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  1. Water plays a critical role in our living and manufacturing activities. The continuously growing exploitation of water over the aquifer poses a risk for over-extraction and pollution, leading to many negative effects on land irrigation. Therefore, predicting aquifer water levels accurately is urgently important, which can help us prepare water demands ahead of time. In this study, we employ the Long-Short Term Memory (LSTM) model to predict the saturated thickness of an aquifer in the Southern High Plains Aquifer System in Texas and exploit TensorBoard as a guide for model configurations. The Root Mean Squared Error of this study showsmore »that the LSTM model can provide a good prediction capability using multiple data sources, and provides a good visualization tool to help us understand and evaluate the model configuration.« less
  2. Loess covers large areas around the earth. Loess deposits are typically composed of silt with clay and fine sand particles and it is usually distributed with a few meters thick. Literature review shows that, the thermal conductivity of loess varies in a relatively large range from 0.2 to 2 W/(mK), depending on the particle composition, texture and moisture content of soil. In this study, loess samples were taken at shallow depth from the Northern France. Suction, volumetric moisture content and thermal conductivity of soil were measured simultaneously while wetting/drying cycles were applied to the sample. The results show that, themore »degree of saturation significantly affects the thermal conductivity of the soil. The relationship between these two parameters is reversible under wetting/drying cycles while hysteresis can be observed while plotting the thermal conductivity versus suction.« less
  3. The gradual deployment of intelligent and coordinated devices in the electrical power system needs careful investigation of the interactions between the various domains involved. Especially due to the coupling between ICT and power systems a holistic approach for testing and validating is required. Taking existing (quasi-) standardised smart grid system and test specification methods as a starting point, we are developing a holistic testing and validation approach that allows a very flexible way of assessing the system level aspects by various types of experiments (including virtual, real, and mixed lab settings). This paper describes the formal holistic test case specificationmore »method and applies it to a particular co-simulation experimental setup. The various building blocks of such a simulation (i.e., FMI, mosaik, domain-specific simulation federates) are covered in more detail. The presented method addresses most modeling and specification challenges in cyber-physical energy systems and is extensible for future additions such as uncertainty quantification.« less
  4. Smart grid systems are characterized by high complexity due to interactions between a traditional passive network and active power electronic components, coupled using communication links. Additionally, automation and information technology plays an important role in order to operate and optimize such cyber-physical energy systems with a high(er) penetration of fluctuating renewable generation and controllable loads. As a result of these developments the validation on the system level becomes much more important during the whole engineering and deployment process, today. In earlier development stages and for larger system configurations laboratory-based testing is not always an option. Due to recent developments, simulation-basedmore »approaches are now an appropriate tool to support the development, implementation, and roll-out of smart grid solutions. This paper discusses the current state of simulation-based approaches and outlines the necessary future research and development directions in the domain of power and energy systems.« less
  5. Free, publicly-accessible full text available March 1, 2023
  6. Free, publicly-accessible full text available January 1, 2023
  7. A bstract A search is presented for new particles produced at the LHC in proton-proton collisions at $$ \sqrt{s} $$ s = 13 TeV, using events with energetic jets and large missing transverse momentum. The analysis is based on a data sample corresponding to an integrated luminosity of 101 fb − 1 , collected in 2017–2018 with the CMS detector. Machine learning techniques are used to define separate categories for events with narrow jets from initial-state radiation and events with large-radius jets consistent with a hadronic decay of a W or Z boson. A statistical combination is made with anmore »earlier search based on a data sample of 36 fb − 1 , collected in 2016. No significant excess of events is observed with respect to the standard model background expectation determined from control samples in data. The results are interpreted in terms of limits on the branching fraction of an invisible decay of the Higgs boson, as well as constraints on simplified models of dark matter, on first-generation scalar leptoquarks decaying to quarks and neutrinos, and on models with large extra dimensions. Several of the new limits, specifically for spin-1 dark matter mediators, pseudoscalar mediators, colored mediators, and leptoquarks, are the most restrictive to date.« less
    Free, publicly-accessible full text available November 1, 2022