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Binding interaction of phosphorus heterocycles with bovine serum albumin:A biochemical study

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Journal of Pharmaceutical Analysis 2017年第1期7卷 19-26页

作者： Swarup Roy Raj k.mar nandi Sintu Ganai k.C. Majumdar Tapan k. Das Department of Biochemistry and Biophysics University of Kalyani Kalyani 741235 West Bengal India Department of Chemistry University of Kalyani Kalyani 741235 West Bengal India

Interaction between bovine serum albumin(BSA) and phosphorus heterocycles(PHs) was studied using multispectroscopic techniques. The results indicated the high binding affinity of PHs to BSA as it quenches the intrinsic fluorescence of BSA. The experimental data suggested the fluorescence quenching mechanism between PHs and BSA as a dynamic quenching. From the UV–vis studies, the apparent association constant(k.(app)) was found to be 9.25×10~2, 1.27×10~4and 9.01×10~2L/mol for the interaction of BSA with PH-1, PH-2 and PH-3,respectively. According to the F?rster's non-radiation energy transfer(FRET) theory, the binding distances between BSA and PHs were calculated. The binding distances(r) of PH-1, PH-2 and PH-3 were found to be2.86, 3.03, and 5.12 nm, respectively, indicating energy transfer occurs between BSA and PHs. The binding constants of the PHs obtained from the fluorescence quenching data were found to be decreased with increase of temperature. The negative values of the thermodynamic parameters ΔH, ΔS and ΔG at different temperatures revealed that the binding process is spontaneous; hydrogen bonds and van der Waals interaction were the main force to stabilize the complex. The microenvironment of the protein-binding site was studied by synchronous fluorescence and circular dichroism(CD) techniques and data indicated that the conformation of BSA changed in the presence of PHs. Finally, we studied the BSA-PHs dock.ng using Auto Dock.and results suggest that PHs is located in the cleft between the domains of BSA.

关键词： BSA Spectroscopy Phosphorus heterocycles BSA-PHs dock.ng

Impact of school reopening on pandemic spread: A case study using an agent-based model for COVID-19

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Infectious Disease Modelling 2021年第1期6卷 839-847页

作者： Hanisha Tatapudi tapas k.Das Department of Industrial and Management System Engineering University of South FloridaTampaFLUSA

This article examines the impact of partial/full reopening of school/college campuses on the spread of a pandemic using COVID-19 as a case study.The study uses an agent-based simulation model that replicates community spread in an urban region of U.S.A.via daily social mixing of susceptible and infected individuals.Data representing population demographics,SARS-CoV-2 epidemiology,and social interventions guides the model's behavior,which is calibrated and validated using data reported by the government.The model indicates a modest but significant increase(8.15%)in the total number of reported cases in the region for a complete(100%)reopening compared to k.eping schools and colleges fully virtual.For partial returns of 75%and 50%,the percent increases in the number of reported cases are shown to be small(2.87%and 1.26%,respectively)and statistically insignificant.The AB model also predicts that relaxing the stringency of the school safety protocol for sanitizing,use of mask.social distancing,testing,and quarantining and thus allowing the school transmission coefficient to double may result in a small increase in the number of reported infected cases(2.14%).Hence for pandemic outbreak. from viruses with similar characteristics as for SARS-CoV-2,k.eping the schools and colleges open with a modest campus safety protocol and in-person attendance below a certain threshold may be advisable.

关键词： COVID-19 SARS-CoV-2 Agent-based simulation model School reopening School transmission rate Age-specific impact

Connected Components-based Colour Image Representations of Vibrations for a Two-stage Fault Diagnosis of Roller Bearings Using Convolutional Neural Network.

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Chinese Journal of Mechanical Engineering 2021年第3期34卷 73-93页

作者： Hosameldin O.A.Ahmed Asok. k.nandi Department of Electronic and Electrical Engineering Brunel University LondonUxbridge UB83PHUK School of Mechanical Engineering Xi’an Jiaotong UniversityXi’anChina

Roller bearing failure is one of the most common faults in rotating machines.Various techniques for bearing fault diagnosis based on faults feature extraction have been proposed.But feature extraction from fault signals requires expert prior information and human labour.Recently,deep learning algorithms have been applied extensively in the condition monitoring of rotating machines to learn features automatically from the input data.Given its robust performance in image recognition,the convolutional neural network.CNN)architecture has been widely used to learn automatically discriminative features from vibration images and classify health conditions.This paper proposes and evaluates a two-stage method RGBVI-CNN for roller bearings fault diagnosis.The first stage in the proposed method is to generate the RGB vibration images(RGBVIs)from the input vibration signals.To begin this process,first,the 1-D vibration signals were converted to 2-D grayscale vibration Images.Once the conversion was completed,the regions of interest(ROI)were found in the converted 2-D grayscale vibration images.Finally,to produce vibration images with more discriminative characteristics,an algorithm was applied to the 2-D grayscale vibration images to produce connected components-based RGB vibration images(RGBVIs)with sets of colours and texture features.In the second stage,with these RGBVIs a CNN-based architecture was employed to learn automatically features from the RGBVIs and to classify bearing health conditions.Two cases of fault classification of rolling element bearings are used to validate the proposed method.Experimental results of this investigation demonstrate that RGBVI-CNN can generate advantageous health condition features from bearing vibration signals and classify the health conditions under different work.ng loads with high accuracy.Moreover,several classification models trained using RGBVI-CNN offered high performance in the testing results of the overall classification accuracy,precision,recall,and F-score.

