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Ahmad, Rizwan,Anwar, Muhammad Shoaib,Kim, Jae,Song, In-Hyuck,Abbas, Syed Zaighum,Ali, Syed Ahmad,Ali, Fahad,Ahmad, Jamil,Bin Awais, Hasan,Mehmood, Mazhar Elsevier 2016 CERAMICS INTERNATIONAL Vol.42 No.16
<P><B>Abstract</B></P> <P>Bimodal porous structures were prepared by combining conventional sacrificial template and partial sintering methods. These porous structures were analysed by comparing pore characteristics and gas permeation properties of alumina/mullite specimens sintered at different temperatures. The pore characteristics were investigated by SEM, mercury porosimetry, and capillary flow porosimetry. A bimodal pore structure was observed. One type of pore was induced by starch, which acted as a sacrificial template. The other pore type was due to partial sintering. The pores produced by starch were between 2 and 10µm whereas those produced by partial sintering exhibited pore size of 0.1–0.5µm. The effects of sintering temperature on porosity, gas permeability, and mullite phase formation were studied. The formation of the mullite phase was confirmed by XRD. Compressive strengths of 37.9MPa and 12.4MPa with porosities of 65.3% and 70% were achieved in alumina and mullite specimens sintered at 1600°C.</P>
Implications of deep learning for the automation of design patterns organization
Hussain, Shahid,Keung, Jacky,Khan, Arif Ali,Ahmad, Awais,Cuomo, Salvatore,Piccialli, Francesco,Jeon, Gwanggil,Akhunzada, Adnan Elsevier 2018 Journal of parallel and distributed computing Vol.117 No.-
<P><B>Abstract</B></P> <P>Though like other domains such as email filtering, web page classification, sentiment analysis, and author identification, the researchers have employed the text categorization approach to automate organization and selection of design patterns. However, there is a need to bridge the gap between the semantic relationship between design patterns (i.e. Documents) and the features which are used for the organization of design patterns. In this study, we propose an approach by leveraging a powerful deep learning algorithm named Deep Belief Network (DBN) which learns on the semantic representation of documents formulated in the form of feature vectors. We performed a case study in the context of a text categorization based automated system used for the classification and selection of software design patterns. In the case study, we focused on two main research objectives: 1) to empirically investigate the effect of feature sets constructed through the global filter-based feature selection methods besides the proposed approach, and 2) to evaluate the significant improvement in the classification decision (i.e. Pattern organization) of classifiers using the proposed approach. The adjustment of DBN parameters such as a number of hidden layers, nodes and iteration can aid a developer to construct a more illustrative feature set. The experimental promising results suggest the significance of the proposed approach to construct a more representative feature set and improve the classifier’s performance in terms of organization of design patterns.</P> <P><B>Highlights</B></P> <P> <UL> <LI> There is a need to bridge the gap between the semantic relationship between patterns. </LI> <LI> We propose an approach by leveraging a powerful deep learning algorithm named Deep Belief Network (DBN). </LI> <LI> The DBN learns on the semantic representation of documents formulated in the form of feature vectors. </LI> <LI> We performed a case study in the context of a text categorization based automated system. </LI> <LI> The experimental promising results suggest the significance of the proposed approach to construct a more representative feature set. </LI> </UL> </P>
Xin Xiong Chang,Nabisab Mujawar Mubarak,Shaukat Ali Mazari,Abdul Sattar Jatoi,Awais Ahmad,Mohammad Khalid,Rashmi Walvekar,E.C. Abdullah,Rama Rao Karri,M.T.H Siddiqui,Sabzoi Nizamuddin 한국공업화학회 2021 Journal of Industrial and Engineering Chemistry Vol.104 No.-
The concept of green chemistry has attracted attention due to the green synthesis and ecofriendly natureof the compounds leading to the green and sustainable chemical industries and processes. Chitosan is anecofriendly material, which is biodegradable, non-toxic, and biocompatible. It has the potential to bemodified into biofilms for various applications such as biomedical, packaging, and pharmaceutical fields. Nevertheless, some poor properties of chitosan restrict its wide applications. The incorporation ofnanocellulose fillers into chitosan matrix can enhance the mechanical and thermal properties of chitosan. Cellulose nanomaterials can be achieved through chemical and mechanical modifications. The commontype of nanocellulose are cellulose nanofibers (CNFs), cellulose nano-whiskers (CNWs), tunicate CNCs (t-CNCs), algae cellulose particles (AC) and bacterial cellulose particles (BC). Nanocellulose are applied asthe reinforcement fillers in various polymer matrices such as polysaccharides, proteins, lipids, polylacticacid etc. Deep eutectic solvents (DES) are relatively novel green solvents, which can be applied in variousfields. DES are widely applied in metal processing, polymer processing and synthesis. Even though thereare not much studies available on DES for synthesis of nanocomposite films; however they are used aseco-friendly solvents in manufacturing processes. This study reviews the discovery, structure, propertiesof chitosan and cellulose, their derivatives and applications. In addition, the paper also discusses theproperties of DES and their applications.