Reprocessable thermosets for sustainable three-dimensional printing

Among all three-dimensional (3D) printing materials, thermosetting photopolymers claim almost half of the market, and have been widely used in various fields owing to their superior mechanical stability at high temperatures, excellent chemical resistance as well as good compatibility with high-resolution 3D printing technologies. However, once these thermosetting photopolymers form 3D parts through photopolymerization, the covalent networks are permanent and cannot be reprocessed, i.e., reshaped, repaired, or recycled. Here, we report a two-step polymerization strategy to develop 3D printing reprocessable thermosets (3DPRTs) that allow users to reform a printed 3D structure into a new arbitrary shape, repair a broken part by simply 3D printing new material on the damaged site, and recycle unwanted printed parts so the material can be reused for other applications. These 3DPRTs provide a practical solution to address environmental challenges associated with the rapid increase in consumption of 3D printing materials

Gender Detection on Social Networks using Ensemble Deep Learning

Analyzing the ever-increasing volume of posts on social media sites such as Facebook and Twitter requires improved information processing methods for profiling authorship. Document classification is central to this task, but the performance of traditional supervised classifiers has degraded as the volume of social media has increased. This paper addresses this problem in the context of gender detection through ensemble classification that employs multi-model deep learning architectures to generate specialized understanding from different feature spaces

FSL-BM: Fuzzy Supervised Learning with Binary Meta-Feature for Classification

This paper introduces a novel real-time Fuzzy Supervised Learning with Binary Meta-Feature (FSL-BM) for big data classification task. The study of real-time algorithms addresses several major concerns, which are namely: accuracy, memory consumption, and ability to stretch assumptions and time complexity. Attaining a fast computational model providing fuzzy logic and supervised learning is one of the main challenges in the machine learning. In this research paper, we present FSL-BM algorithm as an efficient solution of supervised learning with fuzzy logic processing using binary meta-feature representation using Hamming Distance and Hash function to relax assumptions. While many studies focused on reducing time complexity and increasing accuracy during the last decade, the novel contribution of this proposed solution comes through integration of Hamming Distance, Hash function, binary meta-features, binary classification to provide real time supervised method. Hash Tables (HT) component gives a fast access to existing indices; and therefore, the generation of new indices in a constant time complexity, which supersedes existing fuzzy supervised algorithms with better or comparable results. To summarize, the main contribution of this technique for real-time Fuzzy Supervised Learning is to represent hypothesis through binary input as meta-feature space and creating the Fuzzy Supervised Hash table to train and validate model.Comment: FICC201

Ionic liquids at electrified interfaces

Until recently, “room-temperature” (<100–150 °C) liquid-state electrochemistry was mostly electrochemistry of diluted electrolytes(1)–(4) where dissolved salt ions were surrounded by a considerable amount of solvent molecules. Highly concentrated liquid electrolytes were mostly considered in the narrow (albeit important) niche of high-temperature electrochemistry of molten inorganic salts(5-9) and in the even narrower niche of “first-generation” room temperature ionic liquids, RTILs (such as chloro-aluminates and alkylammonium nitrates).(10-14) The situation has changed dramatically in the 2000s after the discovery of new moisture- and temperature-stable RTILs.(15, 16) These days, the “later generation” RTILs attracted wide attention within the electrochemical community.(17-31) Indeed, RTILs, as a class of compounds, possess a unique combination of properties (high charge density, electrochemical stability, low/negligible volatility, tunable polarity, etc.) that make them very attractive substances from fundamental and application points of view.(32-38) Most importantly, they can mix with each other in “cocktails” of one’s choice to acquire the desired properties (e.g., wider temperature range of the liquid phase(39, 40)) and can serve as almost “universal” solvents.(37, 41, 42) It is worth noting here one of the advantages of RTILs as compared to their high-temperature molten salt (HTMS)(43) “sister-systems”.(44) In RTILs the dissolved molecules are not imbedded in a harsh high temperature environment which could be destructive for many classes of fragile (organic) molecules

