Synthesis of Dihydrobenzofuro[3,2-b]chromenes as Potential 3CLpro Inhibitors of SARS-CoV-2: A Molecular Docking and Molecular Dynamics Study

The recent emergence of pandemic of coronavirus (COVID-19) caused by SARS-CoV-2 has raised significant global health concerns. More importantly, there is no specific therapeutics currently available to combat against this deadly infection. The enzyme 3-chymotrypsin-like cysteine protease (3CLpro) is known to be essential for viral life cycle as it controls the coronavirus replication. 3CLpro could be a potential drug target as established before in the case of severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV). In the current study, we wanted to explore the potential of fused flavonoids as 3CLpro inhibitors. Fused flavonoids (5a,10a-dihydro-11H-benzofuro[3,2-b]chromene) are unexplored for their potential bioactivities due to their low natural occurrences. Their synthetic congeners are also rare due to unavailability of general synthetic methodology. Here we designed a simple strategy to synthesize 5a,10a-dihydro-11H-benzofuro[3,2-b]chromene skeleton and it's four novel derivatives. Our structural bioinformatics study clearly shows excellent potential of the synthesized compounds in comparison to experimentally validated inhibitor N3. Moreover, in-silico ADMET study displays excellent druggability and extremely low level of toxicity of the synthesized molecules. Further, for better understanding, the molecular dynamic approach was implemented to study the change in dynamicity after the compounds bind to the protein. A detailed investigation through clustering analysis and distance calculation gave us sound comprehensive data about their molecular interaction. In summary, we anticipate that the currently synthesized molecules could not only be a potential set of inhibitors against 3CLpro but also the insights acquired from the current study would be instrumental in further developing novel natural flavonoid based anti-COVID therapeutic spectrums.     

DEICA: A differential evolution-based improved clustering algorithm for IoT-based heterogeneous wireless sensor networks

With the evolution of technology, many modern applications like habitat monitoring, environmental monitoring, disaster prediction and management, and telehealth care have been proposed on wireless sensor networks (WSNs) with Internet of Things (IoT) integration. However, the performance of these networks is restricted because of the various constraints imposed due to the participating sensor nodes, such as nonreplaceable limited power units, constrained computation, and limited storage. Power limitation is the most severe among these restrictions. Hence, the researchers have sought schemes enabling energy-efficient network operations as the most crucial issue. A metaheuristic clustering scheme is proposed here to address this problem, which employs the differential evolution (DE) technique as a tool. The proposed scheme achieves improved network performance via the formulation of load-balanced clusters, resulting in a more scalable and adaptable network. The proposed scheme considers multiple parameters such as nodes' energy level, degree, proximity, and population for suitable network partitioning. Through various simulation results and experimentation, it establishes its efficacy over state-of-the-art schemes in respect of load-balanced cluster formation, improved network lifetime, network resource utilization, and network throughput. The proposed scheme ensures up to 57.69%, 33.16%, and 57.74% gains in network lifetime, energy utilization, and data packet delivery under varying network configurations. Besides providing the quantitative analysis, a detailed statistical analysis has also been performed that describes the acceptability of the proposed scheme under different network configurations.     

Recent Development Strategies for Bismuth-Driven Materials in Sustainable Energy Systems and Environmental Restoration

Bismuth(Bi)-based materials have gained considerable attention in recent decades for use in a diverse range of sustainable energy and environmental applications due to their low toxicity and eco-friendliness. Bi materials are widely employed in electrochemical energy storage and conversion devices, exhibiting excellent catalytic and non-catalytic performance, as well as CO₂/N₂ reduction and water treatment systems. A variety of Bi materials, including its oxides, chalcogenides, oxyhalides, bismuthates, and other composites, have been developed for understanding their physicochemical properties. In this review, a comprehensive overview of the properties of individual Bi material systems and their use in a range of applications is provided. This review highlights the implementation of novel strategies to modify Bi materials based on morphological and facet control, doping/defect inclusion, and composite/heterojunction formation. The factors affecting the development of different classes of Bi materials and how their control differs between individual Bi compounds are also described. In particular, the development process for these material systems, their mass production, and related challenges are considered. Thus, the key components in Bi compounds are compared in terms of their properties, design, and applications. Finally, the future potential and challenges associated with Bi complexes are presented as a pathway for new innovations.     

