Simulation of a 4-bed silica-gel-water adsorption chiller

Master'sMASTER OF ENGINEERIN

Joint Channel Assignment and Opportunistic Routing for Maximizing Throughput in Cognitive Radio Networks

In this paper, we consider the joint opportunistic routing and channel assignment problem in multi-channel multi-radio (MCMR) cognitive radio networks (CRNs) for improving aggregate throughput of the secondary users. We first present the nonlinear programming optimization model for this joint problem, taking into account the feature of CRNs-channel uncertainty. Then considering the queue state of a node, we propose a new scheme to select proper forwarding candidates for opportunistic routing. Furthermore, a new algorithm for calculating the forwarding probability of any packet at a node is proposed, which is used to calculate how many packets a forwarder should send, so that the duplicate transmission can be reduced compared with MAC-independent opportunistic routing & encoding (MORE) [11]. Our numerical results show that the proposed scheme performs significantly better that traditional routing and opportunistic routing in which channel assignment strategy is employed.Comment: 5 pages, 4 figures, to appear in Proc. of IEEE GlobeCom 201

The Narcissistic Gaze in Pipilotti Rist’s Video Work Mutaflor (1996)

The project focuses on the research of the visual culture in the context of western societies from the 1970s to the 1990s and the art criticism on video art during that time. The final product of this project is an art criticism on the video artwork Mutaflor, made by Pipilotti Rist, an internationally renowned contemporary video and installation artist

On the existence and structures of almost axisymmetric solutions to 3-D Navier-Stokes equations

In this paper, we consider 3-D Navier-Stokes equations with almost axisymmetric initial data, which means that by writing $u_0 =u^r_0 e_r+u^\theta_0 e_\theta+u^z_0 e_z$ in the cylindrical coordinates, then $\partial_\theta u^r_0,\,\partial_\theta u^\theta_0$ and $\partial_\theta u^z_0$ are small in some sense (recall axisymmetric means these three quantities vanish). Then with additional smallness assumption on $u^\theta_0$, we prove the global existence of a unique strong solution $u$, and this solution keeps close to some axisymmetric vector field. We also establish some refined estimates for the integral average in $\theta$ variable for $u$. Moreover, as $u^r_0,\,u^\theta_0$ and $u^z_0$ here depend on $\theta$, it is natural to expand them into Fourier series in $\theta$ variable. And we shall consider one special form of $u_0$, with some small parameter $\varepsilon$ to measure its swirl part and oscillating part. We study the asymptotic expansion of the corresponding solution, and the influences between different profiles in the asymptotic expansion. In particular, we give some special symmetric structures that will persist for all time. These phenomena reflect some features of the nonlinear terms in Navier-Stokes equations

Code Completion by Modeling Flattened Abstract Syntax Trees as Graphs

Code completion has become an essential component of integrated development environments. Contemporary code completion methods rely on the abstract syntax tree (AST) to generate syntactically correct code. However, they cannot fully capture the sequential and repetitive patterns of writing code and the structural information of the AST. To alleviate these problems, we propose a new code completion approach named CCAG, which models the flattened sequence of a partial AST as an AST graph. CCAG uses our proposed AST Graph Attention Block to capture different dependencies in the AST graph for representation learning in code completion. The sub-tasks of code completion are optimized via multi-task learning in CCAG, and the task balance is automatically achieved using uncertainty without the need to tune task weights. The experimental results show that CCAG has superior performance than state-of-the-art approaches and it is able to provide intelligent code completion.Comment: Accepted in AAAI 2021. This version contains the appendix for the derivation of Eq. 1

Differential geometric approach of Betchov-Da Rios soliton equation

MakaleWOS:000964265900009PubMed ID: 35296227In the present paper, we investigate differential geometric properties the soliton surface M associated with Betchov-Da Rios equation. Then, we give derivative formulas of Frenet frame of unit speed curve 4) = 4)(s, t) for all t. Also, we discuss the linear map of Weingarten type in the tangent space of the surface that generates two invariants: k and h. Moreover, we obtain the necessary and sufficient conditions for the soliton surface associated with Betchov-Da Rios equation to be a minimal surface. Finally, we examine a soliton surface associated with Betchov-Da Rios equation as an application. Mathematics Subject Classification (2020). 35Q55, 53A05National Natural Science Foundation of China (NSFC

Enabling CMF Estimation in Data-Constrained Scenarios: A Semantic-Encoding Knowledge Mining Model

Precise estimation of Crash Modification Factors (CMFs) is central to evaluating the effectiveness of various road safety treatments and prioritizing infrastructure investment accordingly. While customized study for each countermeasure scenario is desired, the conventional CMF estimation approaches rely heavily on the availability of crash data at given sites. This not only makes the estimation costly, but the results are also less transferable, since the intrinsic similarities between different safety countermeasure scenarios are not fully explored. Aiming to fill this gap, this study introduces a novel knowledge-mining framework for CMF prediction. This framework delves into the connections of existing countermeasures and reduces the reliance of CMF estimation on crash data availability and manual data collection. Specifically, it draws inspiration from human comprehension processes and introduces advanced Natural Language Processing (NLP) techniques to extract intricate variations and patterns from existing CMF knowledge. It effectively encodes unstructured countermeasure scenarios into machine-readable representations and models the complex relationships between scenarios and CMF values. This new data-driven framework provides a cost-effective and adaptable solution that complements the case-specific approaches for CMF estimation, which is particularly beneficial when availability of crash data or time imposes constraints. Experimental validation using real-world CMF Clearinghouse data demonstrates the effectiveness of this new approach, which shows significant accuracy improvements compared to baseline methods. This approach provides insights into new possibilities of harnessing accumulated transportation knowledge in various applications.Comment: 39 pages, 9 figure