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Role of Lipoxygenase During Flower Bud Opening in Roses (Rosa hybrida L.)
Naveen Kumar,Girish Chand Srivastava,Kiran Dixit 한국원예학회 2008 Horticulture, Environment, and Biotechnology Vol.49 No.3
The adverse effect of lipid peroxidation during flower bud opening in roses was investigated. Experiments were conducted from 2005 to 2007 on two cultivars of cut-roses (Rosa hybrida L.), ‘Grand Gala’ and ‘First Red’ obtained from a commercial grower. Flower stems were harvested at different developmental stages. Petals were separated from seven different petal whorls in flowers (outermost to innermost) of ‘Grand Gala’ and ‘First Red’ at all developmental stages. During the first three stages of flower bud development (S1 - S3) petal membrane remained stable for a considerable period of time. However, at subsequent stages (S4 - S6) membrane leakage increased considerably in both cultivars showing membrane stability index of 48 percent and 38 percent in flower petals of ‘First Red’ and ‘Grand Gala’ respectively. TBARS (Thiobarbiturate reactive substances) content was very low during the first two stages of flower bud development; thereafter, a steep rise was noted in different petal whorls of both cultivars. Lipoxygenase activity showed a progressive rise from stage 1 to stage 6 of flower bud development. Differential LOX (Lipoxygenase) activity was noticed during flower bud opening, a progressive rise during the first three phases, but at a slower pace and a two-fold rise at later stages of development.
Naveen Kumar,Ashwini Aithal Padur,Gayathri Prabhu,Swamy Ravindra Shanthakumar,Ravi Bhaskar 대한해부학회 2019 Anatomy & Cell Biology Vol.52 No.1
Entrapment neuropathies of the peripheral nervous system are frequently encountered due to anatomical variations. Median nerve is the most vulnerable nerve to undergo entrapment neuropathies. The clinical complications are mostly manifested by median nerve impingement in forearm and wrist areas. Median nerve entrapment could also occur at the arm, due to the presence of ligament of Struthers. Here we report a rare case of proximal entrapment of median nerve and brachial artery in the arm by an abnormally formed musculo-fascial tunnel. The tunnel was formed by the muscle fibers of brachialis and medial intermuscular septum in the lower part of arm. Due to this, the median nerve coursed deep, below the tunnel and continued distally into the forearm, underneath the pronator teres muscle and hence did not appear as a content of cubital fossa. The present entrapment of neurovascular structures in the tunnel might lead to pronator syndromes or other neurovascular compression syndromes.
Naveen Kumar,A.K. Sharma,S.K. Maiti,A.K. Gangwar,N. Kumar 한국탄소학회 2007 Carbon Letters Vol.8 No.4
During a 4-year period (2001-2005) 09 animals were surgically treated because of abdominal wall defects (hernia). Out of 9 animals 8 were bovines and one caprine. In each case the defect was repaired with carbon fibre mesh. All the cases were successfully treated and no complication was observed up to six months postoperatively.
Naveen Kumar,김준동 한국진공학회 2021 한국진공학회 학술발표회초록집 Vol.2021 No.2
A unique RF superimposed DC sputtering technique was used to tailor the plasma during the sputtering process and to control the kinetics for the growth of a-IZO. The growth dynamics were controlled to achieve high optoelectronic properties, low surface roughness and low residual stress for flexible devices using statistical design of experiment approach. A common growth space was found to achieve a smooth surface in a stress-free and high optoelectronic merit a-IZO thin film. The grown a-IZO thin film was also used as a transparent electrode in a flexible Ga<sub>2</<sub>O<sub>3</sub> solar-blind photodetector.
Neural Network Based Hybrid Force/Position Control for Robot Manipulators
Naveen Kumar,Vikas Panwar,Nagarajan Sukavanam,Shri Prakash Sharma,범진환 한국정밀공학회 2011 International Journal of Precision Engineering and Vol. No.
This paper presents a neural network based adaptive control scheme for hybrid force/position control for rigid robot manipulators. Firstly the robot dynamics is decomposed into force, position and redundant joint subspaces. Based on this decomposition, a neural network based controller is proposed that achieves the stability in the sense of Lyapunov for desired interaction force between the end-effector and the environment as well as regulate robot tip position in cartesian space. A feedforward neural network is employed to learn the parametric uncertainties, existing in the dynamical model of the robot manipulator. Finally numerical simulation studies are carried out for a two link rigid robot manipulator.
Naveen Kumar,범진환,Vikas Panwar,채장범 한국정밀공학회 2012 International Journal of Precision Engineering and Vol. No.
In this paper, a hybrid trajectory tracking controller is designed for redundant robot manipulators, consisting of RBF neural network and an adaptive bound on disturbances. The controller is composed of computed torque type part, RBF neural network and an adaptive controller. The controller achieves end-effector trajectory tracking as well as subtask tracking effectively. The controller is able to learn the existing structured and unstructured uncertainties in the system in online manner. The RBF network learns the unknown part of the robot dynamics with no requirement of the offline training. The adaptive controller is used to estimate the unknown bounds on unstructured uncertainties and neural network reconstruction error. The overall system is proved to be asymptotically stable in the sense of Lyapunov. Finally, numerical simulation studies are performed on a 3R planar robot manipulator to show the effectiveness of the control scheme.
Intelligent Control of Space Robot System using RBF Neural Network
Naveen Kumar,Vikas Panwar 제어로봇시스템학회 2015 제어로봇시스템학회 국제학술대회 논문집 Vol.2015 No.10
In this paper, an intelligent controller is proposed for a space robot system with an attitude controlled base without joint acceleration measurements. The controller consists of computed torque type part, RBF neural network and an adaptive controller. The controller achieves the required tracking effectively. The controller is able to learn the existing structured and unstructured uncertainties in the system in online manner. The RBF network learns the unknown part of the space robot system dynamics with no requirement of the offline training. The adaptive controller is used to estimate the unknown bounds on unstructured uncertainties and neural network reconstruction error. The overall system is proved to be asymptotically stable in the sense of Lyapunov. Finally numerical simulation studies are performed to evaluate the controller performance.