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A molecular docking study of SARS-CoV-2 main protease against phytochemicals of Boerhavia diffusa Linn. for novel COVID-19 drug discovery
SARS-CoV-2, the causative virus of the Corona virus disease that was first recorded in 2019 (COVID-19), has already affected over 110 million people across the world with no clear targeted drug therapy that can be efficiently administered to the wide spread victims. This study tries to discover a novel potential inhibitor to the main protease of the virus, by computer aided drug discovery where various major active phytochemicals of the plant Boerhavia diffusa Linn. namely 2-3-4 beta-Ecdysone, Bioquercetin, Biorobin, Boeravinone J, Boerhavisterol, kaempferol, Liriodendrin, quercetin and trans-caftaric acid were docked to SAR-CoV-2 Main Protease using Molecular docking server. The ligands that showed the least binding energy were Biorobin with ? 8.17kcal/mol, Bioquercetin with ? 7.97kcal/mol and Boerhavisterol with ? 6.77kcal/mol. These binding energies were found to be favorable for an efficient docking and resultant inhibition of the viral main protease. The graphical illustrations and visualizations of the docking were obtained along with inhibition constant, intermolecular energy (total and degenerate), interaction surfaces and HB Plot for all the successfully docked conditions of all the 9 ligands mentioned. Additionally the druglikeness of the top 3 hits namely Bioquercetin, Biorobin and Boeravisterol were tested by ADME studies and Boeravisterol was found to be a suitable candidate obeying the Lipinskys rule. Since the main protease of SARS has been reported to possess structural similarity with the main protease of MERS, comparative docking of these ligands were also carried out on the MERS Mpro, however the binding energies for this target was found to be unfavorable for spontaneous binding. From these results, it was concluded that Boerhavia diffusa possess potential therapeutic properties against COVID-19. 2021, Indian Virological Society. -
Block chain-based security and authentication for forensics application using consensus proof of work and zero knowledge protocol
The technique that checks the origin, integrity, Zero-Knowledge authenticity of photographs is known as image authentication. Numerous studies on image authentication have revealed numerous trade-offs between four desirable features, namely robustness, security, flexibility, and efficiency. This study demonstrated a high-security Forensic Image (FI) as well as an authentication mechanism. Initially, the FI considered image registration with features for the Consensus method (CM) to generate blocks on each feature using a hypothesis test-based similarity measure. Because Proof-of-Work (PoW) blockchain technology is widely used, maintaining the Consensus PoW(CPoW) requires a massive amount of computing power. ZKP authentication is a critical cryptographic mechanism that authenticates network nodes without revealing the users identity or any other data given by the user. The blockchain stores the secret information, as well as the hash value of the original FI. This allows for the tracking of all medical pictures exchanged through the proposed blockchain network. The blockchain stores the private information as well as the hash value of the original medical image. The experimental results indicate the utility of the proposed approach with performance measures in contrast to established security analysis methods. Bharati Vidyapeeth's Institute of Computer Applications and Management 2024. -
A Study of Customer Satisfaction towards Selected Hotels in Bangalore
Golden Research Thoughts, Vol. 2, Issue No. 2, pp. 61-68, ISSN No. 2231-5063 -
An investigation of the business level strategies in Zimbabwe food manufacturing sector (2006 -2013) /
International Journal Of Science And Research, Vol.3, Issue 6, pp.1052-1063, ISSN No: 2319-7064. -
Effectual Energy Optimization Stratagems for Wireless Sensor Network Collections Through Fuzzy-Based Inadequate Clustering
Wireless Sensor Networks (WSNs) are crucial in the burgeoning Internet of Things (IoT) landscape, serving as a backbone technology that enables myriad applications across various industries. Originating as a simple methodology, WSNs have evolved significantly, propelled by rapid advancements in sensor technology and hardware capabilities. These networks play a pivotal role in collecting and transmitting data, which is essential for the infrastructure of most IoT systems. WSNs operate by deploying sensor nodes across diverse locations to gather environmental data. This scalability and adaptability of WSNs were demonstrated in studies where network coverage was expanded to include 100 and 200 nodes. Notably, the implementation of the innovative FLECH (Fuzzy Logic Energy-efficient Clustering