关键词： Bearing fault diagnosis Image representation of vibrations Deep learning Convolutional neural network.

Analysis of a fuzzy epidemic model with saturated treatment and disease transmission

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International Journal of Biomathematics 2018年第1期11卷 23-40页

作者： Swapan k.mar nandi Soovoojeet Jana Manotosh Manadal T. k. k.r Department of MathematicsNayabasat P. M. Sik.hanik.tan Paschim Medinipur 721253 West Bengal India Department of Mathematics Ramsaday College Amta 711501 West Bengal India Department of Mathematics Tamralipta MahavidyalayaTamluk.721636 West Bengal India Department of MathematicsIndian Institute of Engineering Science and Technology Shibpur Howrah 711103 West Bengal India

In this paper, we describe an SIS epidemic model where both the disease transmission rate and treatment function are considered in saturated forms. The dynamical behavior of the system is analyzed. The system is customized by considering the disease transmission rate and treatment control as fuzzy numbers and then fuzzy expected value of the infected individuals is determined. The fuzzy basic reproduction number is investigated and a threshold condition of pathogen is derived at which the system undergoes a back.ard bifurcation.

关键词：疾病传播流行模型模糊期望浸透在系统动态行为模糊数字 SIS

Discharge water temperature assessment of thermal power plantusing remote sensing techniques

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Energy Geoscience

Energy Geoscience 2022年第2期3卷 172-181页

作者： Priyom Roy Ivaturi N.Rao tapas Ranjan Martha k.Vinod k.mar Geosciences Group National Remote Sensing CentreISROHyderabadIndia Environment&Sustainability Coastal Gujarat Power LimitedMundraGujaratInd

Thermal power plants are generally constructed near to sea coast to meet their requirement of coolingwater. The warm water discharge from the thermal power plant is one of the major environmentalconcerns in view of the thermal pollution in the sea water. The temperature limit for the warm waterdischarge from the thermal power plant has to be monitored and controlled. Coastal Gujarat PowerLimited (CGPL) operates (24×7) at an “once-through system” based sea water circulation for powergeneration. The used sea water is then discharged into the sea through an outlet channel. As per environmental norms, the discharge water temperature needs to be maintained below the stipulated “delta”rise (+7 ℃) with respect to ambient sea surface temperature at the inlet. We demonstrate the applicability of thermal remote sensing data in understanding the seasonal and temporal variations of thetemperature difference between the discharge water and the ambient sea water. We used thermal banddata from Landsat-8 satellite imagery to map water surface temperature and create temperature profilesalong the intak. and outflow channels (till the sea), to understand the variation of temperature andestimate the “DT” between intak. point and various observation points along the outflow. This analysiswas carried out for all 11 months (except June) of the year 2018 to correlate temperature variations withseasonal changes. Tidal conditions during the time of data acquisition were also considered to accountfor the effect of tides on DT. The result shows that the average temperature rise between intak. andoutflow are maintained at ~3 ℃ across all the months of 2018, with minor variations in the months ofJuly and August. Further, average temperature drop from outflow to cooling channel (before diaphragm)is seen to be ~2 ℃ across all the months with similar seasonal fluctuations.

关键词： Discharge water Thermal power plant Ambient temperature Landsat 8 Seawater pollution

Scale‐wise interaction fusion and k.owledge distillation network.for aerial scene recognition

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1178-1190页

作者： Hailong Ning Tao Lei Mengyuan An Hao Sun Zhanxuan Hu Asok. k.nandi School of Computer Science and Technology Xi'an University of Posts and TelecommunicationsShaanxi Key Laboratory of Network Data Analysis and Intelligent ProcessingXi'anChina Xi'an k.y Laboratory of Big Data and Intelligent Computing Xi'anChina School of Electronic Information and Artificial Intelligence Shaanxi University of Science and TechnologyXi'anChina School of Computer Central China Normal UniversityWuhanChina Department of Electronic and Electrical Engineering Brunel University LondonLondonUK Xi'an Jiaotong University Xi'anChina

Aerial scene recognition(ASR)has attracted great attention due to its increasingly essential applications.Most of the ASR methods adopt the multi‐scale architecture because both global and local features play great roles in ASR.However,the existing multi‐scale methods neglect the effective interactions among different scales and various spatial locations when fusing global and local features,leading to a limited ability to deal with challenges of large‐scale variation and complex back.round in aerial scene images.In addition,existing methods may suffer from poor generalisations due to millions of to‐belearnt parameters and inconsistent predictions between global and local features.To tack.e these problems,this study proposes a scale‐wise interaction fusion and k.owledge distillation(SIF‐k.)network.for learning robust and discriminative features with scaleinvariance and back.round‐independent information.The main highlights of this study include two aspects.On the one hand,a global‐local features collaborative learning scheme is devised for extracting scale‐invariance features so as to tack.e the large‐scale variation problem in aerial scene images.Specifically,a plug‐and‐play multi‐scale context attention fusion module is proposed for collaboratively fusing the context information between global and local features.On the other hand,a scale‐wise k.owledge distillation scheme is proposed to produce more consistent predictions by distilling the predictive distribution between different scales during training.Comprehensive experimental results show the proposed SIF‐k. network.achieves the best overall accuracy with 99.68%,98.74%and 95.47%on the UCM,AID and NWPU‐RESISC45 datasets,respectively,compared with state of the arts.

