Popularity prediction of movies: from statistical modeling to machine learning techniques

Multimedia Tools and Applications - Film industries all over the world are producing several hundred movies rapidly and grabbing the attraction of people of all ages. Every movie producer is of...     

Design and optimization of radioisotope sources for betavoltaic batteries

A Monte Carlo source model using PENELOPE was developed to investigate different tritiated metals in order to design a better radioisotope source for betavoltaic batteries. The source model takes into account the self‐absorption of beta particles in the source which is a major factor for an efficient source design. The average beta energy, beta flux, source power output, and source efficiency were estimated for various source thicknesses. The simulated results for titanium tritide with 0° and 90° angular distributions of beta particles were validated with experimental results. The importance of the backscattering effect due to isotropic particle emission was analyzed. The results showed that the normalized average beta energy increases with the source thickness, and it reaches peak energy depending on the density and the specific activity of the source. The beta flux and power output also increase with increasing source thickness. However, the incremental increase in beta flux and power output becomes minimal for higher thicknesses, as the source efficiency decreases significantly at higher thicknesses due to the self‐absorption effect. Thus, a saturation threshold is reached. A low‐density source material such as beryllium tritide provided a higher power output with higher efficiency. A maximum power output of approximately 4 mW/cm³ was obtained for beryllium tritide with SiC. A form factor approach was used to estimate the optimum source thickness. The optimum source thickness was found near the thickness where the peak beta particle average energy occurs.     

Integrative and multi-disciplinary framework for the 3D rehabilitation of large mandibular defects

The International Journal of Advanced Manufacturing Technology - The restoration of mandibular defects, especially large deformities is regarded as the most challenging surgical procedure owing to...     

A Channel Borrowing Approach for Cluster-based Hierarchical Wireless Sensor Networks

Mobile Networks and Applications - In Wireless Sensor Networks (WSNs), energy-efficient routing is required to conserve the scarce resources of these networks. Various energy-efficient routing...     

An optimized skin texture model using gray-level co-occurrence matrix

Neural Computing and Applications - Texture analysis is devised to address the weakness of color-based image segmentation models by considering the statistical and spatial relations among the group...     

Reliability-based fault analysis models with industrial applications: A systematic literature review

Effective and early fault detection and diagnosis techniques have tremendously enhanced over the years to ensure continuous operations of contemporary complex systems, control cost, and enhance safety in assets-intensive industries, including oil and gas, process, and power generation. The objective of this work is to understand the development of different fault detection and diagnosis methods, their applications, and benefits to the industry. This paper presents a contemporary state-of-the-art systematic literature survey focusing on a comprehensive review of the models for fault detection and their industrial applications. This study uses advanced tools from bibliometric analysis to systematically analyze over 500 peer-reviewed articles on focus areas published since 2010. We first present an exploratory analysis and identify the influential contributions to the field, authors, and countries, among other key indicators.  A network analysis is presented to unveil and visualize the clusters of the distinguishable areas using a co-citation network analysis. Later, a detailed content analysis of the top-100 most-cited papers is carried out to understand the progression of fault detection and artificial intelligence–based algorithms in different industrial applications. The findings of this paper allow us to comprehend the development of reliability-based fault analysis techniques over time, and the use of smart algorithms and their success. This work helps to make a unique contribution toward revealing the future avenues and setting up a prospective research road map for asset-intensive industry, researchers, and policymakers.     

Piezoelectricity of the Transmembrane Protein ba3 Cytochrome c Oxidase

Controlling the electromechanical response of piezoelectric biological structures including tissues, peptides, and amino acids provides new applications for biocompatible, sustainable materials in electronics and medicine. Here, the piezoelectric effect is revealed in another class of biological materials, with robust longitudinal and shear piezoelectricity measured in single crystals of the transmembrane protein ba₃ cytochrome c oxidase from Thermus thermophilus. The experimental findings from piezoresponse force microscopy are substantiated using a range of control measurements and molecular models. The observed longitudinal and shear piezoelectric responses of ≈ 2 and 8 pm V⁻¹, respectively, are comparable to or exceed the performance of commonly used inorganic piezoelectric materials including quartz, aluminum nitride, and zinc oxide. This suggests that transmembrane proteins may provide, in addition to physiological energy transduction, technologically useful piezoelectric material derived entirely from nature. Membrane proteins could extend the range of rationally designed biopiezoelectric materials far beyond the minimalistic peptide motifs currently used in miniaturized energy harvesters, and the finding of robust piezoelectric response in a transmembrane protein also raises fundamental questions regarding the molecular evolution, activation, and role of regulatory proteins in the cellular nanomachinery, indicating that piezoelectricity might be important for fundamental physiological processes.     

Preliminary Structural Data Revealed That the SARS-CoV-2 B.1.617 Variant's RBD Binds to ACE2 Receptor Stronger Than the Wild Type to Enhance the Infectivity

The evolution of new SARS-CoV-2 variants around the globe has made the COVID-19 pandemic more worrisome, further pressuring the health care system and immunity. Novel variations that are unique to the receptor-binding motif (RBM) of the receptor-binding domain (RBD) spike glycoprotein, i. e. L452R-E484Q, may play a different role in the B.1.617 (also known as G/452R.V3) variant's pathogenicity and better survival compared to the wild type. Therefore, a thorough analysis is needed to understand the impact of these mutations on binding with host receptor (RBD) and to guide new therapeutics development. In this study, we used structural and biomolecular simulation techniques to explore the impact of specific mutations (L452R-E484Q) in the B.1.617 variant on the binding of RBD to the host receptor ACE2. Our analysis revealed that the B.1.617 variant possesses different dynamic behaviours by altering dynamic-stability, residual flexibility and structural compactness. Moreover, the new variant had altered the bonding network and structural-dynamics properties significantly. MM/GBSA technique was used, which further established the binding differences between the wild type and B.1.617 variant. In conclusion, this study provides a strong impetus to develop novel drugs against the new SARS-CoV-2 variants.