Hierarchy) protocol significantly enhanced energy efficiency, reducing consumption by 12.69% in networks with 100 nodes and by 36.85% in those with 200 nodes, compared to the traditional LEACH (Low-Energy Adaptive Clustering Hierarchy) protocol. This work innovatively combines fuzzy logic and Particle Swarm Optimization (PSO) for efficient Cluster Head selection in Wireless Sensor Networks. The evaluation of these protocols involved numerous simulations and communication tests to ascertain the First Node Die (FND) pointindicative of when a network begins to lose efficacy due to energy depletion. Results indicated that the LEACH protocol reached the FND point faster than FLECH, suggesting that FLECH may offer better longevity and durability for IoT applications, aligning with the needs for sustainable and efficient operation in expanding technological ecosystems. The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024. -
Novel approaches for nonlinear Sine-Gordon equations using two efficient techniques
In this work, we obtained a new functional matrix using Clique-polynomials of complete graphs (Formula presented.) with (Formula presented.) vertices and considered a new approach to solving the SineGordon (SG) equation. The clique polynomial method transforms this equation into a system of algebraic equations. The solution will be drawn with the help of Newton Raphsons method. Also, we employed the q-homotopy analysis transform method (q-HATM), which is the proper collision of the Laplace transform and the q-homotopy analysis method (q-HAM). To witness the reliability and accuracy of the considered schemes, some illustrations of the SG equation and double SG equation are considered. Here, the SG equation is solved easily and elegantly without using discretization or transformation of the equation by using the q-HATM. Also, in q-HATM, the presence of homotopy and axillary parameters allows us to have a large convergence region. The 3D surfaces of acquired solutions are drawn effectively. The tables of error analysis demonstrate the success of these methods. 2024 Informa UK Limited, trading as Taylor & Francis Group. -
Hybrid models for intraday stock price forecasting based on artificial neural networks and metaheuristic algorithms
Stock market prediction is one of the critical issues in fiscal market. It is important issue for the traders and investors. Artificial Neural Networks (ANNs) associated with nature inspired algorithms are playing an increasingly vital role in many areas including medical field, security systems and stock market. Several prediction models have been developed by researchers to forecast stock market trend. However, few studies have focused on improving stock market prediction accuracy especially when utilizing artificial neural networks to perform the analysis. This paper proposed nine new integrated models for forecasting intraday stock price based on the potential of three ANNs, Back Propagation Neural Network (BPNN), Radial Basis Function Neural Network (RBFNN), Time Delay Neural Network (TDNN) and nature inspired algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC).The developed models were named as GA-BPNN, PSO-BPNN, ABC-BPNN, GA-RBFNN, PSO-RBFNN, ABC-RBFNN, GA-TDNN, PSO-TDNN and ABC-TDNN. Nature inspired algorithms are employed for optimizing the parameters of ANNs. Technical indicators calculated from historical data are fed as input to developed models. Proposed hybrid models validated on four datasets representing different sectors in NSE. Four statistical metrics, Root Mean Square Error (RMSE), Hit Rate (HR), Error Rate (ER) and prediction accuracy were utilized to gauge the performance of the developed models. Results proved that the PSO-BPNN model yielded the highest prediction accuracy in estimating intraday stock price. The other models, GA-BPNN, ABC-BPNN, GA-RBFNN, PSO-RBFNN, ABC-RBFNN, GA-TDNN, PSO-TDNN and ABC-TDNN produced lower performance with mean prediction accuracy of 97.24%, 98.37%, 84.01%, 85.15%, 84.01%, 83.87%, 89.95% and 78.61% respectively. 2021 -
Model independent analysis in (?, n) reactions using deuterium targets
Photonuclear reactions play an important role in nuclear physics, astrophysics and in various applications such as non-destructive measurement of nuclear materials (NDT). The study of (?, n) reactions using deuterium targets i.e., photodisintegration of deuterons in addition to all the other (?, n) reactions, is of considerable interest to these fields. In this contribution, we have studied the photodisintegration of deuterons with unpolarized photons. The angular dependence of the differential cross section is studied by expressing it in terms of Legendre polynomials. The analysis of differential cross-section is presented using the model-independent irreducible tensor formalism. 2021 -
