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