http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
AKTER SHAHINA,Md. Amdadul Huq,정유진,조용구,강권규 한국식물생명공학회 2016 JOURNAL OF PLANT BIOTECHNOLOGY Vol.43 No.4
Sweet and taste modifying proteins are natural alternatives to synthetic sweeteners and flavor enhancers, and have been used for centuries in different countries. Use of these proteins is limited due to less stability and availability. However, recent advances in biotechnology have enhanced their availability. These include production of sweet and taste modifying proteins in transgenic organisms, and protein engineering to improve their stability. Their increased availability in the food, beverage or medicinal industries as sweeteners and flavor enhancers will reduce the dependence on artificial alternatives. Production of transgenic plants using sweet and taste modifying genes, is an interesting alternative to the extraction of these products from natural source. In this review paper, we briefly describe various sweet and taste modifying proteins (such as thaumatin, monellin, brazzein, curculin and miraculin), their properties, and their application for plant development using biotechnological approaches.
Akter, Rashida,Jeong, Bongjin,Lee, Yong-Mi,Choi, Jong-Soon,Rahman, Md. Aminur Elsevier 2017 Biosensors & bioelectronics Vol.91 No.-
<P><B>Abstract</B></P> <P>A novel highly sensitive dendrimer coupled impedimetric immunosensor was developed for the label-free and reagent-free detection of cardiac troponin I (TnI) in serum samples. The immunosensor probe was fabricated by covalently attaching carboxylic acid-functionalized third generation (G3) poly (amidoamine) (PAMAM) dendrimer (Den) on the 3, 3′, 5, 5′-tetramethyl benzidine (TMB) modified 6-mercaptohexanoic acid (MHA) self-assembled monolayer (SAM) on a gold (Au) electrode. Monoclonal anti-TnI antibody was then covalently immobilized on the Den and TMB attached MHA SAM modified surface. TMB was used as an internal surface redox couple for generating signal which also allowed to avoid the use of an external one (<I>i.e.</I> ferricyanide couple) in solution during the impedance measurement for monitoring the antibody-antigen binding. On the other hand, Den was used as a signal enhancer by immobilizing more anti-body on the immunosensor probe. The immunosensor probe was characterized using X-ray photoelectron spectroscopy (XPS), quartz crystal microbalance (QCM), cyclic voltammetry (CV), and electrochemical impedance spectroscopy (EIS) techniques. The TnI detection in diluted serum was based on the measurement of charge transfer resistance (<I>R</I> <SUB>ct</SUB>) of the electron transfer process of the surface-attached TMB before and after immunobinding. Under the optimized condition, the proposed immunosensor could detect human TnI in diluted serum samples as low as 11.7 fM with a wide linear dynamic range, good stability, and excellent specificity. The validity of the proposed method was tested in various TnI spiked human undiluted serum samples and was compared with the enzyme-linked immunosorbent assay (ELISA). The results suggested that the proposed immunosensor could be a useful tool for practical applications in clinical diagnosis.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Impedance-based cardiac troponin I immunosensor was fabricated. </LI> <LI> Reagent-less and label-free detection was developed using tetramethylbenzidine. </LI> <LI> Enhanced detection was achieved using Dendrimer at the probe. </LI> <LI> Detection limit of troponin I in a diluted serum was as low as 11.7 fM. </LI> </UL> </P>
Reinforcement Learning-based Duty Cycle Interval Control in Wireless Sensor Networks
Akter, Shathee,Yoon, Seokhoon The Institute of Internet 2018 Journal of Advanced Smart Convergence Vol.7 No.4
One of the distinct features of Wireless Sensor Networks (WSNs) is duty cycling mechanism, which is used to conserve energy and extend the network lifetime. Large duty cycle interval introduces lower energy consumption, meanwhile longer end-to-end (E2E) delay. In this paper, we introduce an energy consumption minimization problem for duty-cycled WSNs. We have applied Q-learning algorithm to obtain the maximum duty cycle interval which supports various delay requirements and given Delay Success ratio (DSR) i.e. the required probability of packets arriving at the sink before given delay bound. Our approach only requires sink to compute Q-leaning which makes it practical to implement. Nodes in the different group have the different duty cycle interval in our proposed method and nodes don't need to know the information of the neighboring node. Performance metrics show that our proposed scheme outperforms existing algorithms in terms of energy efficiency while assuring the required delay bound and DSR.
