Determining human-coronavirus protein-protein interaction using machine intelligence

The Severe Acute Respiratory Syndrome CoronaVirus 2(SARS-CoV-2)virus spread the novel CoronaVirus−19(nCoV-19)pandemic,resulting in millions of fatalities globally.Recent research demonstrated that the Protein-Protein Interaction(PPI)between SARS-CoV-2 and human proteins is accountable for viral pathogenesis.However,many of these PPIs are poorly understood and unexplored,necessitating a more in-depth investigation to find latent yet critical interactions.This article elucidates the host-viral PPI through Machine Learning(ML)lenses and validates the biological significance of the same using web-based tools.ML classifiers are designed based on comprehensive datasets with five sequence-based features of human proteins,namely Amino Acid Composition,Pseudo Amino Acid Composition,Conjoint Triad,Dipeptide Composition,and Normalized Auto Correlation.A majority voting rule-based ensemble method composed of the Random Forest Model(RFM),AdaBoost,and Bagging technique is proposed that delivers encouraging statistical performance compared to other models employed in this work.The proposed ensemble model predicted a total of 111 possible SARS-CoV-2 human target proteins with a high likelihood factor≥70%,validated by utilizing Gene Ontology(GO)and KEGG pathway enrichment analysis.Consequently,this research can aid in a deeper understanding of the molecular mechanisms underlying viral pathogenesis and provide clues for developing more efficient anti-COVID medications.

Nanoscale cryptographic architecture design using quantum-dot cellular automata

Quantum-dot cellular automata (QCA) based on cryptography is a new paradigm in the field of nanotechnology. The overall performance of QCA is high compared to traditional complementary metal-oxide semiconductor (CMOS) technology. To achieve data security during nanocommunication, a cryptography-based application is proposed. The devised circuit encrypts the input data and passes it to an output channel through a nanorouter cum data path selector, where the data is decrypted back to its original form. The results along with theoretical implication prove the accuracy of the circuit. Power dissipation and circuit complexity of the circuit have been analyzed.

Reversible binary subtractor design using quantum dot-cellular automata

在纳米技术领域,量子元胞自动机(quantum dot-cellular automata,QCA)可作为传统互补金属氧化物半导体(complementary metal oxide semiconductor,CMOS)电路的替代方案,具有可观的发展前景。QCA设备密度高,运行速度快,且功耗极低。在QCA中,可逆逻辑有着广泛应用。研究者已开发多个基于QCA的可逆逻辑电路,但基于QCA的可逆二进制减法器研究却不多见。QCA低功耗和高电流密度的特点,使其可用于设计纳米尺度节能逻辑电路。然而,其对大量逻辑门的需求和不利的无用输出,可能限制基于QCA逻辑电路的功能。我们利用QCA设计并实现了一个DG门,实现了DG门的普遍特征。利用该DG门的QCA部分,可以获得新的可逆二进制减法器。相比现有可逆减法器,我们设计的减法器量子成本低,无用输出少。电路设计与模拟基于QCA Designer-2.0.3软件。

Quantum-dot cellular automata based reversible low power parity generator and parity checker design for nanocommunication

Quantum-dot cellular automata(QCA) is an emerging area of research in reversible computing. It can be used to design nanoscale circuits. In nanocommunication, the detection and correction of errors in a received message is a major factor. Besides, device density and power dissipation are the key issues in the nanocommunication architecture. For the first time, QCA-based designs of the reversible low-power odd parity generator and odd parity checker using the Feynman gate have been achieved in this study. Using the proposed parity generator and parity checker circuit, a nanocommunication architecture is proposed. The detection of errors in the received message during transmission is also explored. The proposed QCA Feynman gate outshines the existing ones in terms of area, cell count, and delay. The quantum costs of the proposed conventional reversible circuits and their QCA layouts are calculated and compared, which establishes that the proposed QCA circuits have very low quantum cost compared to conventional designs. The energy dissipation by the layouts is estimated, which ensures the possibility of QCA nano-device serving as an alternative platform for the implementation of reversible circuits. The stability of the proposed circuits under thermal randomness is analyzed, showing the operational efficiency of the circuits. The simulation results of the proposed design are tested with theoretical values, showing the accuracy of the circuits. The proposed circuits can be used to design more complex low-power nanoscale lossless nanocommunication architecture such as nano-transmitters and nano-receivers.

MedGini:Gini index based sustainable health monitoring system using dew computing

Monitoring biosignals is crucial for intelligent health applications.Internet of Health Things(IoHT)provides a new path for monitoring the biosignals.Environment adaptive data dissemination is the primary requirement for the deployment of time and space-efficient monitoring systems.Existing dew-based systems lack an opportunistic architecture of data-synchronization with the cloud.This paper proposes a model that makes efficient use of IoT and cloud-dew architecture for a sustainable health monitoring system.Wireless sensor nodes are used to monitor the biosignals dynamically.All accrued data is temporarily stored in the dew layer.It is synchronized with the cloud at a subsequent phase to achieve seamless accessibility and optimal scalability of the data.Data synchronization plays an essential role in the cloud dew framework.We have used the Gini index and Shannon entropy to ensure intelligent data synchronization with the cloud.Sometimes sensors produce erroneous data,which poses a significant threat to the sustainable health monitoring system.Fuzzy normal distribution with a triangular membership function has been used to clean up the data and filter out the outliers.Further,we compared the proposed MedGini model with the existing models and analyzed the system performance.MedGini is found to outperform others concerning cost and power consumption.