5,235 research outputs found
Deceleration of relativistic jets with lateral expansion
We present a model for the hydrodynamics of a relativistic jet interacting with the circum-stellar medium (CSM). The shocked CSM and the jet material are assumed to be in an infinitely thin surface, so the original 2D problem is effectively reduced to 1D. From general conservation laws, we derive the equation of motion for each fluid element along this surface, taking into account the deceleration along the surface normal due to newly swept-up mass and lateral expansion due to pressure gradient in the tangential direction. The pressure and energy density of the shocked CSM are given by the jump conditions at the forward shock. The method is implemented with a finite-differencing numerical scheme, along with calculation of synchrotron emission and absorption from shock-accelerated electrons, in a new code Jedi (for "jet dynamics"). We present a number of test cases, including top-hat jet, power-law structured jet, "boosted fireball" profile, and CSM with density jump at the wind termination shock. Based on the agreement with other analytical and numerical calculations, we conclude that our simplified method provides a good approximation for the hydrodynamics and afterglow emission for a wide variety of jet structures and CSM density profiles. Efficient modeling of the afterglow from e.g., neutron star mergers, will provide important information on the jet energetics, CSM properties, and the viewing angle
Deceleration of relativistic jets with lateral expansion
We present a model for the hydrodynamics of a relativistic jet interacting with the circum-stellar medium (CSM). The shocked CSM and the jet material are assumed to be in an infinitely thin surface, so the original 2D problem is effectively reduced to 1D. From general conservation laws, we derive the equation of motion for each fluid element along this surface, taking into account the deceleration along the surface normal due to newly swept-up mass and lateral expansion due to pressure gradient in the tangential direction. The pressure and energy density of the shocked CSM are given by the jump conditions at the forward shock. The method is implemented with a finite-differencing numerical scheme, along with calculation of synchrotron emission and absorption from shock-accelerated electrons, in a new code Jedi (for "jet dynamics"). We present a number of test cases, including top-hat jet, power-law structured jet, "boosted fireball" profile, and CSM with density jump at the wind termination shock. Based on the agreement with other analytical and numerical calculations, we conclude that our simplified method provides a good approximation for the hydrodynamics and afterglow emission for a wide variety of jet structures and CSM density profiles. Efficient modeling of the afterglow from e.g., neutron star mergers, will provide important information on the jet energetics, CSM properties, and the viewing angle
Wireless Authentication of Smart Doors Using RFID
In an increasingly interconnected world, the traditional metal lock-and-key method of securing homes and businesses is becoming more outdated and inconvenient when compared to modern solutions. Modern systems are smarter, faster, lighter, more secure and more integrated than ever before. Though the costs of Radio Frequency Identification (RFID) are shrinking overall, professional solutions remain prohibitively expensive. The aim of this project is to develop an inexpensive, secure, and internet-enabled RFID door authentication system. This is to transparently investigate the design limitations for these systems and help determine the ultimate feasibility of RFID growing to define the modern door-authentication standard
Marine stratocumulus aerosol-cloud relationships in the MASE-II experiment: Precipitation susceptibility in eastern Pacific marine stratocumulus
Observational data on aerosol-cloud-drizzle relationships in marine stratocumulus are presented from the second Marine Stratus/Stratocumulus Experiment (MASE-II) carried out in July 2007 over the eastern Pacific near Monterey, California. Observations, carried out in regions of essentially uniform meteorology with localized aerosol enhancements due to ship exhaust (“ship tracks”), demonstrate, in accord with those from numerous other field campaigns, that increased cloud drop number concentration Nc and decreased cloud top effective radius r_e are associated with increased subcloud aerosol concentration. Modulation of drizzle by variations in aerosol levels is
levels is clearly evident.
Variations of cloud base drizzle rate R_(cb) are found to be consistent with the proportionality,
R_(cb) / H^3/N_c, where H is cloud depth. Simultaneous aircraft and A-Train satellite
observations are used to quantify the precipitation susceptibility of clouds to aerosol
perturbations in the eastern Pacific region
An interpretable deep learning method for bearing fault diagnosis
Deep learning (DL) has gained popularity in recent years as an effective tool
for classifying the current health and predicting the future of industrial
equipment. However, most DL models have black-box components with an underlying
structure that is too complex to be interpreted and explained to human users.
This presents significant challenges when deploying these models for
safety-critical maintenance tasks, where non-technical personnel often need to
have complete trust in the recommendations these models give. To address these
challenges, we utilize a convolutional neural network (CNN) with
Gradient-weighted Class Activation Mapping (Grad-CAM) activation map
visualizations to form an interpretable DL method for classifying bearing
faults. After the model training process, we apply Grad-CAM to identify a
training sample's feature importance and to form a library of diagnosis
knowledge (or health library) containing training samples with annotated
feature maps. During the model evaluation process, the proposed approach
retrieves prediction basis samples from the health library according to the
similarity of the feature importance. The proposed method can be easily applied
to any CNN model without modifying the model architecture, and our experimental
results show that this method can select prediction basis samples that are
intuitively and physically meaningful, improving the model's trustworthiness
for human users
A 3D-Shape Similarity-based Contrastive Approach to Molecular Representation Learning
Molecular shape and geometry dictate key biophysical recognition processes,
yet many graph neural networks disregard 3D information for molecular property
prediction. Here, we propose a new contrastive-learning procedure for graph
neural networks, Molecular Contrastive Learning from Shape Similarity
(MolCLaSS), that implicitly learns a three-dimensional representation. Rather
than directly encoding or targeting three-dimensional poses, MolCLaSS matches a
similarity objective based on Gaussian overlays to learn a meaningful
representation of molecular shape. We demonstrate how this framework naturally
captures key aspects of three-dimensionality that two-dimensional
representations cannot and provides an inductive framework for scaffold
hopping
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