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Ali, F.,Kim, Y.S.,Lee, J.W.,Cheong, W.J. Elsevier 2014 Journal of chromatography Vol.1324 No.-
Dibutyltin dichloride (DBTDC) was used as a catalyst to chemically bind 4-chloromehtylphenylisocynate (4-CPI) to porous monolithic silica particles via isocyanate-hydroxyl reaction, and the reaction product was reacted with sodium diethyldithiocarbamate (SDDC) to yield initiator attached silica monolith particles. Reversible addition-fragmentation transfer (RAFT) polymerization was taken place on them to result in polystyrene attached silica particles that showed excellent separation efficiency when packed in a chromatographic column (1.0mmx300mm). The numbers of theoretical plates (N) of 56,500 is better than those of any commercially available HPLC or UHPLC column yet.
Ali, F.,Cheong, W.J.,ALOthman, Z.A.,ALMajid, A.M. Elsevier 2013 Journal of chromatography A Vol.1303 No.-
Partially sub-2μm porous silica monolith particles have been synthesized by a renovated procedure and modified to polystyrene coated silica particles with excellent separation efficiency when used as chromatographic media. In the procedure of preparing silica monolith particles in this study, subtle control of formulation of the reaction mixture and multi-step heating followed by calcination, without any washing and sieving process, enabled formation of silica particles characterized by proper particle and pore size distribution for high separation efficiency. 3-Chloropropyl trimethoxysilane was used as the halogen terminal spacer to combine the initiator to silica particles. Uniform and thin coating of polystyrene layer on initiator attached silica particles was formed via reversible addition-fragmentation chain transfer (RAFT) polymerization. Micro-columns (1.0mm ID and 300mm length) were packed with the resultant phase and their chromatographic performance was elucidated by HPLC. A mobile phase of 60/40 (v/v) acetonitrile/water containing 0.1% TFA and a flow rate of 15μL/min were found to be the optimized conditions leading to number of theoretical plates close to 50,000 (165,000m<SUP>-1</SUP>). This is the very first study to get such highly efficient HPLC columns using a silica monolith particulate stationary phase.
Ali, F.,Kim, E.K.,Kim, Y.G. Elsevier science 2015 Information sciences Vol.295 No.-
The volume of obstacles encountered in the marine environment is rapidly increasing, which makes the development of collision avoidance systems more challenging. Several fuzzy ontology-based simulators have been proposed to provide a virtual platform for the analysis of maritime missions. However, due to the simulators' limitations, ontology-based knowledge cannot be utilized to evaluate maritime robot algorithms and to avoid collisions. The existing simulators must be equipped with smart semantic domain knowledge to provide an efficient framework for the decision-making system of AUVs. This article presents type-2 fuzzy ontology-based semantic knowledge (T2FOBSK) and a simulator for marine users that will reduce experimental time and the cost of marine robots and will evaluate algorithms intelligently. The system reformulates the user's query to extract the positions of AUVs and obstacles and convert them to a proper format for the simulator. The simulator uses semantic knowledge to calculate the degree of collision risk and to avoid obstacles. The available type-1 fuzzy ontology-based approach cannot extract intensively blurred data from the hazy marine environment to offer actual solutions. Therefore, we propose a type-2 fuzzy ontology to provide accurate information about collision risk and the marine environment during real-time marine operations. Moreover, the type-2 fuzzy ontology is designed using Protege OWL-2 tools. The DL query and SPARQL query are used to evaluate the ontology. The distance to closest point of approach (DCPA), time to closest point of approach (TCPA) and variation of compass degree (VCD) are used to calculate the degree of collision risk between AUVs and obstacles. The experimental and simulation results show that the proposed architecture is highly efficient and highly productive for marine missions and the real-time decision-making system of AUVs.
Alif, Sheikh Mohammad,Haque, Sejuty,Nimmi, Naima,Ashraf, Ali,Khan, Saeed Hossain,Khan, Mahfujul Haq Korean Academy of Oral and Maxillofacial Radiology 2011 Imaging Science in Dentistry Vol.41 No.4
Purpose : This study was performed to determine the prevalence of maxillary canine impaction on a basis of a single panoramic radiograph in Bangladeshi population. Materials and Methods : A random sample of seven hundred panoramic radiographs was collected from the patient record of a dental clinic. All the selected panoramic radiographs were taken from January 2009 to August 2010 by a single panoramic radiograph machine with the same exposure time (19 seconds) for all radiographs. One hundred and twenty panoramic radiographs were excluded to minimize the selection bias. In a dim lit room, an observer assessed the radiographs on a standard radiographic light box. The position of the impacted maxillary canine was recorded in line with the longitudinal axis of a tooth using the edge of a metal ruler. Data were subsequently put on SPSS 11.5 software and chi-square (${\chi}^2$) tests were applied to find out the association. Results : Among 580 panoramic radiographs it was found that impacted maxillary canines were present in only 7 (1.2%) radiographs. A statistical significant difference was found between the age of the patients and the vertical position of the impacted canines (p=0.000) and between the age of the patients and the horizontal position of the impacted canines (p=0.003). Conclusion : The prevalence was found to be low compared with the present study from the limitation of panoramic image. Further study needs to include three-dimensional imaging modality.
Sarcasm Detection in Twitter - Performance Impact While Using Data Augmentation: Word Embeddings
Alif Tri Handoyo,Hidayaturrahman,Derwin Suhartono,Criscentia Jessica Setiadi 한국지능시스템학회 2022 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.22 No.4
Sarcasm is the use of words commonly used to ridicule someone or for humorous purposes. Several studies on sarcasm detection have utilized different learning algorithms. However,most of these learning models have always focused on the contents of expression only, thusleaving the contextual information in isolation. As a result, they failed to capture the contextualinformation in the sarcastic expression. Moreover, some datasets used in several studies havean unbalanced dataset, thus impacting the model result. In this paper, we propose a contextualmodel for sarcasm identification in Twitter using various pre-trained models and augmentingthe dataset by applying Global Vector representation (GloVe) for the construction of wordembedding and context learning to generate more sarcastic data, and also perform additionalexperiments by using the data duplication method. Data augmentation and duplication impactis tested in various datasets and augmentation sizes. In particular, we achieved the bestperformance after using the data augmentation method to increase 20% of the data labeledas sarcastic and improve the performance by 2.1% with an F1 Score of 40.44% compared to38.34% before using data augmentation in the iSarcasm dataset.