4,574 research outputs found

    Gender Equity and Empowerment in Nigeria: Implications for Educational Management

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    Despite the tremendous efforts by the Nigeria government over the past threedecades to increase educational opportunities for males and females, genderchallenges still lie ahead if the goal of Education for All is to be achieved.The paper, therefore, set to discuss the significant persistent gender gap inthe educational system, even at the management level and how genderequality will empower girls/women and develop the family and educationalsystem at large. It will also allow for female participation in allocation ofadministrative posts in the educational and other sectors of the economy. Theeducation of the female will bring about a micro and macro benefits ofdevelopment. The paper also presents an overview of the social-cultural andeconomic factors that affect schooling. To help bridge the gap of genderdisparity, some strategies, programmes and projects are being recommendedto promote female participation in education and management positions inNigeria in order to move the country forward from underdevelopment todeveloped nation

    Relativistic symmetry breaking in light kaonic nuclei

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    As the experimental data from kaonic atoms and K−NK^{-}N scatterings imply that the K−K^{-}-nucleon interaction is strongly attractive at saturation density, there is a possibility to form K−K^{-}-nuclear bound states or kaonic nuclei. In this work, we investigate the ground-state properties of the light kaonic nuclei with the relativistic mean field theory. It is found that the strong attraction between K−K^{-} and nucleons reshapes the scalar and vector meson fields, leading to the remarkable enhancement of the nuclear density in the interior of light kaonic nuclei and the manifest shift of the single-nucleon energy spectra and magic numbers therein. As a consequence, the pseudospin symmetry is shown to be violated together with enlarged spin-orbit splittings in these kaonic nuclei.Comment: 15 pages, 7 figure

    DescFold: A web server for protein fold recognition

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    <p>Abstract</p> <p>Background</p> <p>Machine learning-based methods have been proven to be powerful in developing new fold recognition tools. In our previous work [Zhang, Kochhar and Grigorov (2005) <it>Protein Science</it>, <b>14</b>: 431-444], a machine learning-based method called DescFold was established by using Support Vector Machines (SVMs) to combine the following four descriptors: a profile-sequence-alignment-based descriptor using Psi-blast <it>e</it>-values and bit scores, a sequence-profile-alignment-based descriptor using Rps-blast <it>e</it>-values and bit scores, a descriptor based on secondary structure element alignment (SSEA), and a descriptor based on the occurrence of PROSITE functional motifs. In this work, we focus on the improvement of DescFold by incorporating more powerful descriptors and setting up a user-friendly web server.</p> <p>Results</p> <p>In seeking more powerful descriptors, the profile-profile alignment score generated from the COMPASS algorithm was first considered as a new descriptor (i.e., PPA). When considering a profile-profile alignment between two proteins in the context of fold recognition, one protein is regarded as a template (i.e., its 3D structure is known). Instead of a sequence profile derived from a Psi-blast search, a structure-seeded profile for the template protein was generated by searching its structural neighbors with the assistance of the TM-align structural alignment algorithm. Moreover, the COMPASS algorithm was used again to derive a profile-structural-profile-alignment-based descriptor (i.e., PSPA). We trained and tested the new DescFold in a total of 1,835 highly diverse proteins extracted from the SCOP 1.73 version. When the PPA and PSPA descriptors were introduced, the new DescFold boosts the performance of fold recognition substantially. Using the SCOP_1.73_40% dataset as the fold library, the DescFold web server based on the trained SVM models was further constructed. To provide a large-scale test for the new DescFold, a stringent test set of 1,866 proteins were selected from the SCOP 1.75 version. At a less than 5% false positive rate control, the new DescFold is able to correctly recognize structural homologs at the fold level for nearly 46% test proteins. Additionally, we also benchmarked the DescFold method against several well-established fold recognition algorithms through the LiveBench targets and Lindahl dataset.</p> <p>Conclusions</p> <p>The new DescFold method was intensively benchmarked to have very competitive performance compared with some well-established fold recognition methods, suggesting that it can serve as a useful tool to assist in template-based protein structure prediction. The DescFold server is freely accessible at <url>http://202.112.170.199/DescFold/index.html</url>.</p

    A multi-phase flow model for electrospinning process

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    An electrospinning process is a multi-phase and multi-physicical process with flow, electric and magnetic fields coupled together. This paper deals with establishing a multi-phase model for numerical study and explains how to prepare for nanofibers and nanoporous materials. The model provides with a powerful tool to controlling over electrospinning parameters such as voltage, flow rate, and others
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