Search Results (23,022)

Background/Objectives: In addition to the clinical gold standard, tympanometry, several alternatives for middle ear diagnostics have evolved over the past decades. With the so-called pressureless acoustic impedance test, the Neuranix Medwave, another device, came into play. Methods: Using a retrospective, anonymous study design, descriptive data were reported, and the correlation between Medwave’s results and tympanometry types was evaluated. Also, the correlation between the patients’ age and the Medwave resulting parameters was evaluated. We were able to show changes in the measurement results over time in the case of paracentesis and tube insertion. Results: The analyzed data show that it is possible to differentiate between tympanometry result type A and type B using the Medwave resulting parameter resonance frequency (“fR”), but not when using peak admittance (“P”). Between all other types, it was not possible to differentiate using the Medwave resulting parameters, nor fR nor P. Due to the low statistical power, this may be due to a type II error. Regarding age, a correlation was found only for the tympanometry result type A. The case over time showed a clear difference in the affected ear between the time before and after the ear surgeries, as well as the contralateral healthy ear. Conclusions: While this study indicates the potential use of the PLAI technology, especially as a tool in situations where traditional tympanometry is not feasible, the results need to be interpreted with caution. Further validation with larger and more balanced groups of participants is necessary to confirm these initial findings and to more clearly define the clinical utility of this technology. Full article

Laser beam combining is essential for achieving high-power and high-radiance output. However, atmospheric turbulence induces independent tip–tilt aberrations across discrete sub-beams in laser array systems, which severely degrades the concentration of far-field energy. Traditional wavefront sensing techniques are primarily designed for the continuous wavefront of a single laser and are not directly applicable to laser array, whereas indirect optimization-based methods often suffer from slow convergence and limited real-time performance. To address these limitations, this study introduces a tip–tilt aberration compensation system for laser array propagation based on cooperative beacons with a shared-aperture transmit–receive configuration. The primary innovation consists of a modified Shack–Hartmann wavefront sensor (SHWFS) tailored to a discrete multi-beam layout, which facilitates the direct, independent, and simultaneous measurement of tip–tilt aberrations for each sub-beam. In conjunction with a segmented deformable mirror (SDM), the architecture can facilitate real-time closed-loop correction with high bandwidth and high precision. Numerical simulations of a 7-, 19-, and 37-beam laser array, together with validation experiments utilizing a 30-beam configuration, demonstrate that the proposed approach effectively suppresses tip–tilt error induced by turbulence. After closed-loop correction, the Strehl ratio (SR) increases above 0.92 ($r_0=5\mathrm{cm}$), while the beam quality factor β reduces below 1.37 ($r_0=5\mathrm{cm}$). Furthermore, the system retains performance stability as the number of sub-beams increases, demonstrating the scalability of the proposed method. In contrast to conventional approaches designed for a continuous wavefront, the proposed method offers a feasible approach for a discrete laser array system, providing robust and scalable tip–tilt correction under varying atmospheric conditions. Full article

In response to the technical bottleneck of the Venlo greenhouse’s inability to achieve year-round production due to the high temperature and humidity in the summer in South China, this study took an existing Venlo-type greenhouse in Guangzhou as the research object and constructed a three-dimensional computational fluid dynamics (CFD) model of the greenhouse by comprehensively considering key factors such as solar radiation, thermal radiation, and crop canopy resistance. After on-site experiments, it was verified that, except for the top area of the greenhouse, the temperature deviation between the model simulation values and the measured values was less than 2 °C, and the error rate was less than 5%, confirming the model’s accurate representation of the temperature field distribution within the greenhouse. Based on the characteristics of the temperature and humidity fields revealed by the CFD simulation (canopy temperature gradient K = 0.144 °C/m, maximum temperature difference between upper and lower layers 20 °C), an optimized scheme of “wet curtain fan + salt bath dehumidification equipment” for local cooling and dehumidification of the crop canopy was proposed, and a non-uniform air duct layout was designed according to the temperature gradient characteristics. Field experiments showed that after optimization, the daytime temperature of the crop canopy was mostly controlled within 30 °C, the relative humidity was stably maintained below 80%, and the maximum temperature difference along the length of the greenhouse was reduced from 7 °C to 2 °C, effectively solving the problem of poor cooling and dehumidification effects of the traditional system. This scheme enabled the stable operation and year-round production of Venlo-type greenhouses in South China during the summer, providing technical support and engineering reference for greenhouse environmental control in high-humidity areas. Full article

