Hydrodynamic Cross-Scale Archaeology at a Roman River Harbour

Trans-disciplinary research methods and data from archaeology, geology, hydrology, and hydraulic engineering are successfully merged to reevaluate hydrodynamic effects of Roman hydraulic structures at a Rhine river harbour. The archaeological site Colonia Ulpia Traiana, is characterized by its exceptional preservation, providing ample research data on its river harbour. Constructed by the Romans, the berthing area is lined by a wooden quay-wall. Setting this harbour apart is its up-stream tip, which is fitted with a unique hydraulic structure with unknown purpose. Structure related hydrodynamic impacts on the historic Rhine regime are examined by introducing a novel cross-scale multi model approach, consisting of three steps: (i) Scaled physical experiments are performed to investigate the roughness influence of the wooden quay on a local scale. (ii) A numerical representation of the physical experiments is done in Delft3D, validating a linear loss term to accurately capture the roughness influence on the velocity distribution. (iii) A mid-scale Rhine river model of the area is generated that approximates the historic river bathymetry through morphological evolution. The quay-wall is implemented in parametric form and induces a substantial velocity reduction throughout the harbour. The unique structure exhibits hydromechanic properties mimicking present day current-deflection walls, potentially rendering it their primal prototype.

422 Million Q Planar Integrated All-Waveguide Resonator with a 3.4 Billion Absorption Limited Q, Sub-MHz Linewidth and 3005 Finesse

High Q optical resonators that are a key component for ultra-narrow linewidth lasers, frequency stabilization, precision spectroscopy and quantum applications. Integration of these resonators in a photonic waveguide wafer-scale platform is key to reducing their cost, size and power as well as sensitivity to environmental disturbances. However, to date, the intrinsic Q of integrated all-waveguide resonators has been relegated to below 150 Million for a non-etched waveguide resonator and 230 Million for a waveguide-coupled etched silica microresonator. Here, we report an all-waveguide Si3N4 resonator with an intrinsic Q of 422 Million and a 3.4 Billion absorption loss limited Q. The resonator linewidth measures at 453 kHz intrinsic linewidth, 906 kHz loaded linewidth with finesse of 3005. The corresponding linear loss of 0.060 dB/m is the lowest reported to date for an all-waveguide design with deposited upper cladding oxide. These are the highest intrinsic and absorption loss limited Q factors and lowest linewidth reported to date for a photonic integrated all-waveguide resonator. This level of performance is achieved through a careful reduction of scattering and absorption loss components and redeposition of a thin nitride layer. We quantify, simulate and measure the various loss contributions including scattering and absorption and describe a surface-state dangling bond absorption that we believe is passivated by the redeposited layer. In addition to the ultra-high Q and narrow linewidth, the resonator has a large optical mode area and volume, both critical for ultra-low laser linewidths and ultra-stable, ultra-low frequency noise reference cavities. These results demonstrate the performance of bulk optic and etched resonators can be realized in a photonic integrated solution, paving the way towards photonic integration compatible Billion Q cavities for precision scientific systems and applications such as nonlinear optics, atomic clocks, quantum photonics and high-capacity fiber communications systems on-chip.

Initial Optimal Parameters of Artificial Neural Network and Support Vector Regression

This paper presents architecture of backpropagation Artificial Neural Network (ANN) and Support Vector Regression (SVR) models in supervised learning process for cement demand dataset. This study aims to identify the effectiveness of each parameter of mean square error (MSE) indicators for time series dataset. The study varies different random sample in each demand parameter in the network of ANN and support vector function as well. The variations of percent datasets from activation function, learning rate of sigmoid and purelin, hidden layer, neurons, and training function should be applied for ANN. Furthermore, SVR is varied in kernel function, lost function and insensitivity to obtain the best result from its simulation. The best results of this study for ANN activation function is Sigmoid. The amount of data input is 100% or 96 of data, 150 learning rates, one hidden layer, trinlm training function, 15 neurons and 3 total layers. The best results for SVR are six variables that run in optimal condition, kernel function is linear, loss function is ౬-insensitive, and insensitivity was 1. The better results for both methods are six variables. The contribution of this study is to obtain the optimal parameters for specific variables of ANN and SVR.