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A top-down approach for studying the in-silico effect of the novel phytocompound tribulusamide B on the inhibition of Nipah virus transmission through targeting fusion glycoprotein and matrix protein
The proteins of Nipah virus ascribe to its lifecycle and are crucial to infections caused by the virus. In the absence of approved therapeutics, these proteins can be considered as drug targets. This study examined the potential of fifty-three (53) natural compounds to inhibit Nipah virus fusion glycoprotein (NiV F) and matrix protein (NiV M) in silico. The molecular docking experiment, supported by the principal component analysis (PCA), showed that out of all the phytochemicals considered, Tribulusamide B had the highest inhibitory potential against the target proteins NiV F and NiV M (-9.21 and ?8.66 kcal mol?1, respectively), when compared to the control drug, Ribavirin (-7.01 and ?6.52 kcal mol?1, respectively). Furthermore, it was found that Tribulusamide B pharmacophores, namely, hydrogen donors, acceptors, aromatic and hydrophobic groups, contributed towards the effective residual interactions with the target proteins. The molecular dynamic simulation further validated the results of the docking studies and concluded that Tribulusamide B formed a stable complex with the target proteins. The data obtained from MM-PBSA study further explained that the phytochemical could strongly bind with NiV F (-31.26 kJ mol?1) and NiV M (-40.26 kJ mol?1) proteins in comparison with the control drug Ribavirin (-13.12 and ?13.94 kJ mol?1, respectively). Finally, the results indicated that Tribulusamide B, a common inhibitor effective against multiple proteins, can be considered a potential therapeutic entity in treating the Nipah virus infection. 2024 Elsevier Ltd -
Evaluation of therapeutic potentials of selected phytochemicals against Nipah virus, a multi-dimensional in silico study
The current study attempted to evaluate the potential of fifty-three (53) natural compounds as Nipah virus attachment glycoprotein (NiV G) inhibitors through in silico molecular docking study. Pharmacophore alignment of the four(4) selected compounds (Naringin, Mulberrofuran B, Rutin and Quercetin 3-galactoside) through Principal Component Analysis (PCA) revealed that common pharmacophores, namely four H bond acceptors, one H bond donor and two aromatic groups were responsible for the residual interaction with the target protein. Out of these four compounds, Naringin was found to have the highest inhibitory potential ( 9.19kcalmol?1) against the target protein NiV G, when compared to the control drug, Ribavirin ( 6.95kcalmol?1). The molecular dynamic simulation revealed that Naringin could make a stable complex with the target protein in the near-native physiological condition. Finally, MM-PBSA (Molecular Mechanics-PoissonBoltzmann Solvent-Accessible Surface Area) analysis in agreement with our molecular docking result, showed that Naringin ( 218.664kJmol?1) could strongly bind with the target protein NiV G than the control drug Ribavirin ( 83.812kJmol?1). 2023, King Abdulaziz City for Science and Technology. -
Future of knowledge management in investment banking: Role of personal intelligent assistants
Purpose: The studys objective focuses on investigating the involvement of Personal Intelligent Assistants (PIAs) in the Knowledge Management Process (KMP) in Investment Banking Companies leading to Industrial Revolution 5.0 leading to effective Organizational Knowledge Management. Design/Methodology: A Self-administered Survey Questionnaire was circulated to 695 employees of Investment Banking Companies operating in Bangalore, Mumbai, Delhi, Hyderabad, Chennai, and Pune using the Cluster Sampling method. The Covariance-based Structural Equation Modelling (CB-SEM) and Gradient Boosting Regression technique of Machine Learning were used to validate the hypothesis through JASP V.18 Software. Knowledge Creation, Knowledge Sharing, Knowledge Retrieval, Knowledge Application, and Organizational Knowledge Management are the crucial constructs considered in the study. Findings: The results revealed that Knowledge Application is the most influencing factor in effective organizational Knowledge management among the Investment Banks followed by Knowledge Sharing. It also emphasizes that they have a weak Knowledge retrieval process and minimal efforts taken to create knowledge within these banks. Implications: The PIAs can facilitate effective Data Analysis and research in managing vast data eliminating the repeated tasks in portfolio reconciliation and offering personalized recommendations to manage portfolios. It enables in compliance, risk management, client relationship management, real-time monitoring and leveraged decision-making through predictive analysis. The Author(s) 2024. -
Religion, society and state in India: A legal perspective
[No abstract available] -
