Aarhus GeoSoftware (Denmark)

Reliable assessment of kicking performance is critical for monitoring technical development in youth soccer players. This study examined the reliability of kicking performance assessments in skilled youth soccer players aged 14-18, focusing on maximum ball speed and accuracy. Using a test-retest design, fifty-three players from a Danish Super League academy completed standardised kicking tests on two occasions separated by 2 days. Maximum ball speed was assessed during penalty-style kicks with a stationary ball, while accuracy was assessed through target-directed kicks with a rolling ball. Two-way mixed-effects intraclass correlation coefficients (ICC) and limits of agreement (LoA) were used to assess reliability. Results indicated good reliability for maximum ball speed (ICC = 0.87; LoA = [-2.1, 2.5] m/s), consistent with previous studies. Conversely, kicking accuracy demonstrated poor to moderate reliability (ICC = 0.00-0.66; LoA = [-0.46, 0.48] m). Stratified analyses revealed the highest accuracy ICC within the U15 group. Exploratory error ellipse analyses suggested potential utility in assessing kicking distribution, showing good reliability for ellipse angle (ICC = 0.80; LoA = [-0.98, 0.92] rad), though other parameters displayed low reliability. These findings highlight the strengths and limitations of soccer technical assessments. While maximum ball speed tests are highly reliable, kicking accuracy assessments remain problematic and require methodological refinement to improve reliability. Future research should incorporate advanced analytical techniques to enhance test reliability and consistency in elite athlete evaluations.

Accurate three-dimensional acoustic impedance modeling in offshore clastic reservoirs remains a significant challenge due to sparse well control and the highly nonlinear relationship between seismic attributes and subsurface elastic properties. This study introduces an integrated, physics-guided machine learning (ML) workflow that combines rock-physics-driven pseudo-well generation with neural networks to directly map seismic attributes to acoustic impedance under data-limited conditions. A soft-sand rock physics workflow was applied, in which grain moduli were determined using the Voigt-Reuss-Hill average. The dry rock frame was modeled at critical porosity by Hertz-Mindlin contact theory and then interpolated toward zero porosity using the Modified Hashin-Shtrikman lower bound. Gassmann fluid substitution was subsequently performed. Using this approach, 45 pseudo-wells were generated and conditioned through lithofacies classification and spatial statistics, mitigating the risk of overfitting associated with the three available real wells. Six seismic attributes-envelope, RMS amplitude, instantaneous phase, instantaneous frequency, quadrature trace, and sweetness-were selected as predictors. Two neural architectures, a multi-layer feedforward network (MLFN) and a radial basis function network (RBFN), were trained and benchmarked using a leave-one-well-out cross-validation scheme. The MLFN achieved higher predictive accuracy (CC = 0.87, NRMSE = 0.493) compared to the RBFN (CC = 0.79, NRMSE = 0.613), which may reflect its greater capacity to model broader hierarchical relationships between seismic attributes and acoustic impedance. The resulting impedance volume delineates laterally coherent high-impedance sandstone units and low-impedance porous intervals consistent with geological interpretation. These results suggest that integrating physics-guided pseudo-well augmentation with feed-forward neural networks offers a practical and computationally efficient approach for acoustic impedance inversion in data-limited offshore settings. Future work may explore validation across diverse geological settings to assess the robustness and transferability of the proposed methodology. This study provides a basis for hybrid and uncertainty-aware inversion frameworks that may help address complexities in heterogeneous reservoir systems, highlighting the importance of reproducible and widely applicable data-driven seismic inversion methods under sparse well control.

