Silicon Mitigates Drought Stress in Wheat (Triticum aestivum L.) Through Improving Photosynthetic Pigments,Biochemical and Yield Characters

Silicon - Feeling prone to stress differs with plant production stage, water scarcity near commencement of grain filling phase has a significant reduced grain yield through fewer endosperm and sink...     

Vertex-Edge Degree Based Indices of Honey Comb Derived Network

Chemical graph theory is a branch of mathematics which combines graph theory and chemistry. Chemical reaction network theory is a territory of applied mathematics that endeavors to display the conduct of genuine compound frameworks. It pulled the research community due to its applications in theoretical and organic chemistry since 1960. Additionally, it also increases the interest the mathematicians due to the interesting mathematical structures and problems are involved. The structure of an interconnection network can be represented by a graph. In the network, vertices represent the processor nodes and edges represent the links between the processor nodes. Graph invariants play a vital feature in graph theory and distinguish the structural properties of graphs and networks. In this paper, we determined the newly introduced topological indices namely, first -degree Zagreb index, first -degree Zagreb index, second -degree Zagreb index, -degree Randic index, -degree atom-bond connectivity index, -degree geometric-arithmetic index, -degree harmonic index and -degree sum-connectivity index for honey comb derived network. In the analysis of the quantitative structure property relationships (QSPRs) and the quantitative structureactivity relationships (QSARs), graph invariants are important tools to approximate and predicate the properties of the biological and chemical compounds. Also, we give the numerical and graphical representation of our outcomes.     

Comparison of Bougainvillea spectabilis and Bougainvillea glabra species inhibited in Pakistan based on microscopic studies: Light microscope and scanning electron microscope

The anatomical variations of two plants from the Nyctaginaceae family, Bougainvillea spectabilis and Bougainvillea glabra, were studied using light and scanning electron microscopy methods in this work. Bougainvillea is a dicotyledonous with defensive traits that can withstand extreme (hot and dry) settings; according to the findings, crystal inclusions in cells, woody spines, and an abnormal development pattern are all features that help them survive against predators and are unique to this species. The Bougainvillea plant's leaves are arranged in simple pattern, alternate to each other along stem having an undulate leaves edge and an oval form. The xylem and phloem, palisade, parenchyma midrib, spongy mesophyll, raphide crystal bundles, and trichomes were all visible when bracts and leaves were transversally sectioned and dyed with toluidine blue O (TBO). The presence of crystals was confirmed by a detailed examination of the transverse leaves by using bright-field and cross-polarizing microscopy. Dissecting microscopic examination showed that all the leaves revealed leaves venation pattern that had midvein, lateral veins areoles, and trichomes. Although trichomes have been identified on both sides, a closer look at a cleaned leaf dyed with TBO showed multicellular abundant trichomes on adaxial surface. Stomata complexes were typically found on the abaxial surface of the leaf according to epidermal peels. Present studies also showed that on adaxial side, stomata were lesser in number or were absent and also showed that the morphologies of the pavement cells on the adaxial and abaxial sides of the leaf differed.     

Synthesis of highly pure and dense 0.9(KNbO3)-0.1(BaNi1/2Nb1/2O3-δ) ceramic with superior magnetic properties

Residual NiO phase is generally detected in 0.9(KNbO₃)–0.1(BaNi_1/2Nb_1/2O_3-δ) (KBNNO) synthesized using NiO as a nickel precursor by solid-state reaction. In this work, NiO phase is found to exist in the form of the residual NiO particles with a size of 100-200 nm using energy dispersive X-ray elemental mapping. These NiO residual particles are eliminated by using nickel acetate as a nickel precursor and a 100% perovskite phase KBNNO is successfully synthesized at as low as 600°C temperature. Furthermore, using the two-step sintering technique, 100% relative density is achieved in this material. The nickel acetate–based KBNNO shows a robust ferromagnetism with the saturation magnetization of 11.42 memu/g and the remanent magnetization of 3.89 memu/g which is 38 times higher than that of previously reported value in NiO-based KBNNO. Thus, a highly pure and fully dense KBNNO ceramic with superior magnetic properties is obtained using nickel acetate and by the two-step sintering method. This is a key step forward in the processing of KBNNO and is likely to have a significant impact on other physical properties of this newly invented and promising photovoltaic perovskite material.     

