Kent State University

About: Kent State University is a education organization based out in Kent, Ohio, United States. It is known for research contribution in the topics: Liquid crystal & Population. The organization has 10897 authors who have published 24607 publications receiving 720309 citations. The organization is also known as: Kent State & KSU.

Phosphorus‐Doped Graphitic Carbon Nitrides Grown In Situ on Carbon‐Fiber Paper: Flexible and Reversible Oxygen Electrodes

Abstract: Flexible non-metal oxygen electrodes fabricated from phosphorus-doped graphitic carbon nitride nano-flowers directly grown on carbon-fiber paper exhibit high activity and stability in reversibly catalyzing oxygen reduction and evolution reactions, which is a result of N, P dual action, enhanced mass/charge transfer, and high active surface area The performance is comparable to that of the state-of-the-art transition-metal, noble-metal, and non-metal catalysts Remarkably, the flexible nature of these oxygen electrodes allows their use in folded and rolled-up forms, and directly as cathodes in Zn–air batteries, featuring low charge/discharge overpotential and long lifetime

Recovering documentation-to-source-code traceability links using latent semantic indexing

Abstract: An information retrieval technique, latent semantic indexing, is used to automatically identify traceability links from system documentation to program source code. The results of two experiments to identify links in existing software systems (i.e., the LEDA library, and Albergate) are presented. These results are compared with other similar type experimental results of traceability link identification using different types of information retrieval techniques. The method presented proves to give good results by comparison and additionally it is a low cost, highly flexible method to apply with regards to preprocessing and/or parsing of the source code and documentation.

Learning semantic representations using convolutional neural networks for web search

Abstract: This paper presents a series of new latent semantic models based on a convolutional neural network (CNN) to learn low-dimensional semantic vectors for search queries and Web documents. By using the convolution-max pooling operation, local contextual information at the word n-gram level is modeled first. Then, salient local fea-tures in a word sequence are combined to form a global feature vector. Finally, the high-level semantic information of the word sequence is extracted to form a global vector representation. The proposed models are trained on clickthrough data by maximizing the conditional likelihood of clicked documents given a query, us-ing stochastic gradient ascent. The new models are evaluated on a Web document ranking task using a large-scale, real-world data set. Results show that our model significantly outperforms other se-mantic models, which were state-of-the-art in retrieval performance prior to this work.

Thermal Conductivity and Particle Agglomeration in Alumina Nanofluids: Experiment and Theory

Abstract: In recent years many experimentalists have reported an anomalously enhanced thermal conductivity in liquid suspensions of nanoparticles. Despite the importance of this effect for heat transfer applications, no agreement has emerged about the mechanism of this phenomenon, or even about the experimentally observed magnitude of the enhancement. To address these issues, this paper presents a combined experimental and theoretical study of heat conduction and particle agglomeration in nanofluids. On the experimental side, nanofluids of alumina particles in water and ethylene glycol are characterized using thermal conductivity measurements, viscosity measurements, dynamic light scattering, and other techniques. The results show that the particles are agglomerated, with an agglomeration state that evolves in time. The data also show that the thermal conductivity enhancement is within the range predicted by effective medium theory. On the theoretical side, a model is developed for heat conduction through a fluid containing nanoparticles and agglomerates of various geometries. The calculations show that elongated and dendritic structures are more efficient in enhancing the thermal conductivity than compact spherical structures of the same volume fraction, and that surface (Kapitza) resistance is the major factor resulting in the lower than effective medium conductivities measured in our experiments. Together, these results imply that the geometry, agglomeration state, and surface resistance of nanoparticles are the main variables controlling thermal conductivity enhancement in nanofluids.