Exploring the role of plant oils in aquaculture practices: an overview
As the global demand for seafood surges, the expanding aquaculture industry faces a pressing need for viable aquafeed ingredients. The raw material for fish oil is limited and expensive due to unpredictable fishery resources in the fishing zones and the overexploitation of wild fisheries, underscoring the urgency of finding alternatives. This review explores diverse plant oil sources, including soybean, rapeseed, linseed, and algal oils, emphasizing their crucial role in nutritionally balanced aquafeeds. These oils support aquatic animals growth, health, and development, influencing membrane structure, energy storage, and hormone production. Genetically modified oilseeds (GM), such as camelina and canola, offer a controlled nutrient content, enabling customized nutrient profiles. This comprehensive review provides an overview of different plant oil sources, elucidates their nutrient profiles, and assesses their potential applications in aquaculture. The discussion encompasses their impact on growth, feed efficiency, lipid profile, health, immunological status, disease resistance, and overall performance of both freshwater and marine fish. Furthermore, the review compiles relevant data on the current status of genetically modified plant oils and explores their potential integration into aquaculture practices. In summary, substituting plant oils for fish oil in aquafeed presents a promising solution to aquaculture industry challenges to meet nutritional requirements for fish. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024. -
Solar radiative heat-driven Sakiadis flow of a dusty nanoliquid with Brownian motion and an exponential space-based heat source: KooKleinstreuerLi (KKL) model
The advancement of heat transportation is a significant phenomenon in nuclear reactors, solar collectors, heat exchangers, and electronic coolers; and it can be accomplished by choosing ananofluid as the functional fluid. Nanofluids haveimproved thermophysical properties, dueto theirgreat progress in engineering and industrial applications. Therefore here, the significance of exponential space-related heat source (ESHS) on radiative heat motivated Sakiadis two-phase flow over a moving plate is analyzed for a particulate nanoliquid (CuOH2O). The impact of the haphazard motion of nanoparticles is analyzed through the KooKleinstreuerLi model. On applying a similarity transformation to the governing equations, a set of ordinary differential equations is obtained and numerically solved. Through the perception of graphs, the behavior of the velocity and temperature constraints for diverse values of effective parameters is decoded. The results showthat the temperature of both phases (dust and fluid) improves with the ESHS aspect. Also, the heat transport rate/friction factor enhances/declines with the concentration of dust particles. 2020 Wiley Periodicals LLC -
Soft Computing Approaches for Maximum Power Point Tracking of Solar PV System
Solar power changes according to irradiance and temperature in a day. A Maximum Power Point Tracking (MPPT) algorithm is actually necessary to obtain the maximum power from the photovoltaic (PV) arrangement. In this paper, in order to optimize power and improve the efficiency of PV module with regulated output voltage, soft computing MPPT techniques, flying squirrel search optimization and artificial bee colony methods are implemented on cascaded double voltage lift boost converter. The PV module is subjected to both with and without constraints to analyze the performance of the DC/DC converter, and the comparative outcomes are evaluated for resistive and different types of battery loads at various temperature conditions in MATLAB/Simulink platform. The optimized power is achieved by using artificial bee colony technique with less ripple in the output waveforms at constant 25 C temperature irrespective of the changes in irradiation with the battery load and this can be used for charging of the battery system. 2023 Praise Worthy Prize S.r.l.-All rights reserved. -
Enhancing rainwater harvesting and groundwater recharge efficiency with multi-dimensional LSTM and clonal selection algorithm
Rainwater harvesting stands out as a promising solution to alleviate water scarcity and alleviate pressure on conventional water reservoirs. This work introduces a pioneering strategy to elevate the efficiency of rainwater harvesting systems through the fusion of Multi-Dimensional Long Short-Term Memory (LSTM) networks and the Clonal Selection Algorithm (CSA). The Multi-Dimensional LSTM networks serve to model intricate temporal and spatial rainfall patterns, enabling precise predictions regarding the optimal times and locations for rainwater abundance. This insight is pivotal in refining the design and operation of rainwater harvesting setups. Drawing inspiration from the immune system, the Clonal Selection Algorithm is employed to optimize site