A Probabilistic Tensor Factorization approach for Missing Data Inference in Mobile Crowd-Sensing
Akter, Shathee,Yoon, Seokhoon The Institute of Internet 2021 International Journal of Internet, Broadcasting an Vol.13 No.3
Mobile crowd-sensing (MCS) is a promising sensing paradigm that leverages mobile users with smart devices to perform large-scale sensing tasks in order to provide services to specific applications in various domains. However, MCS sensing tasks may not always be successfully completed or timely completed for various reasons, such as accidentally leaving the tasks incomplete by the users, asynchronous transmission, or connection errors. This results in missing sensing data at specific locations and times, which can degrade the performance of the applications and lead to serious casualties. Therefore, in this paper, we propose a missing data inference approach, called missing data approximation with probabilistic tensor factorization (MDI-PTF), to approximate the missing values as closely as possible to the actual values while taking asynchronous data transmission time and different sensing locations of the mobile users into account. The proposed method first normalizes the data to limit the range of the possible values. Next, a probabilistic model of tensor factorization is formulated, and finally, the data are approximated using the gradient descent method. The performance of the proposed algorithm is verified by conducting simulations under various situations using different datasets.
Akter Shahina,Ferdows M,Bég Tasveer A,Bég O Anwar,Kadir A,Sun Shuyu 한국CDE학회 2021 Journal of computational design and engineering Vol.8 No.4
A theoretical model is developed for steady magnetohydrodynamic viscous flow resulting from a moving semi-infinite flat plate in an electrically conducting nanofluid. Thermal radiation and magnetic induction effects are included in addition to thermal convective boundary conditions. Buongiorno’s two-component nanoscale model is deployed, which features Brownian motion and thermophoresis effects. The governing nonlinear boundary layer equations are converted to nonlinear ordinary differential equations by using suitable similarity transformations. The transformed system of differential equations is solved numerically, employing the spectral relaxation method (SRM) via the MATLAB R2018a software. SRM is a simple iteration scheme that does not require any evaluation of derivatives, perturbation, and linearization for solving a nonlinear system of equations. Effects of embedded parameters such as sheet velocity parameter$\lambda$, magnetic field parameter$\beta$, Prandtl number$Pr$, magnetic Prandtl number$Prm$, thermal radiation parameter$Rd$, Lewis number$Le$, Brownian motion parameter$Nb$, and thermophoresis parameter$Nt$ on velocity, induced magnetic field, temperature, and nanoparticle concentration profiles are investigated. The skin-friction results, local Nusselt number, and Sherwood number are also discussed for various values of governing physical parameters. To show the convergence rate against iteration, residual error analysis has also been performed. The flow is strongly decelerated, and magnetic induction is suppressed with greater magnetic body force parameter, whereas temperature is elevated due to extra work expended as heat in dragging the magnetic nanofluid. Temperatures are also boosted with increment in nanoscale thermophoresis parameter and radiative parameter, whereas they are reduced with higher wall velocity, Brownian motion, and Prandtl numbers. Both hydrodynamic and magnetic boundary layer thicknesses are reduced with greater reciprocal values of the magnetic Prandtl number Prm. Nanoparticle (concentration) boundary layer thickness is boosted with higher values of thermophoresis and Prandtl number, whereas it is diminished with increasing wall velocity, nanoscale Brownian motion parameter, radiative parameter, and Lewis number. The simulations are relevant to electroconductive nanomaterial processing.
Akter, Afroja,Yoo, Geonwook,Kim, Sangin,Baac, Hyoung Won,Heo, Junseok American Scientific Publishers 2017 Journal of Nanoscience and Nanotechnology Vol.17 No.5
<P>The electronic intraband absorption in InGaN nanodisks embedded in GaN nanowires with several kinds of cladding materials and without cladding was theoretically investigated. The cladding layer was 5 nm thick, and AlN, GaN, and Al0.4Ga0.6N were considered. The strain distribution, internal electric field, and intraband absorption in the nanodisks were calculated using the elastic energy minimization method and the single-band Schrodinger equation implemented in Nextnano3. For a plain nanowire without cladding, an inhomogeneous strain in the disk caused a piezoelectric field and deformation potential, yielding band-bending and a higher electron probability density in the periphery of the disk. An InGaN nanodisk embedded in a cladding GaN nanowire exhibited a higher intraband absorption. The case of the GaN cladding was optimal owing to the homogeneous surroundings of the disk.</P>
Grid Based Path Planning Using CNN & Artificial Potential Field Method
Akter, Shamina,Lee, Deok Jin,Lim, Shin Taek,Chong, Kil To Trans Tech Publications, Ltd. 2013 Applied mechanics and materials Vol.392 No.-
<P>This proposed path planning method combines cellular neural network (CNN) with artificial potential field approach. The fundamental operation based on CNN gray scale image processing and artificial potential is the additional approach for global path-planning. Every point of the environment has a potential value with respect to start and destination position. In the trajectory planning process, a minimum search of potential value of every surrounding neighbor points around a point is done and the neighbor point with the least minimum value is selected as the next location. This procedure is repeated until the goal point is reached. The advantage of using CNN based image processing with artificial potential field function in a vision system is its effectiveness in robot localization while the use of minimum potential value gives a simple yet efficient path planning method. Their feedback criterion is similar to a procedure in filtering the image and it frequently updates the information about obstacles and free path. The parallel processing properties of CNN makes the proposed method robust for real time application.</P>