The Impact of PbF2-Based Glasses on Radiation Shielding and Mechanical Concepts: An Extensive Theoretical and Monte Carlo Simulation Study

Journal of Inorganic and Organometallic Polymers and Materials - This work aimed to investigate the impact of Lead-fluoride based glasses via theoretical and simulation techniques on mechanical and...     

Transition to turbulence in rotating-disk boundary layers—convective and absolute instabilities

This work is an experimental study of mechanisms for transition to turbulence in the boundary layer on a rotating disk. In one case, the focus was on a triad resonance between pairs of traveling cross-flow modes and a stationary cross-flow mode. The other was on the temporal growth of traveling modes through a linear absolute instability mechanism first discovered by Lingwood (1995, J Fluid Mech 314:373–405). Both research directions made use of methods for introducing controlled initial disturbances. One used a distributed array of ink dots placed on the disk surface to enhance a narrow band of azimuthal and radial wave numbers of both stationary and traveling modes. The size of the dots was small so that the disturbances they produce were linear. Another approach introduced temporal disturbances by a short-duration air pulse from a hypodermic tube located above the disk and outside the boundary layer. Hot-wire sensors primarily sensitive to the azimuthal velocity component, were positioned at different spatial (r,θ) locations on the disk to document the growth of disturbances. Spatial correlation measurements were used with two simultaneous sensors to obtain wavenumber vectors. Cross-bicoherence was used to identify three-frequency phase locking. Ensemble averages conditioned on the air pulses revealed wave packets that evolved in time and space. The space–time evolution of the leading and trailing edges of the wave packets were followed past the critical radius for the absolute instability, r _{c A} . With documented linear amplitudes, the spreading of the disturbance wave packets did not continue to grow in time as r _{c A} was approached. Rather, the spreading of the trailing edge of the wave packet decelerated and asymptotically approached a constant. This result supports the linear DNS simulations of Davies and Carpenter (2003, J Fluid Mech 486:287–329) who concluded that the absolute instability mechanism does not result in a global mode, and that linear-disturbance wave packets are dominated by the convective instability. In contrast, wave-number matching between traveling cross-flow modes confirmed a triad resonance that lead to the growth of a low azimuthal number (n = 4) stationary mode. At transition, this mode had the largest amplitude. Signs of this mechanism can be found in past flow visualization of transition to turbulence in rotating disk flows.     

Comments on “Distinguishability quantification of fuzzy sets”

This paper presents some comments on the above paper regarding the manipulation of Gaussian fuzzy sets and provides two useful generalized formulas for both of similarity and similarity/possibility relationships of Gaussian fuzzy sets with different widths.     

Combustion synthesis of NbC/ZrO2 composites: Influence of Cr additives on the microstructure and mechanical properties

Composites of NbC/ZrO₂ reinforced with different weight ratios of Cr metal were prepared by dynamic compaction combustion from a blend of Nb₂O₅–Zr–Cr powders. Factors controlling the synthesis process, the microstructure, and the mechanical properties of the samples, such as Cr wt% and the compression loads were studied. The samples were characterized by XRD, SEM, porosity, and hardness measurements. The porosity value of the sample with no additives was high and reached 31.6 vol%. A sudden decrease in the sample porosity to 2.4 vol % was noticed for the sample containing 3.0 wt% of Cr. A high-density sample with less than 2.0 vol % porosity and maximum hardness of 1038 HV was produced using 5.0 wt% Cr under 300 MPa compression load. Detailed thermodynamic calculations for the effect of Cr additives on the physicochemical properties of the system were introduced.     

The Impact of Metastable Intermolrecular Nanocomposite Particles on Kinetic Decomposition of Heterocyclic Nitramines Using Advanced Solid‐Phase Decomposition Models

Journal of Inorganic and Organometallic Polymers and Materials - Surface oxygen of oxide catalyst has low coordination number; they are negatively charged. Surface oxygen can act active site for...     

Recognizing arabic handwritten characters using deep learning and genetic algorithms

Automated techniques for Arabic content recognition are at a beginning period contrasted with their partners for the Latin and Chinese contents recognition. There is a bulk of handwritten Arabic archives available in libraries, data centers, historical centers, and workplaces. Digitization of these documents facilitates (1) to preserve and transfer the country’s history electronically, (2) to save the physical storage space, (3) to proper handling of the documents, and (4) to enhance the retrieval of information through the Internet and other mediums. Arabic handwritten character recognition (AHCR) systems face several challenges including the unlimited variations in human handwriting and the leakage of large and public databases. In the current study, the segmentation and recognition phases are addressed. The text segmentation challenges and a set of solutions for each challenge are presented. The convolutional neural network (CNN), deep learning approach, is used in the recognition phase. The usage of CNN leads to significant improvements across different machine learning classification algorithms. It facilitates the automatic feature extraction of images. 14 different native CNN architectures are proposed after a set of try-and-error trials. They are trained and tested on the HMBD database that contains 54,115 of the handwritten Arabic characters. Experiments are performed on the native CNN architectures and the best-reported testing accuracy is 91.96%. A transfer learning (TF) and genetic algorithm (GA) approach named “HMB-AHCR-DLGA” is suggested to optimize the training parameters and hyperparameters in the recognition phase. The pre-trained CNN models (VGG16, VGG19, and MobileNetV2) are used in the later approach. Five optimization experiments are performed and the best combinations are reported. The highest reported testing accuracy is 92.88%.