With the increasing application of novel glass materials in the field of optics, traditional empirical and trial-and-error approaches to glass development are gradually becoming insufficient to meet escalating performance demands. In this study, we propose a neural network-based machine learning method for the design of advanced fluoride glass materials. Predictive models for density and refractive index were first developed based on online fluoride glass datasets. Moreover, SHapley Additive exPlanations (SHAP) analysis was adopted to uncover the quantitative composition-property relationship. Then, the well-trained model was employed for inverse design, identifying specific compositions that fulfill desired properties in terms of density and refractive index. Finally, several recommended compositions were experimentally validated and the measured density and refractive index matched well with the corresponding input values, thereby confirming the effectiveness of the proposed method in designing new fluoride glass materials. Full article

Accurately characterizing the statistical fluctuations of vehicle radar cross sections (RCSs) across polarization states and azimuthal sectors is essential for evaluating detection performance, conducting probabilistic simulations, and analyzing target features in millimeter-wave radar systems. Existing one-dimensional RCS statistical models, including Weibull, Chi-square, Lognormal, Rice, and Gaussian distributions, are often limited by their restricted functional expressiveness, making it difficult to simultaneously capture skewness, tail thickness, and azimuthal dependence under narrow angular-domain conditions. In addition, purely nonparametric approaches tend to produce spurious modes under finite-sample conditions and lack interpretable structural priors. To address these limitations, this paper proposes a Unimodal RCS Semiparametric Density Estimator (URCS-SDE) tailored for ground-vehicle targets. The proposed approach adopts kernel density estimation (KDE) as a data-driven baseline representation and incorporates physically plausible structural constraints through unimodal shape projection. Then a beta-type tail template is further introduced in the normalized amplitude domain to regulate boundary decay behavior. Finally, weighted least-squares calibration is performed on the histogram grid of the empirical probability density function (PDF), achieving a balanced trade-off between fitting accuracy and stability in both the peak and tail regions. Using multi-azimuth RCS measurements of two representative ground vehicles, the URCS-SDE is systematically compared with five classical parametric distributions and a representative regularized mixture density network (MDN) baseline. Performance is evaluated under both full-azimuth and directional-window conditions using the sum of squared errors (SSE), root mean squared error (RMSE), coefficient of determination (R-square) and held-out negative log-likelihood (NLL). The results show that the URCS-SDE consistently provides the most accurate and stable density estimates, especially in narrow angular windows. In addition, a threshold-based detection-support example derived from the fitted PDFs demonstrates that the advantage of the URCS-SDE transfers from density reconstruction to a directly engineering-relevant downstream quantity. Full article

Accurate measurement of ocean wave parameters is paramount for offshore engineering design and marine environmental monitoring. Distributed pressure sensing technology provides a robust data foundation for analyzing the spatio-temporal characteristics of wave fields through synchronized observations at multiple stations. However, multi-sensor data exhibit high-dimensional spatio-temporal coupling, posing significant challenges for traditional single-point signal processing methods in capturing the topological associations between measurement sites. To address these limitations, this study develops a framework for spatio-temporal feature modeling and wave height reconstruction based on Graph Neural Networks (GNNs). The proposed framework integrates the spatial configuration of sensor arrays with graph-theoretic topological representations. By fusing geometric distances and signal correlations, an adaptive adjacency matrix is constructed to establish a dynamically adjustable graph structure. On the feature extraction level, a spatio-temporal fusion method combining multi-scale graph convolutions and gated temporal modeling is proposed. The experimental results obtained on the Blancs Sablons Bay multi-sensor dataset demonstrate that the proposed method significantly outperforms traditional approaches, achieving lower prediction errors and validating the effectiveness of graph-structured modeling in distributed wave sensing. Full article

