Application of SNPs to improve yield of Pisum sativum L. (pea)

Nanotechnology opens an enormous scope of novel application in the fields of biotechnology and agricultural industries, because nanoparticles (NPs) have unique physicochemical properties, i.e. high surface area, high reactivity, tunable pore size and particle morphology. Present study was carried out to determine the role of silver NPs (SNPs) to improve yield of Pisum sativum L. SNPs (10–100 nm) were synthesised by green method using extract of Berberis lycium Royle. Pea seeds were soaked and seedling were foliage sprayed by 0, 30, 60 and 90 ppm SNPs. The experiment was arranged as split–split plot randomised complete block design with three replicates. The application of SNPs enhanced significantly number of seeds pod⁻¹, number of pods plant⁻¹, hundred seed weight, biological yield and green pod yield over control. The highest yield was found when 60 ppm SNPs were applied. However, exposure to 90 ppm SNPs, the yield of the pea decreased significantly as compared with 30 and 60 ppm. This research shows that SNPs have definite ability to improve growth and yield of crops. Nevertheless, a comprehensive experimentation is needed to establish the most appropriate concentration, size and mode of application of SNPs for higher growth and maximum yield of pea.Inspec keywords: nanobiotechnology, silver, nanoparticles, cropsOther keywords: Ag, foliage spraying, crop yield, crop growth, green pod yield, biological yield, seed weight, pod plant number, seed pod number, pea seeds, Berberis lycium Royle extract, green method, nanotechnology, Pisum sativum L, silver nanoparticles     

Investigation of Magnetic,Magnetocaloric, and Critical Properties of La<Subscript>0.5</Subscript>Ba<Subscript>0.5</Subscript>MnO<Subscript>3</Subscript> Manganite

We present an extensive study of the magnetic properties of a novel La_0.5Ba_0.5MnO₃ perovskite material prepared by the hydrothermal method. The explored sample was structurally studied by the x-ray diffraction (XRD) method which confirms the formation of a pure cubic phase of a perovskite structure with Pm3m space group. The magnetic properties were probed by employing temperature M (T) and external magnetic field M (μ_oH) dependence of magnetization measurements. A magnetic phase transition from ferromagnetic to paramagnetic phase occurs at 339 K in this sample. The maximum magnetic entropy change (\(\left | {{\Delta } S}_{M}^{\max } \right |\)) took a value of 1.4 J kg^??1 K^??1 at the applied magnetic field of 4.0 T for the explored sample and has also been found to occur at Curie temperature (T_C). This large entropy change might be instigated from the abrupt reduction of magnetization at T_C. The magnetocaloric effect (MCE) is maximum at T_C as represented by M (μ_oH) isotherms. The relative cooling power (RCP) is 243.2 J kg^??1 at μ_oH =?4.0 T. Moreover, the critical properties near T_C have been probed from magnetic data. The critical exponents δ, β, and γ with values 3.82, 0.42, and 1.2 are close to the values predicted by the 3D Ising model. Additionally, the authenticity of the critical exponents has been confirmed by the scaling equation of state and all data fall on two separate branches, one for T < T_C and the other for T > T_C, signifying that the critical exponents obtained in this work are accurate.     

Electronic structure,elastic and magnetic properties of the binary intermetallics RFe2 (R = Eu,Gd and Tb)

Journal of Computational Electronics - Laves-phase (C15) binary intermetallics RFe2 (R?=?Eu, Gd and Tb) are studied using various potentials in the domain of density functional theory...     

Smart COVID-3D-SCNN: A Novel Method to Classify X-ray Images of COVID-19

The outbreak of the novel coronavirus has spread worldwide, and millions of people are being infected. Image or detection classification is one of the first application areas of deep learning, which has a significant contribution to medical image analysis. In classification detection, one or more images (detection) are usually used as input, and diagnostic variables (such as whether there is a disease) are used as output. The novel coronavirus has spread across the world, infecting millions of people. Early-stage detection of critical cases of COVID-19 is essential. X-ray scans are used in clinical studies to diagnose COVID-19 and Pneumonia early. For extracting the discriminative features through these modalities, deep convolutional neural networks (CNNs) are used. A siamese convolutional neural network model (COVID-3D-SCNN) is proposed in this study for the automated detection of COVID-19 by utilizing X-ray scans. To extract the useful features, we used three consecutive models working in parallel in the proposed approach. We acquired 575 COVID-19, 1200 non-COVID, and 1400 pneumonia images, which are publicly available. In our framework, augmentation is used to enlarge the dataset. The findings suggest that the proposed method outperforms the results of comparative studies in terms of accuracy 96.70%, specificity 95.55%, and sensitivity 96.62% over (COVID-19 vs. non-COVID19 vs. Pneumonia).     