selection and resource allocation, ensuring the maximal utilization of harvested rainwater. The adaptability and robustness of CSA prove invaluable in tackling the dynamic nature of rainfall patterns. This research endeavor is dedicated to enhancing groundwater levels and optimizing its sources through the implementation of efficient harvesting techniques. By delving into innovative methodologies, it aims to contribute significantly to sustainable water management practices and ensure a reliable supply of groundwater for various societal needs. The experiments are conducted to study the effectiveness of rainwater harvesting systems, where the proposed method achieves increased efficiency, thereby reducing dependence on conventional water sources and contributing to sustainable water management practices. The proposed CSA-LSTM model demonstrates superior performance compared to ACO-ANN and PSO-BPNN, achieving higher training, testing, and validation accuracies while exhibiting lower training, testing, and validation losses. Additionally, CSA-LSTM showcases excellent site suitability, high resource utilization, and robustness to changes, with a fast response time, emphasizing its potential for efficient and effective applications. 2024 Elsevier B.V. -
Implementation challenges of Total Quality Management (TQM) in dairy sector /
Smart Journal of Business Management Studies, Vol.15, Issue 1, pp.1-9, ISSN No: 2321-2012. -
Riding the waves of culture: An empirical study on acclimatization of expatriates in IT industry /
Problems And Perspectives In Management, Vol.16, Issue 3, pp.432-442 -
Problems and perspectives in inventory management of fruits and vegetables at HOPCOMS, Bangalore
Increase in demand for Fruit and Vegetables has augmented over the years. Being perishable they are restricted to a limited life span. The time dependency on perishable commodities acts as a barrier to retain the freshness and quality in fruits and vegetables for a longer period. Therefore, Inventory management is vital to manage perishable commodities as it brings in transparency to the actual demand from the customers. The retailer being the key element in the supply chain to come in contact with the customer should follow up with techniques to manage overstocking and stock out situation. The present study focuses on bringing in inventory management in HOPCOMS, a cooperative society in Karnataka. In the road to satisfy the customers, it is necessary for the society to come up with different strategies to manage the inventories. Different inventory evaluation methods are studied in relation to perishable commodities and the factors affecting the same. FIFO (first in first out) an inventory evaluation method was found to be more efficient and must be considered practically by the retailers to manage inventories during the sales. However, with efficient infrastructural facilities, interference of state government to bring in cold storage facilities and, creating awareness regarding the actual demand for a commodity in the market, the retailer would be able to balance overstocking and stock out situation in the future. 2019, Institute of Advanced Scientific Research, Inc. All rights reserved. -
Study of 1s internal bremsstrahlung spectrum from 57Co
The internal bremsstrahlung contribution from the electron capture of 57Co has been measured in coincidence with K-X-ray of the residual atom. The end-point energy (EPE) is extracted from the data using the linearised Jauch plot. The transition energy obtained using the (EPE) is 842.7keV, which is close to the value given by Audi and Wapstra. The measured intensity and shape factor from 300 to 600keV are found to be in good agreement with the Glauber and Martin theory. 2002 Elsevier Science Ltd. All rights reserved. -
Optimization of anti-corrosion performance of novel magnetic polyaniline-Chitosan nanocomposite decorated with silver nanoparticles on Al in simulated acidizing environment using RSM
The suitability of newly synthesized magnetic polyaniline-Chitosan nanocomposite decorated with silver nanoparticles (Ag@PANI-CS-Fe3O4) as a robust corrosion inhibitor for Aluminum (Al) in a 5 M HCl environment has been investigated via Weight Loss (WL), Alternating Current (AC)-Impedance Spectroscopy (IS), Potentiontiodynamic polarization (Tafel plots), and Scanning Electron Microscopy (SEM) techniques. The protection efficiency (PE) was mathematically modeled using the Response Surface Methodology (RSM) to fit an empirical relation in terms of temperature, nanocomposite concentration, and time using the face-centered central composite design. The model was accurate with a coefficient of determination (R2 = 99.27%). The negative Gibb's free energy of adsorption (?Gads) values confirmed the spontaneity of Freundlich adsorption isotherm process on Al in 5 M HCl solution. The optimization simulation yielded maximum protection efficiency (of 97.88%) at 5 mg/L nanocomposite concentration, 1 h time, and an intermediate temperature of 304.8 K. Furthermore, the sensitivity of PE was evaluated to find that the low temperature 303 K is favorable for PE, whereas higher temperature will act adversely on PE. The results obtained by the RSM model are in agreement with the experimental observations. 2021 Elsevier B.V. -
