triaxial accelerometer. Through CWT, the vibration signal is decomposed into 2D time-frequency grayscale images, with a filter bank of ten voices per octave in the frequency band of interest. Three 2D-CNN-based models trained on the CWT-based representation of the vibration signals measured on individual accelerometer axes (X, Y, and Z) are proposed to detect the four health states (one normal and three faulty) of the helical gearbox, regardless of the selected load level or speed on the test rig. These models achieve an accuracy higher than 99%. By fusing the CWT-based representations of the signals on individual axes for use as input to a 2D-CNN, the best-performing model for the proposed defect detection task is generated, reaching an accuracy of 99.91%. Full article

This study investigated the combined effects of adding niobium–fluoride (NbF₅) nanoparticles to a pit-and-fissure sealant. One resin sealant was reinforced with varying amounts of nanoparticles (0.3, 0.6, and 0.9 wt%). The surface hardness (SH), energy-dispersive X-ray spectroscopy (EDX), surface roughness (Ra), color change (ΔE), and mineral deposition were assessed. Bovine enamel blocks were subjected to demineralization and pH-cycling for SH. The elemental composition and Ca/P ratio were evaluated using EDX, while the mineral deposition was measured using Fourier transform infrared spectroscopy (FTIR). Data were analyzed using ANOVA and Tukey’s test for the SH and EDX, ΔE, and Kruskal–Wallis for the Ra. The NbF₅ modification increased the SH, with the 0.9 wt% sealant exhibiting higher SH values, and the 0.3 wt% one exhibiting significant differences compared to the control and the 0.9 wt% (p = 0.00) samples, even after pH-cycling. For the EDX analysis, the 0.3 and 0.6 wt% samples exhibited higher Ca/P ratios, with the 0.3% one showing evidence of P-O crystal formation. There was no significant difference in the Ra (p = 0.458), and the 0.6 and 0.9 wt% ones showed lower ΔE values compared to the control. The 0.3 wt% NbF₅ demonstrated improved overall properties, making these results particularly promising for preventing tooth decay, reducing demineralization through increased ions release and promoting remineralization in posterior teeth. Full article

The finishing mill is a critical link in the hot rolling process, influencing the final product’s quality, and even economic efficiency. The distribution box of the finishing mill plays a vital role in power transmission and distribution. However, harsh operating conditions can frequently lead to distribution box damage and even failure. To diagnose faults in the distribution box promptly, a fault diagnosis network model is constructed in this paper. This model combines depthwise separable convolution and Bi-LSTM. Depthwise separable convolution and Bi-LSTM can extract both spatial and temporal features from signals. This structure enables comprehensive feature extraction and fully utilizes signal information. To verify the diagnostic capability of the model, five types of data are collected and used: the pitting of tooth flank, flat-headed sleeve tooth crack, gear surface crack, gear tooth surface spalling, and normal conditions. The model achieves an accuracy of 97.46% and incorporates a lightweight design, which enhances computational efficiency. Furthermore, the model maintains approximately 90% accuracy under three noise conditions. Based on these results, the proposed model can effectively diagnose faults in the distribution box, and reduce downtime in engineering. Full article

Surface pre-reacted glass-ionomer (S-PRG) is a new bioactive filler utilized for the restoration of decayed teeth by its ability to release six bioactive ions that prevent the adhesion of dental plaque to the tooth surface. Since ionic liquids are reported to facilitate transepithelial penetration, we reasoned that S-PRG applied to root caries could impact the osteoclasts (OCs) in the proximal alveolar bone. Therefore, this study aimed to investigate the effect of S-PRG eluate solution on RANKL-induced OC-genesis and mineral dissolution in vitro. Using RAW264.7 cells as OC precursor cells (OPCs), TRAP staining and pit formation assays were conducted to monitor OC-genesis and mineral dissolution, respectively, while OC-genesis-associated gene expression was measured using quantitative real-time PCR (qPCR). Expression of NFATc1, a master regulator of OC differentiation, and the phosphorylation of MAPK signaling molecules were measured using Western blotting. S-PRG eluate dilutions at 1/200 and 1/400 showed no cytotoxicity to RAW264.7 cells but did significantly suppress both OC-genesis and mineral dissolution. The same concentrations of S-PRG eluate downregulated the RANKL-mediated induction of OCSTAMP and CATK mRNAs, as well as the expression of NFATc1 protein and the phosphorylation of ERK, JNK, and p38. These results demonstrate that S-PRG eluate can downregulate RANKL-induced OC-genesis and mineral dissolution, suggesting that its application to root caries might prevent alveolar bone resorption. Full article

Today, there are many diagnostic methods and advanced measurement techniques enabling the correct diagnosis and assessment of the type and degree of wear of cogwheels (gears, pumps, etc.). The present study presents an analysis of the surface defects of a cogwheel of an oil pump prototype (3PW-BPF-24). The test object operated for a certain number of hours under controlled operating and environmental parameters. The damage to the surface layer was caused by fatigue phenomena and previous thermo-chemical treatment. On the basis of the significant percentage share (~30%) of residual austenite in the volume of the diffusion layer, a hypothetical conclusion was drawn about the suboptimal parameters of the thermo-chemical treatment process (in relation to the chemical composition of the analyzed pinion). A large number of research studies indicate that the significant presence of residual austenite causes a decrease in tooth surface hardness, the initiation of brittle cracks, a sharp decrease in fatigue strength, an increase in brittleness and a tendency to develop surface layer cracks during operation. High-resolution 3D scans of randomly selected pitting defects were used in the detailed study of the present work. It was indicated that the analysis of the morphology of surface defects allowed some degree of verification of the quality of the heat/chemical treatment. The martensitic transformation of residual austenite under controlled (optimum) repeated heat treatment conditions could significantly improve the durability of the pinion (cogwheel). In the case analyzed, the preferred treatment was the low-temperature treatment. The paper concludes with detailed conclusions based on the microscopic and macroscopic investigations carried out. Full article

