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Mechatronic Approaches to Synthesize Biomimetic Flapping-Wing Mechanisms: A Review
Nilanjan Chattaraj,Ranjan Ganguli 한국항공우주학회 2023 International Journal of Aeronautical and Space Sc Vol.24 No.1
Conventional electromechanical actuators cannot independently produce flapping-wing motion and typically require complimentary mechanical transmission mechanisms to achieve that motion. Hence, the selection and design of electromechanical actuators need to be considered in parallel with the selection and design of mechanical transmission mechanisms. The article presents a review on the mechatronics-based flapping-wing mechanisms applicable to micro air vehicles, which have been reported so far in the literature to the best of authors’ knowledge. The contribution of this review explicitly illustrates a design-map showing all the possible mechatronic methods to synthesize flapping-wing mechanisms, highlighting both attempted approaches in literature and unattempted approaches, which can be investigated in the upcoming time. The comparative discussion highlights both the capabilities and design-trade-offs of all the approaches to produce flapping-wing motion in their own way. The research gap recognized by the design-map presents the scope of future investigation in this domain.
Chinese Dragon venturing into GMS territory
Nilanjan Banik,Khanindra Ch. Das 인하대학교 정석물류통상연구원 2013 JOURNAL OF INTERNATIONAL LOGISTICS AND TRADE Vol.11 No.1
The notion that China is factory of the world is now changing. Factories in China are shifting their production base to neighboring Asia, primarily because of higher input costs in China, a volatile Chinese exchange rate, and protectionist measures targeted against Chinese exports. In this paper, we examine the location substitution effect for China: Chinese firms are exporting primary, intermediate and machinery items, meant for producing final output in the Greater Mekong Subregion (GMS). Results suggest that GMS countries are exporting finished items to China, that are increasingly getting manufactured using primary and intermediate inputs imported from China.
Reciprocal Dumping under Antidumping Enforcement
Banik, Nilanjan,Gilbert, John 정석물류통상연구원 2006 JOURNAL OF INTERNATIONAL LOGISTICS AND TRADE Vol.4 No.1
In a dynamic extension of the reciprocal dumping approach, oligopolistic firms producing imperfect substitutes use the carrot and stick strategy to enforce cooperative behavior. When dumping occurs, firms lobby for tariffs as punishment. After a finite punishment period, the non-dumping equilibrium is restored. Conditions are derived on the degrees of substitutability and observability that allow non-dumping under an infinite horizon. The model suggests the degree of substitutability between goods and the market interest rate, affect the likelihood of dumping.
Synthesis and characterization of a new polymeric surfactant for chemical enhanced oil recovery
Ajay Mandal,Keshak Babu,Nilanjan Pal,Vinod Kumar Saxena 한국화학공학회 2016 Korean Journal of Chemical Engineering Vol.33 No.2
Chemical enhanced oil recovery methods are field proven techniques that improve efficiency and effectiveness of oil recovery. We have synthesized polymeric surfactant from vegetable oil (castor oil) for application in chemical enhanced oil recovery. First, an eco-friendly surfactant, sodium methyl ester sulfonate (SMES) was synthesized from castor oil, and then the polymeric surfactant (PMES) was produced by graft co-polymerization reaction using different surfactant to acrylamide ratios. The synthesized PMES was characterized by FTIR, FE-SEM, EDX, TGA, DLS analysis. The performance of PMES as a chemical agent for enhanced oil recovery was studied by measuring the interfacial tension (IFT) between crude oil and PMES solution, rheological behavior and contact angle against sandstone surface. Addition of sodium chloride in PMES solution reduced the IFT to an ultra-low value (2.0×10−3mN/m). Core flooding experiments were conducted in sandpack system, and 26.5%, 27.8% and 29.1% additional recovery of original oil in place (OOIP) was obtained for 0.5, 0.6 and 0.7mass% of PMES solutions, respectively, after conventional water flooding.
Neuropharmacological assessment of Curcuma caesia rhizome in experimental animal models
Indrajit Karmakar,Pathik Saha,Nilanjan Sarkar,Sanjib Bhattacharya,Pallab K. Haldar 경희대학교 융합한의과학연구소 2011 Oriental Pharmacy and Experimental Medicine Vol.11 No.4
Curcuma caesia Roxb. (Zingiberaceae), called black turmeric in English, is a perennial herb found throughout the Himalayan region, North-East and Central India. The plant has been traditionally used in India for several medicinal purposes. The present study was carried out to evaluate the methanol extract of C. caesia rhizome (MECC)for some neuropharmacological activities in experimental animal models. MECC (at 50 and 100 mg/kg body weight)was evaluated for analgesic activity by acetic acid-induced writhing and tail flick tests. Locomotor activity was measured by means of an actophotometer. Anticonvulsant property was assessed against pentylenetetrazol-induced convulsion in mice and muscle relaxant effect was evaluated by using rota-rod apparatus. The results of the present study revealed remarkable analgesic, locomotor depressant, anticonvulsant and muscle relaxant effects of C. caesia rhizome,demonstrating depressant action on the central nervous system. The outcome of present study can validate certain traditional uses of C. caesia rhizome in India.