Performance evaluation of random forest with feature selection methods in prediction of diabetes
Data mining is nothing but the process of viewing data in different angle and compiling it into appropriate information. Recent improvements in the area of data mining and machine learning have empowered the research in biomedical field to improve the condition of general health care. Since the wrong classification may lead to poor prediction, there is a need to perform the better classification which further improves the prediction rate of the medical datasets. When medical data mining is applied on the medical datasets the important and difficult challenges are the classification and prediction. In this proposed work we evaluate the PIMA Indian Diabtes data set of UCI repository using machine learning algorithm like Random Forest along with feature selection methods such as forward selection and backward elimination based on entropy evaluation method using percentage split as test option. The experiment was conducted using R studio platform and we achieved classification accuracy of 84.1%. From results we can say that Random Forest predicts diabetes better than other techniques with less number of attributes so that one can avoid least important test for identifying diabetes. Copyright 2020 Institute of Advanced Engineering and Science. All rights reserved. -
Performance evaluation of machinelearning techniques indiabetes prediction
Diabetes diagnosis is very important at preliminary stage rather than treatment. In todays world devices like sensors are used for detection of diabetes. Accurate classification techniques are required for automatic identification of diabetes disease. In regards to research diabetes prediction with minimal number of attributes (test parameters) is to be identified earlier research states about feature reduction but with less predictive accuracy. In this regards, this work exploits machine learning techniques(methodology) such as Logistic Regression (LR), Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF) and Neural Network (NN) with 10-fold Cross Validation (CV) for classification and prediction of diabetes with Feature Selection Methods (FSMs) using R platform. Above all models enable us to investigate the relationship between a categorical outcome and a set of explanatory variables. The experiment was conducted on PIMA Indian diabetes dataset selected from UCI machine learning repository. From the experimental results it is identified that for full set of diabetes dataset attributes, Classification Accuracy (CA) achieved was 84.25%whereas with reduced set attributes an accuracy of 85.24% is achieved using NN with 10-fold CV technique compared to others which will help in medical application to predict diabetes with minimal features. BEIESP. -
Total syntheses of Prelactone V and Prelactone B
The total syntheses of natural products Prelactone-V and Prelactone-B have been accomplished by a novel Chiron approach starting from D-glucose. The synthesis involves isopropylidene acetal formation of D-glucose using Poly(4-vinylpyridine) supported iodine as a catalyst, Tebbe olefination, Grignard reaction, Wittig olefination, selective mono deprotection of acetal using PMA/SiO2, hydrogenation and anti-1,3-diol formation are as key steps. 2017 -
Analysing the market for digital payments in India using the predator-prey model
Technology has revolutionized the way transactions are carried out in economies across the world. India too has witnessed the introduction of numerous modes of electronic payment in the past couple of decades, including e-banking services, National Electronic Fund Transfer (NEFT), Real Time Gross Settlement (RTGS) and most recently the Unified Payments Interface (UPI). While other payment mechanisms have witnessed a gradual and consistent increase in the volume of transactions, UPI has witnessed an exponential increase in usage and is almost on par with pre-existing technologies in the volume of transactions. This study aims to employ a modified Lotka-Volterra (LV) equations (also known as the Predator-Prey Model) to study the competition among different payment mechanisms. The market share of each platform is estimated using the LV equations and combined with the estimates of the total market size obtained using the Auto-Regressive Integrated Moving Average (ARIMA) technique. The result of the model predicts that UPI will eventually overtake the conventional digital payment mechanism in terms of market share as well as volume. Thus, the model indicates a scenario where both payment mechanisms would coexist with UPI being the dominant (or more preferred) mode of payment. 2023 Balikesir University. All rights reserved. -
