Synergistic and Antagonistic Effects of Aromatics on the Agglomeration of Gas Hydrates

Surfactants are often used to stabilize aqueous dispersions. For example, surfactants can be used to prevent hydrate particles from forming large plugs that can clog, and sometimes rupture pipelines. Changes in oil composition, however dramatically affect the performance of said surfactants. In this work we demonstrate that aromatic compounds, dissolved in the hydrocarbon phase, can have both synergistic and antagonistic effects, depending on their molecular structure, with respect to surfactants developed to prevent hydrate agglomerations. While monocyclic aromatics such as benzene were found to disrupt the structure of surfactant films at low surfactant density, they are expelled from the interfacial film at high surfactant density. On the other hand, polycyclic aromatics, in particular pyrene, are found to induce order and stabilize the surfactant films both at low and high surfactant density. Based on our simulation results, polycyclic aromatics could behave as natural anti-agglomerants and enhance the performance of the specific surfactants considered here, while monocyclic aromatics could, in some cases, negatively affect performance. Although limited to the conditions chosen for the present simulations, the results, explained in terms of molecular features, could be valuable for better understanding synergistic and antagonistic effects relevant for stabilizing aqueous dispersions used in diverse applications, ranging from foodstuff to processing of nanomaterials and advanced manufacturing

A continuous non-linear shadowing model of columnar growth

We propose the first continuous model with long range screening (shadowing) that described columnar growth in one space dimension, as observed in plasma sputter deposition. It is based on a new continuous partial derivative equation with non-linear diffusion and where the shadowing effects apply on all the different processes.Comment: Fast Track Communicatio

Transitions between bursting modes in the integrated oscillator model for pancreatic β-cells

Insulin-secreting $\beta$-cells of pancreatic islets of Langerhans produce bursts of electrical impulses, resulting in intracellular Ca$^{2+}$ oscillations and pulsatile insulin secretion. The mechanism for this bursting activity has been the focus of mathematical modeling for more than three decades, and as new data are acquired old models are modified and new models are developed. Comprehensive models must now account for the various modes of bursting observed in islet $\beta$-cells, which include fast bursting, slow bursting, and compound bursting. One such model is the Integrated Oscillator Model (IOM), in which $\beta$-cell electrical activity, intracellular Ca$^{2+}$ , and glucose metabolism interact via numerous feedforward and feedback pathways. These interactions can produce metabolic oscillations with a sawtooth time course or a pulsatile time course, reflecting very different oscillation mechanisms. In this report, we determine conditions favorable to one form of oscillations or the other, and examine the transitions between modes of bursting and the relationship of the transitions to the patterns of metabolic oscillations. Importantly, this work clarifies what can be expected in experimental measurements of $\beta$-cell oscillatory activity, and suggests pathways through which oscillations of one type can be converted to oscillations of another type.R. Bertram was supported by grant number DMS-1612193 from the National Science Foundatio

Instantaneous frequency measurement system using optical mixing in highly nonlinear fiber

A broadband photonic instantaneous frequency measurement system utilizing four-wave mixing in highly nonlinear fiber is demonstrated. This new approach is highly stable and does not require any high-speed electronics or photodetectors. A first principles model accurately predicts the system response. Frequency measurement responses from 1 to 40 GHz are demonstrated and simple reconfiguration allows the system to operate over multiple bands

Federated Deep Reinforcement Learning-based Bitrate Adaptation for Dynamic Adaptive Streaming over HTTP

In video streaming over HTTP, the bitrate adaptation selects the quality of video chunks depending on the current network condition. Some previous works have applied deep reinforcement learning (DRL) algorithms to determine the chunk's bitrate from the observed states to maximize the quality-of-experience (QoE). However, to build an intelligent model that can predict in various environments, such as 3G, 4G, Wifi, \textit{etc.}, the states observed from these environments must be sent to a server for training centrally. In this work, we integrate federated learning (FL) to DRL-based rate adaptation to train a model appropriate for different environments. The clients in the proposed framework train their model locally and only update the weights to the server. The simulations show that our federated DRL-based rate adaptations, called FDRLABR with different DRL algorithms, such as deep Q-learning, advantage actor-critic, and proximal policy optimization, yield better performance than the traditional bitrate adaptation methods in various environments.Comment: 13 pages, 1 colum

Axial-flexural coupled vibration and buckling of composite beams using sinusoidal shear deformation theory

A finite element model based on sinusoidal shear deformation theory is developed to study vibration and buckling analysis of composite beams with arbitrary lay-ups. This theory satisfies the zero traction boundary conditions on the top and bottom surfaces of beam without using shear correction factors. Besides, it has strong similarity with Euler–Bernoulli beam theory in some aspects such as governing equations, boundary conditions, and stress resultant expressions. By using Hamilton’s principle, governing equations of motion are derived. A displacement-based one-dimensional finite element model is developed to solve the problem. Numerical results for cross-ply and angle-ply composite beams are obtained as special cases and are compared with other solutions available in the literature. A variety of parametric studies are conducted to demonstrate the effect of fiber orientation and modulus ratio on the natural frequencies, critical buckling loads, and load-frequency curves as well as corresponding mode shapes of composite beams

Cost-effective solutions and tools for medical image processing and design of personalised cranioplasty implants

Cranial defects which are caused by bone tumors or traffic accidents are treated by cranioplasty techniques. Cranioplasty implants are required to protect the underlying brain, correct major aesthetic deformities, or both. With the rapid develop-ment of computer graphics, medical image processing (MIP) and manufacturing technologies in recent decades, nowadays, personalised cranioplasty implants can be designed and made to improve the quality of cranial defect treatments. However, software tools for MIP and 3D modelling of implants are ex-pensive; and they normally require high technical skills. Espe-cially, the process of design and development of personalised cranioplasty implants normally requires a multidisciplinary team, including experts in MIP, 3D design and modelling, and Biomedical Engineering; this leads to challenges and difficulties for technology transfers and implementations in hospitals. This research is aimed at developing, in particular, cost-effective solutions and tools for design and modeling of per-sonalised cranioplasty implants, and to simplify the design and modelling of implants, as well as to reduce the design and modeling time. In this way, surgeons and engineers can con-veniently and easily design personalised cranioplasty implants, without the need of using complex MIP and CAD tools; and as a result the cost of implants will be minimised