Learning Patterns from COVID-19 Instances

Coronavirus disease, which resulted from the SARS-CoV-2 virus, has spread worldwide since early 2020 and has been declared a pandemic by the World Health Organization (WHO). Coronavirus disease is also termed COVID-19. It affects the human respiratory system and thus can be traced and tracked from the Chest X-Ray images. Therefore, Chest X-Ray alone may play a vital role in identifying COVID-19 cases. In this paper, we propose a Machine Learning (ML) approach that utilizes the X-Ray images to classify the healthy and affected patients based on the patterns found in these images. The article also explores traditional, and Deep Learning (DL) approaches for COVID-19 patterns from Chest X-Ray images to predict, analyze, and further understand this virus. The experimental evaluation of the proposed approach achieves 97.5% detection performance using the DL model for COVID-19 versus normal cases. In contrast, for COVID-19 versus Pneumonia Virus scenario, we achieve 94.5% accurate detections. Our extensive evaluation in the experimental section guides and helps in the selection of an appropriate model for similar tasks. Thus, the approach can be used for medical usages and is particularly pertinent in detecting COVID-19 positive patients using X-Ray images alone.     

The effect of ferrous ions,calcium ions and citric acid on absorption of ciprofloxacin across caco-2 cells: practical and structural approach

Objective: To study the potential influence of selected metal ions on absorption (and hence oral bioavailability of ciprofloxacin (Cipro) in presence and absence of a competing ligand.

Significance: The presence of metal ions together with Cipro results in complexes exhibiting a decreased bioavailability. Attempts were made to better understand the mechanism of decreased Cipro bioavailability in the presence of metals such as calcium and ferrous ions, and a small-sized ligand citric acid (CitA).

Methods: Effect of complex size or other potential factors was studied using diffusion through synthetic membrane, permeation studies across Caco-2 cells and capillary electrophoresis. A molecular dynamics (MD) simulation study was conducted to find the arrangement and the nature of the interactions between Cipro molecules and ferrous ions.

Results: Cipro was shown to form complexes with metals and CitA. The presence of CitA improved permeation of Cipro through the synthetic membrane but this was not as obvious in case of Caco-2 cells. Capillary electrophoresis suggested the existence of large molecular aggregates of Cipro: metal complexes. MD simulations offered clear evidence of large size aggregates in line with the experimental findings. CitA alone significantly improved permeation of Cipro through Caco-2 cells.

Conclusions: The size of the formed complexes, rather than the decrease in the solubility of formed complexes, plays a significant role in permeation (absorption) of Cipro. CitA might ameliorate the effect of co-administered metal ions on the bioavailability of Cipro.     

Automated localization of retinal features

Retinal fundus images are widely used in the diagnosis and treatment of various eye diseases, such as diabetic retinopathy and glaucoma. A computer-aided retinal fundus image analysis could provide an immediate detection and characterization of retinal features prior to specialist inspection. This paper proposes an approach to automatically localize the main features in fundus images, such as blood vessels, optic disc, and fovea by exploiting the spatial and geometric relations that govern their distribution within the fundus image. The blood vessels are segmented by scale-space analysis. The average thickness of these blood vessels is then computed using the vessels centerlines and orientations from a Hessian matrix. The optic disc is localized using the circular Hough transform, the parabolic Hough transform fitting, and the localization of the fovea. The proposed method can be extended to establish a foveal coordinate system to facilitate grading lesions based on the spatial relationships between lesions and landmark features. The proposed method was evaluated on publicly available image databases, and the results have demonstrated a significant improvement over the current state-of-the-art methods.     

Influence of Clay Lens on Migration of Light Nonaqueous Phase Liquid in Unsaturated Zone

This study concerns the control of movement of light nonaqueous phase liquid (LNAPL) in unsaturated zone in the presence of relatively low permeability clay lens. A two-dimensional, finite-difference numerical model for the simultaneous movement of LNAPL and water through the unsaturated zone of the soil has been developed. The system is a three fluid phase system (water, LNAPL, and air) but in the derivation of the model, air was treated as an immobile phase at constant atmospheric pressure. The flow equations for LNAPL and water were cast in terms of the wetting and non-wetting fluid pressure heads, respectively. The finite-difference equations were solved implicitly with explicitly scheme using Newton-Raphson iteration with Taylor series expansion to treat nonlinearity. A physical model to represent the infiltration of kerosene above the clay lens was constructed. The numerical results were compared with those observed experimentally. The results of all tests showed that the presence of clay lens controls the vertical movement of LNAPL in heterogeneous porous medium.     

Global Search Method for Locating General Slip Surface Using Monte Carlo Techniques

Searching for the critical slip surface and the lowest factor of safety in slope stability analysis can be achieved by means of optimization techniques. A new search procedure in generating kinematically admissible slip surfaces has been introduced in this paper. Such a procedure is based, mainly, on the Monte Carlo methods, where both the critical global slip surface as well as its associated factor of safety is determined. Several practical examples, of known minimum factor of safety and its associated slip surface, have been used to demonstrate the efficiency and capability of the proposed method. The method is intended to be robust and effective to solve problems that involve extremely complicated slope geometry. It is as powerful as any other powerful optimization methods.     