Neural-based prediction of structural failure of multistoried RC buildings
Sirshendu Hore,Sankhadeep Chatterjee,Sarbartha Sarkar,Nilanjan Dey,Amira S. Ashour,Dana Bălas-Timar,Valentina E. Balas 국제구조공학회 2016 Structural Engineering and Mechanics, An Int'l Jou Vol.58 No.3
Various vague and unstructured problems encountered the civil engineering/ designers that persuaded by their experiences. One of these problems is the structural failure of the reinforced concrete (RC) building determination. Typically, using the traditional Limit state method is time consuming and complex in designing structures that are optimized in terms of one/many parameters. Recent research has revealed the Artificial Neural Networks potentiality in solving various real life problems. Thus, the current work employed the Multilayer Perceptron Feed-Forward Network (MLP-FFN) classifier to tackle the problem of predicting structural failure of multistoried reinforced concrete buildings via detecting the failure possibility of the multistoried RC building structure in the future. In order to evaluate the proposed method performance, a database of 257 multistoried buildings RC structures has been constructed by professional engineers, from which 150 RC structures were used. From the structural design, fifteen features have been extracted, where nine features of them have been selected to perform the classification process. Various performance measures have been calculated to evaluate the proposed model. The experimental results established satisfactory performance of the proposed model.
Scavenging activity of Curcuma caesia rhizome against reactive oxygen and nitrogen species
Indrajit Karmakar,Narayan Dolai,Pathik Saha,Nilanjan Sarkar,Asis Bala,Pallab Kanti Haldar 경희대학교 융합한의과학연구소 2011 Oriental Pharmacy and Experimental Medicine Vol.11 No.4
Curcuma caesia Roxb. (Zingiberaceae), known as black turmeric in English, is a perennial herb found throughout the Himalayan region, North-East and Central India. The plant has been traditionally used in India for several medicinal purposes. Present study was carried out to evaluate the methanol extract of C. caesia (MECC) rhizome for some in vitro antioxidant studies as because we know that many diseases are associated with reactive oxygen species (ROS) and reactive nitrogen species (RNS). Effect of MECC on ROS and RNS were evaluated in different in vitro methods like 1, 1-diphenyl-2-picrylhydrazil radical, hydroxyl radicals, superoxide anions, nitric oxide, hydrogen peroxide,peroxynitrite and hypochlorous acid. Lipid peroxidation,total phenolic content was also measured by standard assay method. The extract showed significant antioxidant activities in a dose dependent manner. The IC_(50) values for scavenging of free radicals were 94.03±0.67 μg/ml, 155.59±3.03 μg/ml,68.10±1.24 μg/ml, 21.07±1.78 μg/ml, 260.56±12.65 μg/ml and 33.33±0.52 μg/ml for DPPH, nitric oxide, superoxide,hydroxyl, peroxynitrite and hypochlorous acid respectively. Reductive ability of the extract was also tested where dose dependent reducing capability was observed. The rhizome extract contains 677.7 μg of phenolic compound in 10 mg of the extract which is accounted for its free radical as well as antioxidant activity. From the above study it is concluded that the methanol extract of C. caesia rhizome is a potential source of natural antioxidant.
Chatterjee, Sankhadeep,Sarkar, Sarbartha,Hore, Sirshendu,Dey, Nilanjan,Ashour, Amira S.,Shi, Fuqian,Le, Dac-Nhuong Techno-Press 2017 Structural Engineering and Mechanics, An Int'l Jou Vol.63 No.4
Structural design has an imperative role in deciding the failure possibility of a Reinforced Concrete (RC) structure. Recent research works achieved the goal of predicting the structural failure of the RC structure with the assistance of machine learning techniques. Previously, the Artificial Neural Network (ANN) has been trained supported by Particle Swarm Optimization (PSO) to classify RC structures with reasonable accuracy. Though, keeping in mind the sensitivity in predicting the structural failure, more accurate models are still absent in the context of Machine Learning. Since the efficiency of multi-objective optimization over single objective optimization techniques is well established. Thus, the motivation of the current work is to employ a Multi-objective Genetic Algorithm (MOGA) to train the Neural Network (NN) based model. In the present work, the NN has been trained with MOGA to minimize the Root Mean Squared Error (RMSE) and Maximum Error (ME) toward optimizing the weight vector of the NN. The model has been tested by using a dataset consisting of 150 RC structure buildings. The proposed NN-MOGA based model has been compared with Multi-layer perceptron-feed-forward network (MLP-FFN) and NN-PSO based models in terms of several performance metrics. Experimental results suggested that the NN-MOGA has outperformed other existing well known classifiers with a reasonable improvement over them. Meanwhile, the proposed NN-MOGA achieved the superior accuracy of 93.33% and F-measure of 94.44%, which is superior to the other classifiers in the present study.