Publication stress amongst scholars and faculties: a concern of mental health
Purpose: The purpose of this paper is to explore the impact of the seemingly entrenched culture of publish or perish on academics and lecturers mental health in academia. From an autoethnographic perspective, personal experiences of stress, anxiety and burnout are articulated and considered in terms of broader system issues within academia. Design/methodology/approach: Using personal reflections on publication pressure and combining that with the broader existing literature on mental health in academia, this paper, like the ones mentioned above, has been written with autoethnography as the research mode. Autoethnography is a research method that allows for profoundly exploring personal experiences but frames them in a broader academic context, thereby allowing for a qualitative analysis of academics mental health challenges. Findings: The pressure to publish in high-impact journals puts a person under a level of mental health stress that includes feeling anxious, feeling like an impostor and suffering from burnout. Therefore, this very unfitting competitive environment requires institutional support and strategies to mitigate the stress associated with publication. Originality/value: This paper offers an autoethnographic view of the mental health difficulties in academia, providing a firsthand account of the emotional toll of academic publishing. This paper fleshes out the burgeoning discourse surrounding mental health within higher education by connecting personal experiences with systemic issues, pointing to changes in culture and structure. 2024, Emerald Publishing Limited. -
Mapping of groundwater availability in dry areas of rural and urban regions in India using IOT assisted deep learning classification model
Groundwater is a crucial resource for fulfilling the water requirements of India's rural and urban areas. The heterogeneous nature of geological, hydrological, and climatic factors results in substantial variability in the accessibility of groundwater across disparate regions. The present investigation centers on the cartography of groundwater accessibility in arid zones of rural and urban Indian areas using a Deep Learning Classification Model (DL-GWCM) supported by the Internet of Things (IoT). The introductory section underscores the importance of groundwater in India, where groundwater sources cater to around 80% of rural and 50% of urban water demands. The text highlights statistical data derived from surveys that indicate a notable decrease in groundwater levels. This underscores the pressing necessity for implementing effective monitoring and management strategies. The DL-GWCM is a proposed solution that aims to enhance the precision and effectiveness of groundwater availability mapping by incorporating IoT technology and Deep Learning Classification. The DL-GWCM comprises multiple constituent elements, such as Groundwater Prediction, Water Quality Index, and Conventional Neural Network- Bidirectional Long Short-Term Memory (CNNBi LSTM) classification. The process of Groundwater Prediction involves the utilization of past data and environmental factors to make precise forecasts of groundwater levels. The Water Quality Index evaluates the quality of subsurface water resources, guaranteeing their secure and enduring utilization. The Deep Learning Classification Model with IoT technology was implemented for groundwater accessibility mapping in Indian arid zones. It integrates Groundwater Prediction, Water Quality Index, and CNNBi LSTM classification. The model makes precise forecasts using past data and environmental factors, ensuring secure water quality. Using the CNNBi LSTM classification model improves the precision of groundwater availability mapping due to its resilient classification capabilities. These findings suggest that the DL-GWCM outperforms conventional approaches. The mean values of all five metrics for the proposed method are presented as follows: The performance metrics of the model are as follows: Root Mean Square Error (RMSE) of 0.77%, Mean Absolute Error (MAE) of 2.13%, Relative Absolute Error (RAE) of 8.72%, Root Relative Squared Error (RRSE) of 0.92%, and Correlation Coefficient (CC) of 0.92. The results of the proposed methodology facilitate the discernment of regions with abundant or scarce groundwater accessibility, thereby supporting the sustainable management and planning of groundwater resources. 2024 Elsevier B.V. -
Economic impact of micro loans on the rural women through self help group-bank linkage programme(SBLP) /
Zenith International Journal Of Business Economics And Management Research, Vol.6, Issue 2, pp.98-112, ISSN: 2249-8826. -
Recent advances in electrochemical and optical sensing of the organophosphate chlorpyrifos: A review /
Critical Reviews in Toxicology, Vol.52, Issue 6, ISSN No: 1040-8444.