Discrete fractional Fourier transform based on the eigenvectors of tridiagonal and nearly tridiagonal matrices

The recent emergence of the discrete fractional Fourier transform (DFRFT) has caused a revived interest in the eigenanalysis of the discrete Fourier transform (DFT) matrix F with the objective of generating orthonormal Hermite–Gaussian-like eigenvectors. The Grünbaum tridiagonal matrix T—which commutes with matrix F—has only one repeated eigenvalue with multiplicity two and simple remaining eigenvalues. A detailed eigendecomposition of matrix T is performed with the objective of deriving two orthonormal eigenvectors—common to both the F and T matrices—pertaining to the repeated eigenvalue of T. The nearly tridiagonal matrix S first introduced by Dickinson and Steiglitz and later studied by Candan et al.—which commutes with matrix F—is rigorously proved to reduce to a 2×2 block diagonal form by means of a similarity transformation defined in terms of an involutary matrix P. Moreover explicit expressions are derived for the elements of the two tridiagonal submatrices forming the two diagonal blocks in order to circumvent the need for performing two matrix multiplications. Although matrix T has the merit of being tridiagonal and does not need the tridiagonalization step as matrix S, the simulation results show that the eigenvectors of matrix S better approximate samples of the Hermite–Gaussian functions than those of matrix T and moreover they have a shorter computation time due to the block diagonalization result. Consequently they can serve as a better basis for developing the DFRFT.     

Backtracking-Based Instruction Scheduling to Fill Branch Delay Slots

Conventional schedulers schedule operations in dependence order and never revisit or undo a scheduling decision on any operation. In contrast, backtracking schedulers may unschedule operations and can often generate better schedules. This paper develops and evaluates the backtracking approach to fill branch delay slots. We first present the structure of a generic backtracking scheduling algorithm and prove that it terminates. We then describe two more aggressive backtracking schedulers and evaluate their effectiveness. We conclude that aggressive backtracking-based instruction schedulers can effectively improve schedule quality by eliminating branch delay slots with a small amount of additional computation.     

An improved artificial bee colony algorithm based on mean best-guided approach for continuous optimization problems and real brain MRI images segmentation

Neural Computing and Applications - The artificial bee colony (ABC) algorithm is a relatively new algorithm inspired by nature and has been shown to be efficient in contrast to other optimization...     

Energy Cooperation with Sleep Mechanism in Renewable Energy Assisted Cellular HetNets

The emerging fifth generation (5G) and beyond radio access networks are expected to be extremely dense and heterogeneous as compared to the current networks, involving a large number of different classes of base stations (BSs), namely macro, micro, femto and pico BSs. Among several performance requirements 5G and beyond systems aim to achieve, energy efficiency is one of the crucial requirements. In order to achieve energy-efficient design in dense heterogeneous 5G networks, various approaches in terms of resource allocation, off-loading techniques, hardware solutions and energy harvesting are being considered. In this regard, this paper develops an energy usage optimization framework in a cellular heterogeneous network (HetNet) consisting of a central macro-BS and a number of micro-BSs, equipped with renewable energy sources (RESs) such as solar panels and wind turbines. The proposed framework incorporates an energy cooperation mechanism along with a sleep mechanism (BS ON/OFF switching), in which the BSs having lean traffic are put into a sleep mode and their traffic load gets shared by the central BS. The surplus harvested energy from RESs of the sleeping BSs can then be sold back to the grid. An optimization problem for maximizing the utilization of RES and minimizing the usage of the traditional sources, such as utility and generator, is formulated and this mixed integer non-linear programming problem is solved through an interior point method. The presented results for various HetNet sizes demonstrate the significant savings in the energy cost with the proposed RES-enabled HetNet sleep mechanism model over the conventional approaches.

     

Fluorous Metal–Organic Frameworks and Nonporous Coordination Polymers as Low‐κ Dielectrics

A fluorous metal–organic framework [Cu(FBTB)(DMF)] (FMOF‐3) [H₂FBTB = 1,4‐bis(1‐H‐tetrazol‐5‐yl)tetrafluorobenzene] and fluorous nonporous coordination polymer [Ag₂(FBTB)] (FN‐PCP‐1) are synthesized and characterized as for their structural, thermal, and textural properties. Together with the corresponding nonfluorinated analogues lc‐[Cu(BTB)(DMF)] and [Ag₂(BTB)], and two known (super)hydrophobic MOFs, FMOF‐1 and ZIF‐8, they have been investigated as low‐dielectric constant (low‐κ) materials under dry and humid conditions. The results show that substitution of hydrogen with fluorine or fluoroalkyl groups on the organic linker imparts higher hydrophobicity and lower polarizability to the overall material. Pellets of FMOF‐1, FMOF‐3, and FN‐PCP‐1 exhibit κ values of 1.63(1), 2.44(3), and 2.57(3) at 2 × 10⁶ Hz, respectively, under ambient conditions, versus 2.94(8) and 3.79(1) for lc‐[Cu(BTB)(DMF)] and [Ag₂(BTB)], respectively. Such low‐κ values persist even upon exposure to almost saturated humidity levels. Correcting for the experimental pellet density, the intrinsic κ for FMOF‐1 reaches the remarkably low value of 1.28, the lowest value known to date for a hydrophobic material.