Robust robotic autonomy remains challenging in complex environments, where loss of stability on uneven or slippery terrain can induce extreme accelerations and angular velocities. Such motions corrupt sensor measurements and degrade state estimation, motivating the need for improved algorithmic robustness. To investigate this issue, we introduce the Tumbling-Induced Gyroscope Saturation (TIGS) dataset, which consists of recordings from a mechanical lidar and an Inertial Measurement Unit (IMU) tumbling down a hill. The dataset contains angular speeds up to four times higher than those in similar datasets and is publicly available. We then propose two complementary methods to improve Simultaneous Localization And Mapping (SLAM) robustness and evaluate them on TIGS. First, Saturation-Aware Angular Velocity Estimation (SAAVE) estimates angular velocities when gyroscope measurements become saturated during aggressive motions, reducing angular speed estimation error by 83.4%. Second, Stretch-ICP, a novel registration and deskewing algorithm, enables reconstruction of smoother 6-Degrees Of Freedom (DOF) trajectories under aggressive motions compared to classical Iterative Closest Point (ICP). Stretch-ICP reduces linear and angular velocity errors by 95.2% and 94.8%, respectively, at scan boundaries. Together, these contributions improve the robustness and consistency of lidar-inertial state estimation under aggressive motions. Full article

Estimating in a stream’s cross-section the depth-averaged velocity, V, from the free-surface velocity, v_surf, is an efficient, non-invasive hydrometric method. The ratio f_v = V/v_surf is typically assumed constant at f_v = 0.86 in field applications, despite observations to the contrary. Guidance is, therefore, needed in estimating actual f_v-ratios when velocity profile data are absent. This work provides field-verified guidance based on the hydromechanics of the logarithmic velocity law, which shows that f_v depends on the scaled resistance measure ‘friction length/depth’, y_o/h, with the y_o(k) function of the equivalent sand grain roughness, k. The mean-to-surface-velocity ratio in rough-bed streams is estimated from the bed roughness and stream morphology by modifying Nikuradze’s equation, y_o = k/30, to y_o = ck, with c(h/k) ≥ 1/30, and k ≈ D₈₄—data fit: c ≈ 8.61(h/k)^−1.821, ~5 ≤ h/k < ~30. Field-verification of the ratio’s modified hydromechanics, f_v = f$\left(h/y_o\right),$ with y_o(h/k) evaluated from bed roughness estimated by inspection or sieve analysis shows this ratio holding within ~|10|% error for shallow streamflow over a coarse bed of gravels and rocks, giving submergences of ~5 ≤ h/D₈₄ ≤ ~30; y_o = k/30 suits large streams with smooth beds (h/k ≥ ~30, f_v ≥ ~0.86). Variable roughness-estimated f_v-ratios appear to be more reliable than the fixed default, f_v(h/y_o ≈ 1000) = 0.86. This flow-gauging concept is based on observable physical characteristics of a monitoring cross-section and facilitates the rating of hard-to-access streams draining small basins in ragged upland terrain. Full article

