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.     

Numerical study of hydrodynamic flow of a Casson nanomaterial past an inclined sheet under porous medium

The main aim of the current paper is to investigate the mass and heat transportation of a Casson nanomaterial generated by the inclination of the surface. The magnetic field effect along with suction or injection are considered. The working nanomaterial is taken into consideration based on the concept of the Buongiorno nanofluid theory, which explores the thermal efficiencies of liquid flows under movement of Brownian and thermophoretic phenomena. The emergent system of differential expressions is converted to dimensionless form with the help of the appropriate transformations. This system is numerically executed by the implementation of Keller–Box and Newton's schemes. A good agreement of results can be found with the previous data in a limiting approach. The behavior of the physical quantities under concern, including energy exchange, Sherwood number, and wall shear stress are portrayed through graphs and in tabular form. The Nusselt number and Sherwood number are found to diminish against the altered magnitudes of Brownian motion and the inclination parameter. Moreover, the velocity profile decreases with the growth of the inclination effect. In the same vein, the buoyancy force and solutal buoyancy effects show a direct relation with the velocity field. The outcomes have promising technological uses in liquid‐based systems related to stretchable constituents.     

Catalytic steam reforming of tar for enhancing hydrogen production from biomass gasification: a review

Presently, the global search for alternative renewable energy sources is rising due to the depletion of fossil fuel and rising greenhouse gas (GHG) emissions. Among alternatives, hydrogen (H₂) produced from biomass gasification is considered a green energy sector, due to its environmentally friendly, sustainable, and renewable characteristics. However, tar formation along with syngas is a severe impediment to biomass conversion efficiency, which results in process-related problems. Typically, tar consists of various hydrocarbons (HCs), which are also sources for syngas. Hence, catalytic steam reforming is an effective technique to address tar formation and improve H₂ production from biomass gasification. Of the various classes in existence, supported metal catalysts are considered the most promising. This paper focuses on the current researching status, prospects, and challenges of steam reforming of gasified biomass tar. Besides, it includes recent developments in tar compositional analysis, supported metal catalysts, along with the reactions and process conditions for catalytic steam reforming. Moreover, it discusses alternatives such as dry and autothermal reforming of tar.     

Direct Formation of Titanium Carbide via In Situ Carbothermal Reduction of TiO2/Carbon Cladding for Coating Application

Protection of Metals and Physical Chemistry of Surfaces - In situ direct formation of titanium carbide (TiC) was realized via tungsten inert gas (TIG) arc surface cladding using titanium dioxide...     

A provable and secure mobile user authentication scheme for mobile cloud computing services

The mobile cloud computing (MCC) has enriched the quality of services that the clients access from remote cloud‐based servers. The growth in the number of wireless users for MCC has further augmented the requirement for a robust and efficient authenticated key agreement mechanism. Formerly, the users would access cloud services from various cloud‐based service providers and authenticate one another only after communicating with the trusted third party (TTP). This requirement for the clients to access the TTP during each mutual authentication session, in earlier schemes, contributes to the redundant latency overheads for the protocol. Recently, Tsai et al have presented a bilinear pairing based multi‐server authentication (MSA) protocol, to bypass the TTP, at least during mutual authentication. The scheme construction works fine, as far as the elimination of TTP involvement for authentication has been concerned. However, Tsai et al scheme has been found vulnerable to server spoofing attack and desynchronization attack, and lacks smart card‐based user verification, which renders the protocol inapt for practical implementation in different access networks. Hence, we have proposed an improved model designed with bilinear pairing operations, countering the identified threats as posed to Tsai scheme. Additionally, the proposed scheme is backed up by performance evaluation and formal security analysis.     