Web of Science

Copyright (c) 2017 Kamran Kowsari Permission is hereby granted, free of charge, to any person obtaining a copy of this dataset and associated documentation files (the "Dataset"), to deal in the dataset without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Dataset, and to permit persons to whom the dataset is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Dataset. If you use this dataset please cite: Referenced paper: HDLTex: Hierarchical Deep Learning for Text Classification Description of Dataset: Here is three datasets which include WOS-11967 , WOS-46985, and WOS-5736 Each folder contains: -X.txt -Y.txt -YL1.txt -YL2.txt X is input data that include text sequences Y is target value YL1 is target value of level one (parent label) YL2 is target value of level one (child label) Meta-data: This folder contain on data file as following attribute: Y1 Y2 Y Domain area keywords Abstract Abstract is input data that include text sequences of 46,985 published paper Y is target value YL1 is target value of level one (parent label) YL2 is target value of level one (child label) Domain is majaor domain which include 7 labales: {Computer Science,Electrical Engineering, Psychology, Mechanical Engineering,Civil Engineering, Medical Science, biochemistry} area is subdomain or area of the paper such as CS-> computer graphics which contain 134 labels. keywords : is authors keyword of the papers Web of Science Dataset WOS-11967 -This dataset contains 11,967 documents with 35 categories which include 7 parents categories. Web of Science Dataset WOS-46985 -This dataset contains 46,985 documents with 134 categories which include 7 parents categories. Web of Science Dataset WOS-5736 -This dataset contains 5,736 documents with 11 categories which include 3 parents categories. Referenced paper: HDLTex: Hierarchical Deep Learning for Text Classification Bib: @inproceedings{kowsari2017HDLTex, title={HDLTex: Hierarchical Deep Learning for Text Classification}, author={Kowsari, Kamran and Brown, Donald E and Heidarysafa, Mojtaba and Jafari Meimandi, Kiana and and Gerber, Matthew S and Barnes, Laura E}, booktitle={Machine Learning and Applications (ICMLA), 2017 16th IEEE International Conference on}, year={2017}, organization={IEEE}

Web of Science

Copyright (c) 2017 Kamran Kowsari Permission is hereby granted, free of charge, to any person obtaining a copy of this dataset and associated documentation files (the "Dataset"), to deal in the dataset without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Dataset, and to permit persons to whom the dataset is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Dataset. If you use this dataset please cite: Referenced paper: HDLTex: Hierarchical Deep Learning for Text Classification Description of Dataset: Here is three datasets which include WOS-11967 , WOS-46985, and WOS-5736 Each folder contains: -X.txt -Y.txt -YL1.txt -YL2.txt X is input data that include text sequences Y is target value YL1 is target value of level one (parent label) YL2 is target value of level one (child label) Meta-data: This folder contain on data file as following attribute: Y1 Y2 Y Domain area keywords Abstract Abstract is input data that include text sequences of 46,985 published paper Y is target value YL1 is target value of level one (parent label) YL2 is target value of level one (child label) Domain is majaor domain which include 7 labales: {Computer Science,Electrical Engineering, Psychology, Mechanical Engineering,Civil Engineering, Medical Science, biochemistry} area is subdomain or area of the paper such as CS-&gt; computer graphics which contain 134 labels. keywords : is authors keyword of the papers Web of Science Dataset WOS-11967 -This dataset contains 11,967 documents with 35 categories which include 7 parents categories. Web of Science Dataset WOS-46985 -This dataset contains 46,985 documents with 134 categories which include 7 parents categories. Web of Science Dataset WOS-5736 -This dataset contains 5,736 documents with 11 categories which include 3 parents categories. Referenced paper: HDLTex: Hierarchical Deep Learning for Text Classification Bib: @inproceedings{kowsari2017HDLTex, title={HDLTex: Hierarchical Deep Learning for Text Classification}, author={Kowsari, Kamran and Brown, Donald E and Heidarysafa, Mojtaba and Jafari Meimandi, Kiana and and Gerber, Matthew S and Barnes, Laura E}, booktitle={Machine Learning and Applications (ICMLA), 2017 16th IEEE International Conference on}, year={2017}, organization={IEEE}

LabVIEW image display and stage control code

LabVIEW image display and stage control codeTHIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Web of Science

