Background: Nanotechnology-based surface coatings show promise in enhancing the antibacterial activity and mechanical performance of orthodontic materials. Although numerous in vitro studies suggest some benefits, the clinical relevance of these effects in vivo remains uncertain. Objectives: The aim of this systematic review was to assess whether nanocoated orthodontic materials improve clinical and clinically derived laboratory outcomes in in vivo human studies, including microbial adhesion, enamel mineralization, surface characteristics and other treatment-related parameters. Materials and Methods: An electronic literature search was conducted up to 31 July 2025, yielding 15,330 records after duplicate removal. Only in vivo studies using nanocoated materials in patients undergoing orthodontic treatment were included. Study selection, data extraction, risk-of-bias assessment and certainty-of-evidence assessment were performed independently by two reviewers. Risk of bias was assessed using RoB 2 and ROBINS-I, and certainty of evidence was evaluated using GRADE. Results: A total of 13 studies were included in the qualitative synthesis. The comparative appliances were coated and uncoated brackets, tubes, archwires, miniscrews and modules. Most studies primarily assessed microbial outcomes. Overall, nanocoated orthodontic appliances showed promising antimicrobial properties and lower bacterial adhesion in several studies. Other studies assessed enamel demineralization, surface characteristics, bond failure, orthodontic miniscrew success rate and rate of canine retraction. Conclusions: Although the included studies indicate promising results, confidence is limited by heterogeneity in nanoparticle types, coating protocols and outcome measures. Safety outcomes, including coating degradation and long-term local or systemic effects, were not adequately assessed. Further well-designed randomized trials with standardized and clinically meaningful outcomes are needed before routine clinical implementation can be supported.
Global Navigation Satellite Systems (GNSS) signals are delayed by the atmosphere, and the resulting zenith total delay (ZTD) provides valuable information on atmospheric water vapour for numerical weather prediction (NWP). Despite the demonstrated benefits of GNSS ZTD assimilation, the southern part of the Meteorological Cooperation on Operational Numerical Weather Prediction (MetCoOp) domain remains sparsely observed, particularly along the main southwesterly moisture-transport pathway into Fennoscandia. This study investigates the impact of assimilating additional ZTD observations from northern Germany—provided by the Helmholtz Centre for Geosciences (GFZ)—into the MetCoOp system. By extending ZTD coverage upstream of the forecast domain, the study addresses a documented gap in previous MetCoOp assimilation research, which has largely focused on densely observed regions or event-specific cases. Since moisture transport into Fennoscandia is climatologically dominated by southwesterly flow from the North Sea, particularly during summer, strengthening ZTD coverage in the southern MetCoOp domain therefore provides critical upstream constraints on humidity and temperature advection. Using the convection-permitting High-Resolution Limited Area Model—Applications of Research to Operations at Mesoscale (HARMONIE–AROME) model coupled with the Surface Externalisée scheme (SURFEX), two experiments were run for June–August 2025: a baseline configuration and one including GFZ ZTDs. Assimilating GFZ data significantly refines the mid-to-lower-tropospheric moisture field (500–925 hPa). Although domain-averaged differences remain modest—specific humidity variations of ~1.3 g kg−1 and temperature deviations near 1 K—spatial analyses reveal sharper moisture gradients and improved boundary-layer structure over land. These findings demonstrate that enhanced upstream ZTD density provides valuable constraints on moisture advection and latent-heat-related processes, thereby improving short-range humidity analyses in a high-resolution NWP system and complementing earlier studies conducted in Fennoscandia.
During tunnel construction, ground-penetrating radar detection requires stable contact between the antenna and the tunnel wall, whereas manual operation is labor-intensive, risky, and difficult to maintain with consistent coupling. To improve automated tunnel surrounding-rock quality detection, this study develops an end-effector compliant control system for a tunnel inspection manipulator. A three-degree-of-freedom manipulator equipped with a passive compliant end-effector mechanism was designed, and an admittance controller was established to regulate the contact force between the radar antenna and the tunnel surface. To improve adaptability under irregular wall conditions, a fuzzy variable-damping strategy was introduced, in which the equivalent end-effector velocity and acceleration were used to adjust the damping coefficient online. The proposed controller was evaluated through MATLAB/Simulink simulations, indoor experiments with wall and ground irregularities, and field tests in an actual tunnel. Compared with fixed-damping admittance control, the proposed method reduced contact-establishment overshoot by 69.3% and inspection-stage force fluctuation by 36.49% in simulation. Indoor experiments further verified lower peak contact force and shorter settling time under different wall and ground conditions. Field tests at the tunnel crown and sidewall demonstrated that the radar antenna maintained effective wall contact. The results indicate that fuzzy variable-damping admittance control improves contact stability and practical applicability for robotic tunnel inspection.
Identifying induction-motor equivalent-circuit parameters from nameplate data is challenging without the IEEE Std 112 no-load and locked-rotor tests. This study reformulates the task from constraint-based curve fitting to population-calibrated maximum-a-posteriori (MAP)-type estimation of seven steady-state parameters. Under an identical protocol, the best of fifteen metaheuristics reached 14.71% global error against 23.40% for the best analytical method; its residual structure motivates the central step: closed-form nameplate predictors, calibrated on a five-motor reference population, become Gaussian prior means whose cross-validated dispersions set the prior widths and the ±3σ search bounds. Re-anchoring the loss priors to a per-machine constant-loss decomposition (MAP+) lowers the apparent error to 3.48%, and nested leave-one-out cross-validation, re-calibrating all statistical quantities per fold, gives a validated 4.27%. With algorithm, budget and seeds fixed, formulation outweighs optimizer choice. Withholding catalog quantities and predicting them back gives forty out-of-fit checks on ten machines: rated current within 0.96% and partial-load power factor within 2.65%, but the breakdown-torque ratio only within 9.04%, proving it an input rather than an output; locked-rotor errors confine the single-cage model to running operation. It offers a calibrated, validity-bounded basis for the steady-state layer of digital twins and energy auditing, not a general-purpose estimator.
Accurate characterization of picosecond broadband pulse signals is important for radio-frequency electronics, radar detection and high-speed communications. However, bandwidth-limited oscilloscopes attenuate high-frequency components, broaden rising edges, reduce peak amplitudes and introduce ringing distortion. Conventional regularized deconvolution relies heavily on manual parameter tuning and is vulnerable to noise, while Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) models suffer from long training time and insufficient capability to capture global temporal dependencies. Here we present a waveform reconstruction method integrating convolutional neural networks, Transformer and learnable physics-guided deconvolution constraints. The model simultaneously extracts local waveform details and models global temporal correlations, which improves training efficiency and noise robustness, and restores pulse rising edges, peak amplitudes and overall waveform morphology with higher precision. The proposed method offers a low-cost software solution to realize high-precision measurement of high-speed pulse signals without hardware upgrades.