     

Multi-objective topology optimization of multi-component continuum structures via a Kriging-interpolated level set approach

This paper explores a framework for topology optimization of multi-component sheet metal structures, such as those often used in the automotive industry. The primary reason for having multiple components in a structure is to reduce the manufacturing cost, which can become prohibitively expensive otherwise. Having a multi-component structure necessitates re-joining, which often comes at sacrifices in the assembly cost, weight and structural performance. The problem of designing a multi-component structure is thus posed in a multi-objective framework. Approaches to solve the problem may be classified into single and two stage approaches. Two-stage approaches start by focusing solely on structural performance in order to obtain optimal monolithic (single piece) designs, and then the decomposition into multiple components is considered without changing the base topology (identical to the monolithic design). Single-stage approaches simultaneously attempt to optimize both the base topology and its decomposition. Decomposition is an inherently discrete problem, and as such, non-gradient methods are needed for single-stage and second stage of two-stage approaches. This paper adopts an implicit formulation (level-sets) of the design variables, which significantly reduces the number of design variables needed in either single or two stage approaches. The number of design variables in the formulation is independent from the meshing size, which enables application of non-gradient methods to realistic designs. Test results of a short cantilever and an L-shaped bracket studies show reasonable success of both single and two stage approaches, with each approach having different merits.     

Detection of Abnormal Activities from Various Signals Based on Statistical Analysis

We perceive big data with massive datasets of complex and variegated structures in the modern era. Such attributes formulate hindrances while analyzing and storing the data to generate apt aftermaths. Privacy and security are the colossal perturb in the domain space of extensive data analysis. In this paper, our foremost priority is the computing technologies that focus on big data, IoT (Internet of Things), Cloud Computing, Blockchain, and fog computing. Among these, Cloud Computing follows the role of providing on-demand services to their customers by optimizing the cost factor. AWS, Azure, Google Cloud are the major cloud providers today. Fog computing offers new insights into the extension of cloud computing systems by procuring services to the edges of the network. In collaboration with multiple technologies, the Internet of Things takes this into effect, which solves the labyrinth of dealing with advanced services considering its significance in varied application domains. The Blockchain is a dataset that entertains many applications ranging from the fields of crypto-currency to smart contracts. The prospect of this research paper is to present the critical analysis and review it under the umbrella of existing extensive data systems. In this paper, we attend to critics' reviews and address the existing threats to the security of extensive data systems. Moreover, we scrutinize the security attacks on computing systems based upon Cloud, Blockchain, IoT, and fog. This paper lucidly illustrates the different threat behaviour and their impacts on complementary computational technologies. The authors have mooted a precise analysis of cloud-based technologies and discussed their defense mechanism and the security issues of mobile healthcare.

     

On the application of quantum clustering on speech data

In this work, Quantum clustering (QC) algorithm is applied to a labeled dataset of Arabic vowels. The accuracy and processing time are, then, compared with nonhierarchical kernel approaches for unsupervised clustering; namely, k-means, self-organizing map and fuzzy c-means. The choice of speech data is according to large database statistics which reveal that vowels class represents about 60–70% of Arabic speech whereas the remaining percentage is distributed among other sounds. The analysis features, in this work, are the mel-frequency cepstarl coefficients. The results show that all algorithms are competitive from accuracy point of view while QC still guarantees the solution stability.     

Polyaniline/akaganéite nanocomposite for detoxification of noxious Cr(VI) from aquatic environment

In this work, a removal of toxic hexavalent chromium Cr(VI) ion from aqueous solution was investigated and studied using nanocomposite of polyaniline (PANI) and akaganéite nanoparticles (NP). HCl doped PANI, and akaganéite NPs were prepared by chemical oxidative polymerization and co-precipitation techniques, respectively. The synthesized materials were characterized by Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), scanning electron microscope (SEM), and high-resolution transmission electron microscope (HRTEM). It was indicated that the formed oxide NPs were consisted of akaganéite as dominant phase plus minor phases of hematite, magnetite, and/or maghemite. HRTEM images of the prepared nanocomposite demonstrated that the phases of oxide NPs embedded in the nanocomposite had the same crystallinity and morphology of pristine oxide NPs. It was found that size of nanocomposite particles has diameter ranged from 8.95 to 16.21 nm. Cr(VI) removals in a wide pH range from 2 to 9 were appropriated for prepared nanocomposite. The nanocomposite has demonstrated high removal percentage of 99.2 % and removal capacity of 17.36 mg/g for 7.0 mg/L Cr(VI) polluted aqueous solution at pH 2.0 for 5-min contact time. The synthesized nanocomposite was applied to remove Cr(VI) from a leather tanning wastewater sample with efficiency of 93.4 %.