Copyright (c) 2017 Kamran Kowsari Permission is hereby granted, free of charge, to any person obtaining a copy of this dataset and associated documentation files (the "Dataset"), to deal in the dataset without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Dataset, and to permit persons to whom the dataset is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Dataset. If you use this dataset please cite: Referenced paper: HDLTex: Hierarchical Deep Learning for Text Classification Description of Dataset: Here is three datasets which include WOS-11967 , WOS-46985, and WOS-5736 Each folder contains: -X.txt -Y.txt -YL1.txt -YL2.txt X is input data that include text sequences Y is target value YL1 is target value of level one (parent label) YL2 is target value of level one (child label) Meta-data: This folder contain on data file as following attribute: Y1 Y2 Y Domain area keywords Abstract Abstract is input data that include text sequences of 46,985 published paper Y is target value YL1 is target value of level one (parent label) YL2 is target value of level one (child label) Domain is majaor domain which include 7 labales: {Computer Science,Electrical Engineering, Psychology, Mechanical Engineering,Civil Engineering, Medical Science, biochemistry} area is subdomain or area of the paper such as CS-> computer graphics which contain 134 labels. keywords : is authors keyword of the papers Web of Science Dataset WOS-11967 -This dataset contains 11,967 documents with 35 categories which include 7 parents categories. Web of Science Dataset WOS-46985 -This dataset contains 46,985 documents with 134 categories which include 7 parents categories. Web of Science Dataset WOS-5736 -This dataset contains 5,736 documents with 11 categories which include 3 parents categories. Referenced paper: HDLTex: Hierarchical Deep Learning for Text Classification Bib: @inproceedings{kowsari2017HDLTex, title={HDLTex: Hierarchical Deep Learning for Text Classification}, author={Kowsari, Kamran and Brown, Donald E and Heidarysafa, Mojtaba and Jafari Meimandi, Kiana and and Gerber, Matthew S and Barnes, Laura E}, booktitle={Machine Learning and Applications (ICMLA), 2017 16th IEEE International Conference on}, year={2017}, organization={IEEE}

Web of Science Dataset

Copyright (c) 2017 Kamran Kowsari Permission is hereby granted, free of charge, to any person obtaining a copy of this dataset and associated documentation files (the "Dataset"), to deal in the dataset without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Dataset, and to permit persons to whom the dataset is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Dataset. If you use this dataset please cite: Referenced paper: HDLTex: Hierarchical Deep Learning for Text Classification Description of Dataset: Here is three datasets which include WOS-11967 , WOS-46985, and WOS-5736 Each folder contains: -X.txt -Y.txt -YL1.txt -YL2.txt X is input data that include text sequences Y is target value YL1 is target value of level one (parent label) YL2 is target value of level one (child label) Meta-data: This folder contain on data file as following attribute: Y1 Y2 Y Domain area keywords Abstract Abstract is input data that include text sequences of 46,985 published paper Y is target value YL1 is target value of level one (parent label) YL2 is target value of level one (child label) Domain is majaor domain which include 7 labales: {Computer Science,Electrical Engineering, Psychology, Mechanical Engineering,Civil Engineering, Medical Science, biochemistry} area is subdomain or area of the paper such as CS-&gt; computer graphics which contain 134 labels. keywords : is authors keyword of the papers Web of Science Dataset WOS-11967 -This dataset contains 11,967 documents with 35 categories which include 7 parents categories. Web of Science Dataset WOS-46985 -This dataset contains 46,985 documents with 134 categories which include 7 parents categories. Web of Science Dataset WOS-5736 -This dataset contains 5,736 documents with 11 categories which include 3 parents categories. Referenced paper: HDLTex: Hierarchical Deep Learning for Text Classification Bib: @inproceedings{kowsari2017HDLTex, title={HDLTex: Hierarchical Deep Learning for Text Classification}, author={Kowsari, Kamran and Brown, Donald E and Heidarysafa, Mojtaba and Jafari Meimandi, Kiana and and Gerber, Matthew S and Barnes, Laura E}, booktitle={Machine Learning and Applications (ICMLA), 2017 16th IEEE International Conference on}, year={2017}, organization={IEEE}