This paper presents a method of pitting failure detection in toothed gears based on the reconstruction of the gear case vibrational signal. The effectiveness of the proposed method was tested in an experiment on a power circulation test stand. The autoencoder deep neural network architecture, semi-supervised training, and validation, along with the latent data convex hull-based clustering, are presented. The proposed method offers high efficiency (0.99 F1-measure) in gear state prediction (100% in failure detection, 98.9% in normal state prediction) and provides more capabilities in terms of generalization in comparison with linear machine learning techniques such as principal component analysis and nonlinear like the generative adversarial network. Moreover, it is distinguished by high sensitivity while also being able to detect even slight surface damage (initial pitting). These findings will be of particular relevance to a range of scientists and practitioners working with gear drives who are willing to implement machine learning in signal processing and diagnosis. Full article

The current paper presents helical gearbox defect detection models built from raw vibration signals measured using a triaxial accelerometer. Gear faults, such as localized pitting, localized wear on helical pinion tooth flanks, and low lubricant level, are under observation for three rotating velocities of the actuator and three load levels at the speed reducer output. The emphasis is on the strong connection between the gear faults and the fundamental meshing frequency GMF, its harmonics, and the sidebands found in the vibration spectrum as an effect of the amplitude modulation (AM) and phase modulation (PM). Several sets of features representing powers on selected frequency bands or/and associated peak amplitudes from the vibration spectrum, and also, for comparison, time-domain and frequency-domain statistical feature sets, are proposed as predictors in the defect detection task. The best performing detection model, with a testing accuracy of 99.73%, is based on SVM (Support Vector Machine) with a cubic kernel, and the features used are the band powers associated with six GMF harmonics and two sideband pairs for all three accelerometer axes, regardless of the rotation velocities and the load levels. Full article

Analytical modelling is an effective approach to obtaining a gear dynamic response or vibration pattern for health monitoring and useful life prediction. Many researchers have modelled this response with various fault conditions commonly observed in gears. The outcome of such models provides a good idea about the changes in the dynamic response available between different gear health states. Hence, a catalogue of the responses is currently available, which ought to aid predictions of the health of actual gears by their vibration patterns. However, these analytical models are limited in providing solutions to useful life prediction. This may be because a majority of these models used single fault conditions for modelling and are not valid to predict the remaining life of gears undergoing more than one fault condition. Existing reviews related to gear faults and dynamic modelling can provide an overview of fault modes, methods for modelling and health prediction. However, these reviews are unable to provide the critical similarities and differences in the single-fault dynamic models to ascertain the possibility of developing models under combined fault modes. In this paper, existing analytical models of spur gears are reviewed with their associated challenges to predict the gear health state. Recommendations for establishing more realistic models are made especially in the context of modelling combined faults and their possible impact on gear dynamic response and health prediction. Full article

The planetary gearbox plays a vital role in a wide range of mechanical power transmission systems, including high-speed trains, wind turbines, vehicles, and aircraft. At the same time, the planetary gear train inside the gearbox is regarded as the most susceptible to failure in the entire transmission system. To analyze the influence of surface roughness on the dynamic characteristics of the planetary gear train, a dynamic modeling method based on fractal theory is proposed. Firstly, the tooth surface contact model was established based on the W-M fractal function, and the time-varying mesh stiffness (TVMS) of the planetary gear train was calculated under healthy and tooth macro-pitting. Then, the lumped-parameter method is introduced to construct a planetary gear train translation-torsion dynamic model that comprehensively considers TVMS and tooth backlash. The vibration acceleration signals of the planetary gear train under different macro-pitting states and surface roughness are simulated and calculated, allowing a quantificative analysis of the influence of surface roughness on system vibration response. Finally, the correctness of the model for the planetary gear train is verified by experiments. The results show that compared with the planetary gear train modeling method based on Hertz contact theory, the root mean squared error of the vibration signal of this work under a macro-pitting fault state is reduced by 8.7%, further improving the reliability of the model. Full article

A study on the gearbox (speed reducer) defect detection models built from the raw vibration signal measured by a triaxial accelerometer and based on convolutional neural networks (CNNs) is presented. Gear faults such as localized pitting, localized wear on helical pinion tooth flanks, and lubricant low level are under observation for three rotating velocities of the actuator and three load levels at the reducer output. A deep learning approach, based on 1D-CNN or 2D-CNN, is employed to extract from the vibration image significant signal features that are used further to identify one of the four states (one normal and three defects) of the system, regardless of the selected load level or the speed. The best-performing 1D-CNN-based detection model, with a testing accuracy of 98.91%, was trained on the signals measured on the Y axis along the reducer input shaft direction. The vibration data acquired from the X and Z axes of the accelerometer proved to be less relevant in discriminating the states of the gearbox, the corresponding 1D-CNN-based models achieving 97.15% and 97% testing accuracy. The 2D-CNN-based model, built using the data from all three accelerometer axes, detects the state of the gearbox with an accuracy of 99.63%. Full article