Background/Objectives: The modified Budin radiographic technique is a practical alternative to CT for measuring femoral neck anteversion (FNA); however, the impact of hip abduction angle on its accuracy remains unclear. This experimental study examined how varying abduction angles affect agreement between modified Budin measurements and CT. Methods: Twenty-seven dry adult femora underwent CT scanning, and FNA was measured using a validated three-slice superimposition method as the reference standard. Modified Budin radiographs were obtained at 20°, 30°, and 40° of femoral abduction. Two orthopedic surgeons independently measured FNA on all images twice, with at least 15 days between measurements. Intra- and interobserver reliability were assessed using the intraclass correlation coefficient (ICC). Mean values per femur were analyzed. Agreement with CT was evaluated using Pearson correlation, Bland–Altman analysis, and absolute error comparisons across abduction angles. Results: Reliability was excellent across all modalities (ICC, 0.982–0.998). Mean CT-derived FNA was 10.0° ± 8.5°, compared with 9.1° ± 8.0° at 20°, 8.3° ± 7.8° at 30°, and 7.8° ± 7.5° at 40° of abduction (p < 0.001). Correlation with CT was strong at all positions, but systematic underestimation increased with abduction angle. Among the tested positions, 20° abduction showed the smallest bias, the narrowest limits of agreement, and the lowest absolute error. Conclusions: Hip abduction angle significantly influences the accuracy of the modified Budin view. Under controlled experimental conditions, 20° abduction provided the closest agreement with CT among the tested positions. These findings suggest that lower abduction angles may improve geometric accuracy, although clinical feasibility and performance must be confirmed in vivo before routine clinical application can be recommended. Full article

Several prediction equations have been created, based on various dietary composition variables, to predict dairy cattle enteric methane emissions (EMEs). Inconsistencies in measuring EMEs have created difficulties comparing dairy cattle emissions between farms and inhibits certain in efforts to reduce emissions and work towards Net Zero. The aims of the current study were to gather existing EME prediction equations and evaluate the variability in their prediction results. An additional aim was to create a combined prediction equation, based on the dietary components with the highest predictive ability, representing the average prediction across existing equations, which accounted for the variation amongst existing equations. The 32 equations produced large variation in the prediction of EMEs for each of the 15 example diets, ranging from 12.49 to 34.27 g CH₄/kg DM. To create a combined EME prediction equation, twelve combinations of dietary variables were evaluated using a mixed-effects model. An equation based on metabolised energy (ME) and neutral detergent fibre (NDF) was chosen (methane (CH₄) = 0.33 × ME + 0.31 × NDF + 3.47), due to the significance of the predictor variables and low prediction error (RMSE = 1.47 g CH₄/kg DM), with a random-effects residual variance of 2.32. The combined equation may act as a suitable compromise to compare emissions between studies accounting for unexplained variation. Full article

Background. The Foot and Ankle Ability Measure is frequently used by clinicians and researchers to assess the effectiveness of therapeutic interventions for patients with foot and ankle pathologies. To review different versions of the FAAM and to evaluate the methodological quality of studies published in this respect. Methods. Systematic review. Setting. A search was conducted in the PubMed, SCOPUS, PEDro, PROSPERO and SPORTDiscus databases, applying the following inclusion criteria: validation studies of the Foot and Ankle Ability Measure, in different languages, with no time limit, in a population aged ≥18 years. Two of the present authors independently assessed the quality of the studies located and extracted the relevant data. The COSMIN checklist was employed to assess methodological quality. Results. Thirteen instruments met the inclusion criteria for this review. In many cases, significant methodological flaws were detected, mostly regarding criterion validity and measurement error. Conclusion. Only the Spanish-language cultural adaptation of the FAAM presents adequate methodological quality. Further studies, with greater methodological rigour, are required of the cultural adaptations of this measurement instrument. Full article

In authenticity checking of tobacco products, the identification of the varieties present is of primary importance. Nowadays the detection of illegal tobacco products is often based on package analysis and administrative verification, sometimes complemented with laboratory analysis. In this study an approach based on IR spectroscopy (MID-IR and NIR) for the identification of tobacco varieties in tobacco blends is proposed. Therefore, different blends were prepared, spectra were measured, and binary PLS-DA models were created. All models were evaluated and compared for their predictive performance, using both cross-validation (internal validation) and an external test set. For the best-performing model for each analyte the limit of detection was estimated. Finally, quantitative models were created to estimate the relative amount of one of the targeted varieties in the mixtures and a proof of concept using five commercial tobacco blends was performed. NIR proved to outperform MID-IR with maximum values of correct classification rate, precision, specificity, and accuracy for four varieties and only one misclassification for the two remaining ones. Indicative limit of detection values were obtained between 1 and 8%. Quantitative errors were all smaller than 5%. These values as well as the application to commercial samples proved the feasibility of the presented approach and its potential value as tool in the fight against fraud and counterfeited tobacco products. Full article

