Kinematic Self-Similar Plane Symmetric Solutions

This paper is devoted to classify the most general plane symmetric spacetimes according to kinematic self-similar perfect fluid and dust solutions. We provide a classification of the kinematic self-similarity of the first, second, zeroth and infinite kinds with different equations of state, where the self-similar vector is not only tilted but also orthogonal and parallel to the fluid flow. This scheme of classification yields twenty four plane symmetric kinematic self-similar solutions. Some of these solutions turn out to be vacuum. These solutions can be matched with the already classified plane symmetric solutions under particular coordinate transformations. As a result, these reduce to sixteen independent plane symmetric kinematic self-similar solutions.Comment: 29 pages, accepted for publication in Classical Quantum Gravit

Energy and Momentum of a Class of Rotating Gravitational Waves

We calculate energy and momentum for a class of cylindrical rotating gravitational waves using Einstein and Papapetrou's prescriptions. It is shown that the results obtained are reduced to the special case of the cylindrical gravitational waves already available in the literature.Comment: 11 pages, no figure, Late

Fault Sneaking Attack: a Stealthy Framework for Misleading Deep Neural Networks

Despite the great achievements of deep neural networks (DNNs), the vulnerability of state-of-the-art DNNs raises security concerns of DNNs in many application domains requiring high reliability.We propose the fault sneaking attack on DNNs, where the adversary aims to misclassify certain input images into any target labels by modifying the DNN parameters. We apply ADMM (alternating direction method of multipliers) for solving the optimization problem of the fault sneaking attack with two constraints: 1) the classification of the other images should be unchanged and 2) the parameter modifications should be minimized. Specifically, the first constraint requires us not only to inject designated faults (misclassifications), but also to hide the faults for stealthy or sneaking considerations by maintaining model accuracy. The second constraint requires us to minimize the parameter modifications (using L0 norm to measure the number of modifications and L2 norm to measure the magnitude of modifications). Comprehensive experimental evaluation demonstrates that the proposed framework can inject multiple sneaking faults without losing the overall test accuracy performance.Comment: Accepted by the 56th Design Automation Conference (DAC 2019

Machine Learning-Based Prediction of Compressive Performance in Circular Concrete Columns Confined with FRP

This article presents a comprehensive investigation, focusing on the prediction and formulation of the design equation of compressive strength of circular concrete columns confined with Fiber Reinforced Polymer (FRP) using advanced machine learning models. Through an extensive analysis of 170 experimental data specimens, the study examines the effects of six key parameters, including concrete cylinder diameter, concrete cylinder-FRP thickness, compressive strength of concrete without FRP, initial compressive strain of concrete without FRP, elastic modulus and tensile strength of FRP, on the compressive strength of the circular concrete columns confined with FRP. The predictive model and design equation of compressive strength is developed using a machine learning technique, specifically the artificial neural networks (ANN) model. The results demonstrates strong correlations between the compressive strength of the circular concrete columns confined with FRP and certain factors, such as the compressive strength of the concrete and compressive strain of the concrete column without FRP, elastic modulus of FRP, and tensile strength of FRP. The ANN model specifically developed using Neural Designer, exhibits superior predictive accuracy compared to other constitutive models, showcasing its potential for practical implementation. The study's findings contribute valuable insights into accurately predicting the compressive performance of circular concrete columns confined with FRP, which can aid in optimizing and designing civil engineering structures for enhanced performance and efficiency

A rapid and simple method for staining of the crystal protein ofBacillus thuringiensis

A rapid and simple method of staining for the crystal protein (δ-endotoxin or parasporal body) ofBacillus thuringiensis has been developed. Changes in colonial morphology were observed when cells lost their ability to form crystal protein or both crystal protein and spore

Energy Distribution associated with Static Axisymmetric Solutions

This paper has been addressed to a very old but burning problem of energy in General Relativity. We evaluate energy and momentum densities for the static and axisymmetric solutions. This specializes to two metrics, i.e., Erez-Rosen and the gamma metrics, belonging to the Weyl class. We apply four well-known prescriptions of Einstein, Landau-Lifshitz, Papaterou and M$\ddot{o}$ller to compute energy-momentum density components. We obtain that these prescriptions do not provide similar energy density, however momentum becomes constant in each case. The results can be matched under particular boundary conditions.Comment: 18 pages, accepted for publication in Astrophysics and SpaceScienc