Compared to traditional river surface velocity measurement techniques such as in-situ point measurements with electromagnetic current meters, remote sensing techniques are attractive because measurements are fast, low cost and contactless. Based on Unmanned Aerial Systems (UAS) equipped with optical equipment (e.g., HD camera) and Doppler radar, surface velocity can be efficiently measured with high spatial resolution. UAS-borne Doppler radar is particularly attractive, because it is suitable for real-time velocity determination and has fewer limitations (no seeding of the flow required, no daylight required, works for both narrow and wide rivers).In this paper, videos from a UAS RGB video camera were analysed using both Particle Image Velocimetry (PIV) and Space-Time Image Velocimetry (STIV) techniques. Furthermore, we recorded full waveform signal data using a 24 GHz continuous wave Doppler radar (e.g., Geolux RSS-2-300) at multiple waypoints across the river. Different from previous processing methods, which only considered the processed velocity from Doppler radar itself, we propose an algorithm for picking the correct river surface velocity from the raw data. The algorithm fits two alternative models to the raw data average amplitude curve to derive the correct river surface velocity: a Gaussian one peak model, or a Gaussian two peaks model.Results indicate that river flow velocity and drone-induced propwash velocity can be found in the river’s lower flow velocity portions (i.e., surface velocity between 30 cm/s and 80 cm/s), while the drone-induced velocity can be neglected in fast and highly turbulent flows (i.e., surface velocity > 80 cm/s). To verify the river flow velocity derived from Doppler radar, a mean PIV value within the footprint of the Doppler radar at each waypoint was calculated. Finally, quantitative comparisons of electromagnetic velocity sensor data (OTT MF Pro) with STIV, mean PIV and Doppler radar revealed that UAS-borne Doppler radar could reliably measure the river flow velocity.

Abstract Using Unoccupied Aerial Systems (UAS) equipped with optical RGB cameras and Doppler radar, surface velocity can be efficiently measured at high spatial resolution. UAS‐borne Doppler radar is particularly attractive because it is suitable for real‐time velocity determination, because the measurement is contactless, and because it has fewer limitations than image velocimetry techniques. In this paper, five cross‐sections (XSs) were surveyed within a 10 km stretch of Rönne River in Sweden. Ground‐truth surface velocity observations were retrieved with an electromagnetic velocity sensor (OTT MF Pro) along the XS at one m spacing. Videos from a UAS RGB camera were analyzed using both Particle Image Velocimetry (PIV) and Space‐Time Image Velocimetry (STIV) techniques. Furthermore, we recorded full waveform signal data using a Doppler radar at multiple waypoints across the river. An algorithm fits two alternative models to the average amplitude curve to derive the correct river surface velocity based on Gaussian models with: (a) one peak, and (b) two peaks. Results indicate that river flow velocity and propwash velocity caused by the drone can be found in XS where the flow velocity is low, while the drone‐induced propwash velocity can be neglected in fast and highly turbulent flows. To verify the river flow velocity derived from Doppler radar, a mean PIV value within the footprint of the Doppler radar at each waypoint was calculated. Finally, quantitative comparisons of OTT MF Pro data with STIV, mean PIV and Doppler radar revealed that UAS‐borne Doppler radar could reliably measure the river surface velocity.

Due to fresh water being rare, especially in the arid regions, the need to produce more food, and facing the climate change effects on agricultural production, so, enhancing irrigation systems and raising their efficiency become urgent necessity. The good evaluation of irrigation systems leads to better water management and sustainable agricultural development. This study proposed a framework for evaluating the center pivot irrigation systems based on remotely sensed data and field measurements. Irrigation uniformity distribution (CU and DUlq) were assessed from field data using catch cans method. Irrigation equity was evaluated by estimating actual evapotranspiration (ETa) based on the triangle method from satellite data. The spatial variation of ETa was assessed from the coefficient of variation (CV), and ETa maps were produced for better interpretation. Irrigation equity assessed at different two scales, the whole cultivated area and different placements of the center pivot irrigation system. The results showed that the irrigation system water distribution uniformity was "poor" as the CU value was between 70%-79% and the DUlq was 61%. The results also indicated the absence of irrigation equity (i.e., fairness of irrigation distribution) especially in the early and late stages of the crop life cycle, as the STDV of ETa and the CV values were high during the monitoring dates. Also the irrigation equity did not exist along the center pivot irrigation system radius at different 4 placement of irrigation.

ABSTRACT Performance analysis (PA) has become a key requirement for association football coaches within England to aid their practice but less is known in other countries. We examined the perceptions of Danish association football coaches towards the use and engagement with PA. An online survey with 34 open-ended and close-ended questions was completed by 200 UEFA A and Pro Licenced coaches, gaining insights into how the coaches’ engaged with PA, how PA supported their practice and the future of PA in Denmark. Additionally, five male coaches completed a semi-structured interview. UEFA Pro Licenced coaches had greater experience with analysis support and perceived the importance of video for opposition and reviewing their own team’s performance with higher regard than UEFA A Licenced coaches. Cost and time were highlighted as barriers, but a clear desire to have a dedicated performance analyst was acknowledged by all coaches. The research findings provide key considerations regarding the knowledge and understanding of how Danish football coaches review and evaluate performances, highlight the perceived benefits of PA and acknowledge a desire to increase investment. The insights can be used to inform future decision making regarding the direction of PA provision and the education and development of Danish coaches.