In this article, we study scalar-on-function regression with functional covariates observed through replicated measurements subject to measurement error. Treating replicated curves as surrogates of an underlying latent process, the proposed framework resolves the identifiability issues commonly encountered in functional measurement error models. Through functional principal component analysis, the model is represented as a finite-dimensional hierarchical linear measurement error model. Parameter estimation is carried out using an expectation-maximization algorithm, and alternative correction strategies based on adjusted regression calibration and simulation extrapolation are also considered for comparison. Simulation studies demonstrate the advantages of explicitly accounting for measurement error in terms of bias reduction and estimation stability. An application to soybean yield prediction in Illinois, using meteorological variables contaminated by measurement error, illustrates the practical value of the proposed approach. Full article

Groundwater drainage-induced karst collapse is a major geohazard in coal-mining regions of central Hunan, threatening residential safety and infrastructure. This study focuses on the Tuoshan minefield in the Jinzhushan mining area by integrating multi-source field data, including surveys of 170 collapse points, long-term groundwater monitoring at six boreholes, and high-density electrical geophysics. A topographically corrected MODFLOW seepage-field model is developed and calibrated for 2014 (RMSE = 0.32 m; NSE = 0.85) and validated for 2015–2016 (RMSE = 0.41 m; NSE = 0.81). To address the large groundwater-level simulation errors commonly encountered in subtropical hilly karst mining settings, the model incorporates a topographic correction, improving simulation accuracy by 12% relative to an uncorrected model. The simulations capture rapid “steep rise–slow fall” groundwater dynamics: Heavy rainfall (>100 mm/day) raises groundwater levels by 2.8–3.1 m within 2–3 days, whereas pumping (200 m³/h) causes a 1.9–2.2 m decline within one week. A 1.2 km drawdown funnel forms and overlaps with 89% of collapse points, indicating that seepage-field evolution and groundwater-level decline control collapse clustering, with soil suffusion and soil–water–rock interaction acting as key amplifying processes. Based on Terzaghi’s effective stress principle and the Theis solution, a collapse prediction formula is derived and validated using measured events (accuracy = 87.5%), and a region-specific critical hydraulic gradient (i_n = 0.85) is determined, lower than values reported for North China. The proposed workflow provides quantitative thresholds and model-based guidance for karst collapse prevention in subtropical mining areas. Full article

Northeast China’s rust-belt cities have faced persistent concerns about stagnating labor productivity amid structural change. This paper examines how the productivity payoff to urban density depends on local economic structure and ownership composition using an annual panel of prefecture-level cities. We estimate two-way fixed-effects models with city and year effects and city-clustered standard errors, complemented by dynamic specifications and additional robustness checks. The results show a robust positive within-city association between population density and labor productivity. This density premium is structure-conditioned: the productivity payoff to density is significantly larger in city-years that are more industry-oriented. Information-theoretic measures further show that sectoral and ownership composition matter in distinct ways. A normalized entropy measure based on 19 all-city sectoral employment categories is positively associated with labor productivity, while its interaction with density is negative and significant, indicating that the density premium is weaker in more sectorally balanced city-years. A normalized four-category ownership entropy measure, constructed from SOE, private/self-employed, collective, and other employment shares, is positively associated with labor productivity and interacts positively with density, indicating a stronger density–productivity association in city-years with a more balanced ownership composition. Collectively, the findings suggest that urban density is not a uniform engine of productivity: its payoff depends on whether dense city economies are organized around productive sectoral linkages and a sufficiently balanced ownership environment. Overall, the evidence supports a conditional agglomeration view in which productivity dynamics in Northeast China reflect the interaction of density, structural orientation, sectoral dispersion, and ownership mixing. Full article