Structural Innovation for Better MOSFET Performance Suitable for Low Power Application

Silicon - In this work, an innovative architecture of gate underlap junctionless double-gate MOSFET has been introduced with the idea of using triangular oxide layers to control the electric field...     

Comparison between self-organizing map and principal component analysis for water quality assessment and hydro-geochemical characterization in dyke intruded complex geological settings

Hydro-geochemical characterization is challenging in dyke intruded complex geological setting. The comparison between self-organizing map (SOM) classification and principal component analysis (PCA) is used for better understanding of hydrogeological process surrounding Amarpur dyke in Dhanbad district, Jharkhand. Total 30 water samples were collected and tested for 12 physicochemical parameters. The K-means clustering with SOM grouped the water quality data into cluster 1 (46.67%, low mineralization), cluster 2 (36.67%, moderate mineralization) and cluster 3 (16.67%, high mineralization). The clusters of the majority of samples identified by PCA analysis is almost same as identified by SOM with little difficulty in discriminating between cluster 2 and cluster 3. The transformation of Ca-HCO₃ to Ca-Cl-SO₄ occurred because of exchange of Ca²⁺ with Na⁺ adsorbed in the aquifer leading excess of sulphate ions. The results of this study suggest that SOM is an effective tool for a better understanding of patterns and processes driving water quality.     

Enhanced Thermoelectric Performance of Rare-Earth-Free n-Type Oxide Perovskite Composite with Graphene Analogous 2D MXene

Here, the first experimental demonstration on the effect of incorporating new generation 2D material, MXene, on the thermoelectric performance of rare-earth-free oxide perovskite is reported. The charge localization phenomenon is predominant in the electron transport of doped SrTiO₃ perovskites, which deters from achieving a higher thermoelectric power factor in these oxides. In this work, it is shown that incorporating Ti₃C₂T_x MXene in a matrix of SrTi_0.85Nb_0.15O₃ (STN) facilitates the delocalization of electrons resulting in better than single-crystal-like electron mobility in polycrystalline composites. A 1851% increase in electrical conductivity and a 1000% enhancement in power factor are attained. Besides, anharmonicity caused by MXene in the STN matrix has led to enhanced Umklapp scattering giving rise to lower lattice thermal conductivity. Hence, 700% ZT enhancement is achieved in this composite. Further, a prototype of thermoelectric generator (TEG) using only n-type STN + MXene is fabricated and a power output of 38 mW is obtained, which is higher than the reported values for oxide TEG.     

A Simulation Approach to Study the Effect of SiC Polytypism Factor on Sensitivity of Piezoresistive MEMS Pressure Sensor

Silicon - SiC is a well known wide band gap semiconductor explored for realizing the piezoresistive micro-electro-mechanical systems (MEMS) pressure sensors for harsh environments. In this work a...     

An improved output feedback controller design for linear discrete-time systems using a matrix decomposition method

This paper presents the design of output feedback controllers for discrete-time (DT) linear systems. New sufficient LMI conditions are derived for designing static $H_2$ and $H_{\infty }$ controllers using decomposition of an auxiliary matrix. The decomposition facilitates linearization of nonlinear term of reduced size to obtain linear matrix inequality criteria. This leads to less conservative results as shown in the numerical examples. In addition, the proposed static output feedback criteria is also used for designing dynamic output feedback controllers for DT systems. Furthermore, a comparative study is also made for the proposed design method with the results existing in the literature. Finally, a DT static output feedback $H_{\infty }$ controller is designed for a quarter-car suspension system. Simulation results are provided to show the efficacy of the proposed design method.