Linearized approximations to the P-wave reflectivity as a function of the incidence angle (called amplitude variation with offset) involve the extraction of band-limited reflectivity terms that are a function of changes in the elastic constants of the earth across each lithologic interface. The most common of these extracted reflectivities are the intercept and gradient, usually labeled [Formula: see text] and [Formula: see text], respectively. The extended elastic impedance (EEI) method uses a rotation angle [Formula: see text] to map [Formula: see text] and [Formula: see text] into a new reflectivity corresponding to a particular elastic parameter. The success of EEI depends on finding an optimum value for the angle [Formula: see text]. This value is usually calculated by correlating the EEI result over a range of [Formula: see text] angles with various elastic parameters and then finding the best correlation coefficient. We have developed a new approach for the interpretation of the EEI method, which incorporates the Biot-Gassmann poroelastic theory and attaches a physical meaning to the [Formula: see text] angle. We call this method extended poroelastic impedance (EPI). The main advantage of the EPI method is that the [Formula: see text] angle is now interpreted as a parameter that is dependent on the dry-rock properties of the reservoir, rather than a parameter whose value is estimated empirically. The method is evaluated by numerical and synthetic seismic examples and by application to field data from a gas sand reservoir.

Induced polarization (IP) effects are becoming more evident in time-domain helicopter airborne electromagnetic (AEM) data thanks to advances in instrumentation, mainly due to improvements in the signal-to-noise ratio and hence better data quality. Although the IP effects are often manifested as negative receiver voltage values, which are easy to detect, in some cases, IP effects can distort recovered transients in other ways so they may be less obvious and require careful data analysis and processing. These effects represent a challenge for modeling and inversion of the AEM data. For proper modeling of electromagnetic transients, the chargeability of the subsurface and other parameters describing the dispersion also need to be taken into consideration. We use the Cole-Cole model to characterize the dispersion and for modeling of the IP effects in field AEM data, collected by different airborne systems over different geologies and exploration targets, including examples from diamond, gold, and base metal exploration. We determined how multiparametric inversion techniques can simultaneously recover all four Cole-Cole parameters, including resistivity [Formula: see text], chargeability [Formula: see text], relaxation time [Formula: see text], and frequency parameter [Formula: see text]. The results obtained are in good agreement with the ancillary information available. Interpretation of the IP effects in AEM data is therefore seen by the authors as providing corrected electrical resistivity distributions, as well as additional information that could assist in mineral exploration.

Durable osseointegration of metallic bone implants requires that progenitor cells attach, proliferate and differentiate on the implant surface. Previously, we demonstrated superior biocompatibility of human mesenchymal stromal cells (MSCs) cultivated on ultrasmooth tantalum (Ta) as compared to titanium (Ti) surface. The aim of this study was to extend the previous investigation of biocompatibility by monitoring temporal gene expression of MSCs on topographically comparable smooth Ta and Ti surfaces using whole-genome gene expression analysis. Total RNA samples from telomerase-immortalized human MSCs cultivated on plain sputter-coated surfaces of Ta or Ti for 1, 2, 4, and 8 days were hybridized to n = 16 U133 Plus 2.0 arrays (Affymetrix®). Functional annotation, cluster and pathway analyses were performed. The vast majority of genes were differentially regulated after 4 days of cultivation and genes upregulated by MSCs exposed to Ta and Ti were predominantly related to the processes of differentiation and transcription, respectively. Functional annotation analysis of the 1,000 temporally most significantly regulated genes suggests earlier cellular differentiation on Ta compared to Ti surface. Key genes related to osteogenesis and cell adhesion were upregulated by MSCs exposed to Ta. We further identified differentially regulated candidate transcription factors, e.g., NRF2, EGR1, IRF-1, IRF-8, NF-Y, and p53 as well as relevant signaling pathways, e.g., p53 and mTOR, indicating e.g., differences in the Ta- and Ti-induced oxidative stress reactions at the cell/biomaterial interface. These findings suggest that Ta is a promising material for bone implants.