Security and key management in IoT‐based wireless sensor networks: An authentication protocol using symmetric key

Wireless sensor networks (WSN) consist of hundreds of miniature sensor nodes to sense various events in the surrounding environment and report back to the base station. Sensor networks are at the base of internet of things (IoT) and smart computing applications where a function is performed as a result of sensed event or information. However, in resource‐limited WSN authenticating a remote user is a vital security concern. Recently, researchers put forth various authentication protocols to address different security issues. Gope et al presented a protocol claiming resistance against known attacks. A thorough analysis of their protocol shows that it is vulnerable to user traceability, stolen verifier, and denial of service (DoS) attacks. In this article, an enhanced symmetric key‐based authentication protocol for IoT‐based WSN has been presented. The proposed protocol has the ability to counter user traceability, stolen verifier, and DoS attacks. Furthermore, the proposed protocol has been simulated and verified using Proverif and BAN logic. The proposed protocol has the same communication cost as the baseline protocol; however, in computation cost, it has 52.63% efficiency as compared with the baseline protocol.     

Discovering and tracking query oriented active online social groups in dynamic information network

The efficient identification of social groups with common interests is a key consideration for viral marketing in online social networking platforms. Most existing studies in social groups or community detection either focus on the common attributes of the nodes (users) or rely on only the topological links of the social network graph. The temporal evolution of user activities and interests have not been thoroughly studied to identify their effects on the formation of groups. In this paper, we investigate the problem of discovering and tracking time-sensitive activity driven user groups in dynamic social networks for a given input query consisting a set of topics. The users in these groups have the tendency to be temporally similar in terms of their activities on the topics of interest. To this end, we develop two baseline solutions to discover effective social groups. The first solution uses the network structure, whereas the second one uses the topics of common interest. We further propose an index-based method to incrementally track the evolution of groups with a lower computational cost. Our main idea is based on the observation that the degree of user activeness often degrades or upgrades widely over a period of time. The temporal tendency of user activities is modelled as the freshness of recent activities by tracking the social streams with a fading time window. We conduct extensive experiments on three real data sets to demonstrate the effectiveness and efficiency of the proposed methods. We also report some interesting observations on the temporal evolution of the discovered social groups using case studies.

     

Continuous microwave assisted flow synthesis and characterization of calcium deficient hydroxyapatite nanorods

Synthetic calcium deficient hydroxyapatite (CDHA) nanorods (<100?nm) were rapidly prepared with the help of a new continuous microwave assisted flow synthesis (CMFS) reactor in 5?min only from aqueous solution of calcium hydroxide and orthophosphoric acid at pH 8.5. The effect of various reaction parameters like, pH, concentration, temperature, residence time, degree of crystallinity and particle surface area were studied in detail. The phase purity, particle size and morphology of the powder samples were characterised by techniques such as X-ray diffraction analysis, transmission electron microscopy, scanning electron microscopy and FTIR and Raman spectroscopy. With the help of X-ray photoelectron spectroscopy, the chemical analysis was completed. Measurements were taken into account to estimate the particle size following the dynamic light scattering. The results showed that the employed synthesis procedure offered an efficient and economical route to achieve high quality nano-sized products with suitable size and low level of impurities.     

Cyber-based design for additive manufacturing using artificial neural networks for Industry 4.0

Additive Manufacturing (AM) requires integrated networking, embedded controls and cloud computing technologies to increase their efficiency and resource utilisation. However, currently there is no readily applicable system that can be used for cloud-based AM. The objective of this research is to develop a framework for designing a cyber additive manufacturing system that integrates an expert system with Internet of Things (IoT). An Artificial Neural Network (ANN) based expert system was implemented to classify input part designs based on CAD data and user inputs. Three ANN algorithms were trained on a knowledge base to identify optimal AM processes for different part designs. A two-stage model was used to enhance the prediction accuracy above 90% by increasing the number of input factors and datasets. A cyber interface was developed to query AM machine availability and resource capability using a Node-RED IoT device simulator. The dynamic AM machine identification system developed using an application programme interface (API) that integrates inputs from the smart algorithm and IoT interface for real-time predictions. This research establishes a foundation for the development of a cyber additive design for manufacturing system which can dynamically allocate digital designs to different AM techniques over the cyber network.