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...     

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...     

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.     

Knowledge-Based Virtual Modeling and Simulation of Manufacturing Processes for Industry 4.0

Abstract

Industry 4.0 aims at providing a digital representation of a production landscape, but the challenges in building, maintaining, optimizing, and evolving digital models in inter-organizational production chains have not been identified yet in a systematic manner. In this paper, various Industry 4.0 research and technical challenges are addressed, and their present scenario is discussed. Moreover, in this article, the novel concept of developing experience-based virtual models of engineering entities, process, and the factory is presented. These models of production units, processes, and procedures are accomplished by virtual engineering object (VEO), virtual engineering process (VEP), and virtual engineering factory (VEF), using the knowledge representation technique of Decisional DNA. This blend of the virtual and physical domains permits monitoring of systems and analysis of data to foresee problems before they occur, develop new opportunities, prevent downtime, and even plan for the future by using simulations. Furthermore, the proposed virtual model concept not only has the capability of Query Processing and Data Integration for Industrial Data but also real-time visualization of data stream processing.     

Experimental investigation of multiphase flow behavior in drilling annuli using high speed visualization technique

Imaging with high definition video camera is an important technique to visualize the drilling conditions and to study the physics of complex multiphase flow associated with the hole cleaning process. The main advantage of visualizing multiphase flow in a drilling annulus is that the viewer can easily distinguish fluid phases, flow patterns and thicknesses of cutting beds. In this paper the hole cleaning process which involves the transportation of cuttings through a horizontal annulus was studied. The two-phase (solid-liquid) and the three-phase (solid-liquid-gas) flow conditions involved in this kind of annular transportation were experimentally simulated and images were taken using a high definition camera. Analyzing the captured images, a number of important parameters like velocities of different phases, heights of solid beds and sizes of gas bubbles were determined. Two different techniques based on an image analysis software and MATLAB coding were used for the determinations. The results were compared to validate the image analyzing methodology. The visualization technique developed in this paper has a direct application in investigating the critical conditions required for efficient hole cleaning as well as in optimizing the mud program during both planning and operational phases of drilling. Particularly, it would be useful in predicting the cuttings transport performance, estimating solid bed height, gas bubble size, and mean velocities of bubbles/particles.     

Blockchain for Collaborative Businesses

Mobile Networks and Applications - Blockchain applications have continuously improved ever since its first debut on cryptocurrency. From then on, its uses have branched out from the financial...