Efficiency of higher education in the presence of shared inputs using data envelopment analysis

This study measures the efficiency of higher education institutions with respect to teaching activity, research activity, and overall activities and also provides a direction for low-performing institutions to improve their performance. In many instances, the efficiency of the institution is a sum up of the efficiency of its activities. However, when the activities consume some resources in common, it requires the allocation of shared resources among the activities. Since sums up the efficiency of each activity does not give the institution’s overall efficiency, we use a joint data envelopment analysis that takes into account the institution’s internal operations to measure the teaching efficiency, research efficiency, and overall efficiency of the institutions. The empirical results indicate that the institutions that execute both activities simultaneously become more productive rather than adopt a single activity. Hence, this study suggests a useful and measurable action to improve the institution’s performance

Texture representation through overlapped multi-oriented tri-scale local binary pattern

This paper ideates a novel texture descriptor that retains its classification accuracy under varying conditions of image orientation, scale, and illumination. The proposed Overlapped Multi-oriented Tri-scale Local Binary Pattern (OMTLBP) texture descriptor also remains insensitive to additive white Gaussian noise. The wavelet decomposition stage of the OMTLBP provides robustness to photometric variations, while the two subsequent stages – overlapped multi-oriented fusion and multi-scale fusion – provide resilience against geometric transformations within an image. Isolated encoding of constituent pixels along each scale in the joint histogram enables the proposed descriptor to capture both micro and macro structures within the texture. Performance of the OMTLBP is evaluated by classifying a variety of textured images belonging to Outex, KTH-TIPS, Brodatz, CUReT, and UIUC datasets. The experimental results validate the superiority of the proposed method in terms of classification accuracy when compared with the state-of-the-art texture descriptors for noisy images

Robustness-Driven Hybrid Descriptor for Noise-Deterrent Texture Classification

A robustness-driven hybrid descriptor (RDHD) for noise-deterrent texture classification is presented in this paper. This paper offers the ability to categorize a variety of textures under challenging image acquisition conditions. An image is initially resolved into its low-frequency components by applying wavelet decomposition. The resulting low-frequency components are further processed for feature extraction using completed joint-scale local binary patterns (CJLBP). Moreover, a second feature set is obtained by computing the low order derivatives of the original sample. The evaluated feature sets are integrated to get a final feature vector representation. The texture-discriminating performance of the hybrid descriptor is analyzed using renowned datasets: Outex original, Outex extended, and KTH-TIPS. The experimental results demonstrate a stable and robust performance of the descriptor under a variety of noisy conditions. An accuracy of 95.86%, 32.52%, and 88.74% at noise variance of 0.025 is achieved for the given datasets, respectively. A comparison between performance parameters of the proposed paper with its parent descriptors and recently published paper is also presented

Robustness-driven hybrid descriptor for noise-deterrent texture classification

A robustness-driven hybrid descriptor for noise-deterrent texture classification is presented in this paper. This work offers the ability to categorize a variety of textures under challenging image acquisition conditions. An image is initially resolved into its low-frequency components by applying wavelet decomposition. The resulting low-frequency components are further processed for feature extraction using completed joint-scale local binary patterns (CJLBP). Moreover, a second feature set is obtained by computing the low order derivatives of the original sample. The evaluated feature sets are integrated to obtain a final feature vector representation. The texture-discriminating performance of the hybrid descriptor is analysed using renowned datasets: Outex original, Outex extended and KTH-TIPS. Experimental results demonstrate a stable and robust performance of the descriptor under a variety of noisy conditions. An accuracy of 95.86%, 32.52% and 88.74% at noise variance of 0.025 is achieved for the given datasets, respectively. A comparison between performance parameters of the proposed work with its parent descriptors and recently published work is also demonstrated

Stability Analysis and Solutions of Dynamical Models for Dengue

This paper focuses on the analytical and numerical solutions of dynamic models for dengue fever. The considered model consists of a system of coupled non-linear differential equations. Homotopy Perturbation Method (HPM) and Runge Kutta Method of Order 4 (RK4) are applied to obtain analytical and numerical solutions, respectively. Moreover, the positivity of the solution is proved. The results of the suggested methods are validated with the exact solution for simplified model equations. Furthermore, the proposed methods are applied to the considered model using the real time data of Lahore (years 2011-2014), Pakistan. The results are found to be in agreement with the available exact solution and real time data. HPM results approach the exact solution by including higher order polynomials. The parametric study of dengue cases are performed. Moreover, the synchronization between the simulated results and real time data of dengue cases verifies the correctness of model formulation. On the basis of these results, it is concluded that RK4 and HPM are suitable techniques to solve a non-linear dynamical system for dengue

Analysis of Homotopy Perturbation Method for Solving Fractional Order Differential Equations

The analysis of Homotopy Perturbation Method (HPM) for the solution of fractional partial differential equations (FPDEs) is presented. A unified convergence theorem is given. In order to validate the theory, the solution of fractional-order Burger-Poisson (FBP) equation is obtained. Furthermore, this work presents the method to find the solution of FPDEs, while the same partial differential equation (PDE) with ordinary derivative i.e., for α = 1 , is not defined in the given domain. Moreover, HPM is applied to a complicated obstacle boundary value problem (BVP) of fractional order

BCR-ABL1 in leukemia: Disguise master outplays riding shotgun

Leukemia is a many-sided molecular disorder that arises because of over expression of oncogenes, suppression of tumor suppressor genes, and chromosomal translocations. These chromosomal rearrangements are nonetheless among the many determinants that underlie transformation of cells from normal to a cancerous phenotype and predispose cells to refractoriness against interventions by reduced drug influx and substantial drug efflux. This review unfolds current understanding of BCR-ABL1 (break point cluster region-c-abl oncogene 1, non-receptor tyrosine kinase) signaling with a focus on apoptotic suppressive mechanisms and alternative approaches to chronic myeloid leukemia therapy

Unsteady Magnetohydrodynamic Convective Fluid Flow of Oldroyd-B Model Considering Ramped Wall Temperature and Ramped Wall Velocity

This paper examines unsteady magnetohydrodynamic (MHD) convective fluid flow described by the Oldroyd-B model using ramped wall temperature and velocity simultaneously. The fluid flow is closed to an infinite vertical flat plate immersed through a porous medium. Laplace transformation is used to find solutions of momentum and energy equations. Afterwards, the Nusselt number and skin friction coefficient are obtained. A parametric study is performed to investigate the effects of ramped velocity and temperature (at wall) on the considered fluid flow model