Chlorpyrifos (CP) is one of the most popular organophosphorus pesticides that is commonly used in agricultural and nonagricultural environments to combat pests. However, several concerns regarding contamination due to the unmitigated use of chlorpyrifos have come up over recent years. This has popularized research on various techniques for chlorpyrifos detection. Since conventional methods do not enable smooth detection, the recent trends of chlorpyrifos detection have shifted toward electrochemical and optical sensing techniques that offer higher sensitivity and selectivity. -
Blending the old with the new through technology- sanskrit and e-learning /
Internation Journal Of English Language, Vol.5(10), pp.57-64. -
Selection of tightened-normal-tightened sampling scheme under the implications of intervened poisson distribution /
Pakistan Journal of Statistics and Operation Research, Vol.15, Issue 1, pp.129-140 -
Harnessing technology for mitigating water woes in the city of Bengaluru /
Journal of Physics: Conference Series, Vol.1427, pp.1-12, ISSN No: 1742-6596. -
Effect of imposed time periodic boundary temperature on the onset of Rayleigh-Benard convection in a dielectric couple stress fluid /
International Journal of Applied Mathematics and Computation, Vol.5, Issue 4, pp.400-412, ISSN No: 0974-4665 (Print), 0974-4673 (Online) -
Linear and weakly non-linear analysis of gravity modulation and electric field on the onset of Rayleigh- Benard convection in a micropolar fluid /
Journal of Advances in Mathematics, Vol.9, Issue 3, pp.363-388, ISSN No: 2347-1921. -
Optimizing milk run and use of bin-packing in waste collection problems /
International Journal of Engineering & Technology, Vol.7, Issue 4.10, pp.577-579 -
Large scale extinction maps with UVIT
Astrophysics and Space Science Vol.343, No.2 ISSN No. 0004-640X -
Contemporary ethnobotany of pastoralism in semi-arid Deccan region-Koppal district, Karnataka, India
The aim of the current study is on ethnobotanical survey carried out in Koppal district of Karnataka, India. Study focused on exploration of traditional knowledge and pastoral practices of particular communities where livestock rearing activity is potent and use of ethnomedicinal plants in daily livelihood. The landscape was diverse in nature covering semi-arid to modest tropical climate, and unique topography encourage for wide range of vegetation useful for caters to feed the livestock. Methodology used for the study by participant observation, interviews, and transect walks, the study identified 48 plant species from 20 families, with Fabaceae and Poaceae being the most prevalent. These families play a vital role in providing nutritious fodder for livestock and sustaining pastoralists livelihoods. The study resulted pastoralists ethno-veterinary knowledge, where specific plants are used to treat cattle ailments and reflecting their deep understanding of traditional herbal remedies. However, the research finding revealed that some traditional knowledge may be diminishing with the younger generation, stressing the importance of knowledge preservation and transfer. The study underscores the potential benefits of integrating traditional practices with scientific research to optimize the selection and utilization of valuable fodder and veterinary plants. It is evident remarks to add up in the scientific data by collaboration between indigenous communities and scientific institutions for sustainable resource management. 2023, Indian journals. All rights reserved.