Augmented reality for history education
Augmented Reality is live, direct or indirect view of a physical real world environment whose elements are augmented by personal computers (PC) that produces the information such as sound, video, designs or GPS data. This paper shows an instructive mobi le application based system model on Augmented Reality which is used to learn subjects like history through augmented videos. The objective of development of this system model is to make the learning interesting for the young generation. Unity 3D and Vufo ria Augmented Reality Software Development Kit (SDK) is used for the development of this model. The prime purpose of this application model is to enhance the learning process with digital technologies. This paper has step by step implementation instructions for the development of augmented reality modeling that can supplement the current teaching-learning environment to generate interest among young generation in less interesting subjects such as History, Geography, etc. 2018 Authors. -
Phytochemical Analysis and Antibacterial Potential of Stevia rebaudiana (Bertoni, 1899) Leaf Extracts against Aeromonas Species: Influence of Extraction Methods and Solvents in Aquaculture Applications
Recent studies have explored Stevia rebaudiana Bertoni leaf extracts for their antibacterial potential and phytochemical content. However, the impact of extraction methods and solvents on aquaculture bacteria remains understudied. This research aimed to evaluate the antibacterial, radical scavenging, and phytochemical properties of S. rebaudiana extracts against Aeromonas species. Dried S. rebaudiana leaves were extracted using methanol (Mt) and ethanol (Et) through Soxhlet and maceration methods (SMt, SEt, MMt and MEt respectively). Soxhlet extraction yielded higher amounts (36.29% for Mt, 23.87% for Et) compared to maceration. Phytochemical analysis identified phenolics, flavonoids, alkaloids, saponin, tannin, and steroids in all extracts. Notably, MEt had elevated phenolic and flavonoid content, while SEt contained more tannins. MEt exhibited the strongest antioxidant activity (IC50 = 67.95g/mL), aligning with its high phenolic and flavonoid levels. In antibacterial assays against Aeromonas strains, ethanol extract showed the largest zone of inhibition (ZOI) of 16.67mm for A. salmonicida, followed by methanol extract (15mm) at 250 mg/mL, using maceration and Soxhlet methods, respectively. However, none of the extracts displayed activity against A. hydrophila. This suggests that cold maceration is a cost-effective method that preserves heat-sensitive secondary metabolites within a shorter extraction time. In conclusion, this study highlights the significance of extraction techniques and solvents in obtaining potent antibacterial and antioxidant extracts from S. rebaudiana leaves. The findings emphasize the potential of these extracts in aquaculture practices and open avenues for further research in utilizing natural compounds for sustainable aquaculture strategies. The Author(s) 2023. -
Classification of epileptic seizures using wavelet packet log energy and norm entropies with recurrent Elman neural network classifier
Electroencephalogram shortly termed as EEG is considered as the fundamental segment for the assessment of the neural activities in the brain. In cognitive neuroscience domain, EEG-based assessment method is found to be superior due to its non-invasive ability to detect deep brain structure while exhibiting superior spatial resolutions. Especially for studying the neurodynamic behavior of epileptic seizures, EEG recordings reflect the neuronal activity of the brain and thus provide required clinical diagnostic information for the neurologist. This specific proposed study makes use of wavelet packet based log and norm entropies with a recurrent Elman neural network (REN) for the automated detection of epileptic seizures. Three conditions, normal, pre-ictal and epileptic EEG recordings were considered for the proposed study. An adaptive Weiner filter was initially applied to remove the power line noise of 50Hz from raw EEG recordings. Raw EEGs were segmented into 1s patterns to ensure stationarity of the signal. Then wavelet packet using Haar wavelet with a five level decomposition was introduced and two entropies, log and norm were estimated and were applied to REN classifier to perform binary classification. The non-linear Wilcoxon statistical test was applied to observe the variation in the features under these conditions. The effect of log energy entropy (without wavelets) was also studied. It was found from the simulation results that the wavelet packet log entropy with REN classifier yielded a classification accuracy of 99.70% for normal-pre-ictal, 99.70% for normal-epileptic and 99.85% for pre-ictal-epileptic. 2016, Springer Science+Business Media Dordrecht.