Non-blind Arnold scrambled hybrid image watermarking in YCbCr color space

Microsystem Technologies - Due to fast technological development, human beings generally depend upon computer and other digital equipments in different areas of concern/applications. Therefore,...     

A new approach for simplification and control of linear time invariant systems

Microsystem Technologies - This study presents a new approach for system simplification and control. This approach is based on the behaviour of growth and reproduction of weed plants namely...     

ACTNET: End-to-End Learning of Feature Activations and Multi-stream Aggregation for Effective Instance Image Retrieval

International Journal of Computer Vision - We propose a novel CNN architecture called ACTNET for robust instance image retrieval from large-scale datasets. Our key innovation is a learnable...     

Investigation of structural,optical, electrical,and magnetic properties of Fe-doped La0.7Sr0.3MnO3 manganites

In this work, the physical properties of nanocrystalline samples of La_0.7Sr_0.3Mn_1−xFe_xO₃ (0.0 ≤ x ≤ 0.20) perovskite manganites synthesized by the reverse micelle (RM) technique were explored in detail. The phase purity, crystal structure, and crystallite size of the samples were determined using X-ray diffraction (XRD) and Fourier transform infrared (FTIR) spectroscopy. All the samples had rhombohedral crystal structure and crystallite size increased with increase in Fe content in La_0.7Sr_0.3MnO₃. The scanning electron micrographs (SEMs) exhibited smooth surface morphology and nonuniform shape of the particles. The optical properties studied using UV-visible absorption spectroscopy revealed a decrease in the absorbance and optical band gap with an increase in Fe content in La_0.7Sr_0.3MnO₃ compound. The temperature-dependent resistivity measurements revealed semiconducting nature of x = 0 and 0.1 samples up to the studied temperature range, while a metal-to-insulator transition was observed at higher Fe doping. Magnetic studies revealed weak ferromagnetism in all the samples and a reduction in the maximum magnetization with an increase in Fe content. A close correlation between electrical transport and magnetic properties was observed with the doping of Fe ion in La_0.7Sr_0.3MnO₃ at Mn site. These results advocate strong interactions associated with the double exchange mechanism among Fe³⁺ and Mn³⁺ ions.     

A hybrid knowledge and ensemble classification approach for prediction of venous thromboembolism

Clinical narratives such as progress summaries, lab reports, surgical reports, and other narrative texts contain key biomarkers about a patient's health. Evidence-based preventive medicine needs accurate semantic and sentiment analysis to extract and classify medical features as the input to appropriate machine learning classifiers. However, the traditional approach of using single classifiers is limited by the need for dimensionality reduction techniques, statistical feature correlation, a faster learning rate, and the lack of consideration of the semantic relations among features. Hence, extracting semantic and sentiment-based features from clinical text and combining multiple classifiers to create an ensemble intelligent system overcomes many limitations and provides a more robust prediction outcome. The selection of an appropriate approach and its interparameter dependency becomes key for the success of the ensemble method. This paper proposes a hybrid knowledge and ensemble learning framework for prediction of venous thromboembolism (VTE) diagnosis consisting of the following components: a VTE ontology, semantic extraction and sentiment assessment of risk factor framework, and an ensemble classifier. Therefore, a component-based analysis approach was adopted for evaluation using a data set of 250 clinical narratives where knowledge and ensemble achieved the following results with and without semantic extraction and sentiment assessment of risk factor, respectively: a precision of 81.8% and 62.9%, a recall of 81.8% and 57.6%, an F measure of 81.8% and 53.8%, and a receiving operating characteristic of 80.1% and 58.5% in identifying cases of VTE.     

Data broadcasting strategies for cognitive radio based AMI networks

Wireless Networks - Communication systems play an important role in smart grid (SG). Advanced Metering Infrastructure (AMI) is hybrid architecture in smart grid comprising of smart meters and...