New Integral Antiwindup Scheme for PI Motor Speed Control

Windup refers to the phenomenon where a control system operates in a nonlinear region when the controller's output exceeds the input limits of the plant being controlled. Windup can lead to performance degradation in terms of overshoot, settling time and even system stability. Many anti‐windup strategies involve switching and manipulating the integral control component in various ways when saturation occurs aiming to bring control back into the linear region. For better insight into windup, the proportional–integral (PI) plane is now used as a means to explain the phenomenon in terms of the controller's signals. A PI controller with a built‐in closed‐loop integral controller that has a reference set based on the input command and external torque is proposed. The performance for this proposed method is compared against existing conditional integration, tracking back calculation and integral state prediction schemes on second and third order systems using MATLAB/SIMULINK simulations of an induction motor and a DC motor respectively. The proposed controller showed promising potential with its ability to eliminate overshoot in both no load and full load conditions due to the decoupling of its parameters from its response and has the shortest settling time when compared against existing schemes, even in the presence of noise.     

Robust Integral Sliding Mode Control Design for Stability Enhancement of Under-actuated Quadcopter

International Journal of Control, Automation and Systems - In this paper, a robust backstepping integral sliding mode control (RBISMC) technique is designed for the flight control of a quadcopter,...     

An efficient computerized decision support system for the analysis and 3D visualization of brain tumor

The quality of health services provided by medical centers varies widely, and there is often a large gap between the optimal standard of services when judged based on the locality of patients (rural or urban environments). This quality gap can have serious health consequences and major implications for patient’s timely and correct treatment. These deficiencies can manifest, for example, as a lack of quality services, misdiagnosis, medication errors, and unavailability of trained professionals. In medical imaging, MRI analysis assists radiologists and surgeons in developing patient treatment plans. Accurate segmentation of anomalous tissues and its correct 3D visualization plays an important role inappropriate treatment. In this context, we aim to develop an intelligent computer-aided diagnostic system focusing on human brain MRI analysis. We present brain tumor detection, segmentation, and its 3D visualization system, providing quality clinical services, regardless of geographical location, and level of expertise of medical specialists. In this research, brain magnetic resonance (MR) images are segmented using a semi-automatic and adaptive threshold selection method. After segmentation, the tumor is classified into malignant and benign based on a bag of words (BoW) driven robust support vector machine (SVM) classification model. The BoW feature extraction method is further amplified via speeded up robust features (SURF) incorporating its procedure of interest point selection. Finally, 3D visualization of the brain and tumor is achieved using volume marching cube algorithm which is used for rendering medical data. The effectiveness of the proposed system is verified over a dataset collected from 30 patients and achieved 99% accuracy. A subjective comparative analysis is also carried out between the proposed method and two state-of-the-art tools ITK-SNAP and 3D-Doctor. Experimental results indicate that the proposed system performed better than existing systems and assists radiologist determining the size, shape, and location of the tumor in the human brain.

     

LPV Modeling and Tracking Control of Dissimilar Redundant Actuation System for Civil Aircraft

International Journal of Control, Automation and Systems - Dissimilar redundant actuation systems (DRAS) are in practice in advanced aircraft in order to increase reliability and to resolve the...     

Human facial expression recognition using curvelet feature extraction and normalized mutual information feature selection

To recognize expressions accurately, facial expression systems require robust feature extraction and feature selection methods. In this paper, a normalized mutual information based feature selection technique is proposed for FER systems. The technique is derived from an existing method, that is, the max-relevance and min-redundancy (mRMR) method. We, however, propose to normalize the mutual information used in this method so that the domination of the relevance or of the redundancy can be eliminated. For feature extraction, curvelet transform is used. After the feature extraction and selection the feature space is reduced by employing linear discriminant analysis (LDA). Finally, hidden Markov model (HMM) is used to recognize the expressions. The proposed FER system (CNF-FER) is validated using four publicly available standard datasets. For each dataset, 10-fold cross validation scheme is utilized. CNF-FER outperformed the existing well-known statistical and state-of-the-art methods by achieving a weighted average recognition rate of 99 % across all the datasets.     

Adaptive fuzzy clustering by fast search and find of density peaks

Clustering by fast search and find of density peaks (CFSFDP) is proposed to cluster the data by finding of density peaks. CFSFDP is based on two assumptions that: a cluster center is a high dense data point as compared to its surrounding neighbors, and it lies at a large distance from other cluster centers. Based on these assumptions, CFSFDP supports a heuristic approach, known as decision graph to manually select cluster centers. Manual selection of cluster centers is a big limitation of CFSFDP in intelligent data analysis. In this paper, we proposed a fuzzy-CFSFDP method for adaptively selecting the cluster centers, effectively. It uses the fuzzy rules, based on aforementioned assumption for the selection of cluster centers. We performed a number of experiments on nine synthetic clustering datasets and compared the resulting clusters with the state-of-the-art methods. Clustering results and the comparisons of synthetic data validate the robustness and effectiveness of proposed fuzzy-CFSFDP method.