Association of Urinary Vitamin D-binding Protein and Megalin as Biomarkers for Diabetic Nephropathy in Type 2 Diabetes Mellitus in Qatari Patients

Abstract Background: Nephropathy is a common complication of type 2 diabetes mellitus (T2DM). Previous studies revealed that T2DM patients with nephropathy have higher concentrations of urinary Vitamin D Binding Protein (VDBP) that carries vitamin D to the target tissues, and megalin that mediates endocytosis in the proximal tubule than those who are healthy. Methodology: 196 urine samples with their blood data were obtained from Qatar Biobank, of which 21 samples were measured for VDBP and megalin using enzyme-linked immunosorbent assay (ELISA). They were divided intothree groups; group 1 (control group with eGFR ≥ 90 mL/min/1.73 m²), group 2 (T2DM patients with eGFR ≥ 90 mL/min/1.73 m²) and group 3; (T2DM patients with eGFR < 90 mL/min/1.73 m²). Results: Urinary VDBP and Megalin levels were non-significantly elevated in T2DM patients with DN (P=0.198) and (P=0.293) respectively. Moreover, a weak negative correlation was observed between the urinary VDBP and Megalin levels with eGFR (r=-0.326, P=0.149) and (r=-0.315, P=0.165) respectively. Conclusion: Previous studies revealed that uVDBP and Megalin are potential biomarkers for DN in T2DM patients. However, the current study reveals that urinary VDBP and megalin levels were non-significantly elevated in T2DM patients with DN. Furthermore, eGFR showed a weak negative correlation with urinary VDBP and megalin levels. However, it is suggested that these results could be due to some limitations. Further tests should be performed on larger sample size to confirm the association of Megalin and VDBP in T2DM nephropathy

Teleparallel Energy-Momentum Distribution of Spatially Homogeneous Rotating Spacetimes

The energy-momentum distribution of spatially homogeneous rotating spacetimes in the context of teleparallel theory of gravity is investigated. For this purpose, we use the teleparallel version of Moller prescription. It is found that the components of energy-momentum density are finite and well-defined but are different from General Relativity. However, the energy-momentum density components become the same in both theories under certain assumptions. We also analyse these quantities for some special solutions of the spatially homogeneous rotating spacetimes.Comment: 12 pages, accepted for publication in Int. J. Theor. Phy

An Innovative Approach Based on Machine Learning to Evaluate the Risk Factors Importance in Diagnosing Keratoconus

Background and objective: Keratoconus is a non-inflammatory corneal condition affecting both eyes and is present in one out of every 2,000 people worldwide. The cornea deforms into a conical shape and thins, resulting in high-order aberrations and gradual vision loss. Risk factor analysis in the degradation of keratoconus is under-researched. Methods: This research work investigates and uses effective machine learning models to gain insight into how much the risk factors of a patient contribute towards the progressive stages of keratoconus, as well as how significant these factors are in the creation of an accurate prediction model. This research demonstrates the value of machine learning approaches on a clinical dataset. This research paper employs several machine learning algorithms to classify the patients' stage of keratoconus using clinical information, such as measurements of the cornea's topography, elevation, and pachymetry taken using pentacam equipment at Sydney's Vision Eye Institute Chatswood. Results: Eight different machine learning techniques were investigated over three variations of a dataset and achieved an average accuracy of 68, 80, and 90% for the risk factor, pentacam, and cumulative datasets, respectively. The results show a significant increase in accuracy and a 97% increase in AUC upon addition of risk factor data compared to the models trained on pentacam data alone. The machine learning methods shown in this paper outperform those in current research. Conclusions: This research highlights the importance of machine learning methods and risk factor data in the diagnosis of keratoconus and highlights the patient's primary optical aid as the strongest risk factor. The goal of this research is to support the work of the ophthalmologists in diagnosing keratoconus and provide better care for the patient

Energy Contents of Some Well-Known Solutions in Teleparallel Gravity

In the context of teleparallel equivalent to General Relativity, we study energy and its relevant quantities for some well-known black hole solutions. For this purpose, we use the Hamiltonian approach which gives reasonable and interesting results. We find that our results of energy exactly coincide with several prescriptions in General Relativity. This supports the claim that different energy-momentum prescriptions can give identical results for a given spacetime. We also evaluate energy-momentum flux of these solutions.Comment: 16 pages, accepted for publication in Astrophys. Space Sc