关键词： deep learning image analysis image classification information fusion

Effect of pyroxenite and olivine minerals as source of MgO in hematite pellet on improvement of metallurgical properties

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Journal of Central South University 2015年第9期22卷 3302-3310页

作者： Jagannath Pal Satadal Ghorai Bik.sh nandi tapas Chak.aborty Goutam Das T.Venugopalan CSIR-National Metallurgical Laboratory Tata Steel

Pelletization of hematite ore requires high fineness and very high induration temperature(~1325 °C) owing to its poor diffusion bonding unlik. magnetite ore. Further, high-alumina hematite pellets show very high reduction degradation index(RDI) during low temperature(500-650 °C) reduction due to their volume expansion and lattice distortion. Noamundi(India) hematite ore contains very high Al2O3(2.3%) with adverse ratio of alumina to silica(~2) for which, it shows very high RDI. In this work. the acid pellets prepared from Noamundi ore fines of optimum Blaine fineness show good cold crushing strength(CCS). However, it shows very high RDI(77%). In order to reduce RDI, Mg O in form of two different gangue-containing fluxes, such as pyroxenite and olivine in varying quantities has been added. The optimum requirement and performance of these fluxes has been examined and compared. Both pyroxenite and olivine fluxed pellets show significant lowering of RDI(26% and 23%, respectively) and improvement of other properties, viz CCS, swelling indices etc with good reducibility(70%-77%). Finally, a good quality acidic hematite pellet was developed from high-alumina ore without using any lime which is very important charge material in combination of basic sinter in blast furnace.

关键词：铁矿球团冶金性能橄榄石辉石岩 MgO 酸性球团矿矿物 RDI

Uncertainty-Aware Deep Learning: A Promising Tool for Trustworthy Fault Diagnosis

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IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1317-1330页

作者： Jiaxin Ren Jingcheng Wen Zhibin Zhao Ruqiang Yan Xuefeng Chen Asok. k.nandi School of Mechanical Engineering Xi’an Jiaotong UniversityXi’an 710049China Department of Electronic and Electrical Engineering Brunel University LondonKingston LaneUxbridgeUB83PHUK IEEE

Recently,intelligent fault diagnosis based on deep learning has been extensively investigated,exhibiting state-of-the-art performance.However,the deep learning model is often not truly trusted by users due to the lack.of interpretability of“black.box”,which limits its deployment in safety-critical applications.A trusted fault diagnosis system requires that the faults can be accurately diagnosed in most cases,and the human in the deci-sion-mak.ng loop can be found to deal with the abnormal situa-tion when the models fail.In this paper,we explore a simplified method for quantifying both aleatoric and epistemic uncertainty in deterministic network.,called SAEU.In SAEU,Multivariate Gaussian distribution is employed in the deep architecture to compensate for the shortcomings of complexity and applicability of Bayesian neural network..Based on the SAEU,we propose a unified uncertainty-aware deep learning framework.UU-DLF)to realize the grand vision of trustworthy fault diagnosis.Moreover,our UU-DLF effectively embodies the idea of“humans in the loop”,which not only allows for manual intervention in abnor-mal situations of diagnostic models,but also mak.s correspond-ing improvements on existing models based on traceability analy-sis.Finally,two experiments conducted on the gearbox and aero-engine bevel gears are used to demonstrate the effectiveness of UU-DLF and explore the effective reasons behind.

关键词： Out-of-distribution detection traceability analysis trustworthy fault diagnosis uncertainty quantification.

Analysis of a fractional-order SIS epidemic model with saturated treatment

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International Journal of Modeling, Simulation, and Scientific Computing 2021年第1期12卷 185-212页

作者： Soovoojeet Jana Manotosh Mandal Swapan k.mar nandi T.k.k.r Department of Mathematics Ramsaday College Amta-711401HowrahWest BengalIndia Department of Mathematics Tamralipta Mahavidyalaya Tamluk-721636West BengalIndia Department of Mathematics Nayabasat P.M.Sikshaniketan Paschim Medinipur-721253West BengalIndia Department of Mathematics Indian Institute of Engineering Science and Technology Shibpur Howrah-711103West BengalIndia

In this paper,we propose and analyze a fractional-order SIS epidemic model with the saturated treatment and disease transmission.The existence and uniqueness,nonnegativity and finiteness of solutions for our suggested model have been studied.Different equilibria of the model are found and their local and global stability analyses are also examined.Furthermore,the conditions for fractional back.ard and fractional Hopf bifurcation are also analyzed in both the commensurate and incommensurate fractional-order model.We study how the control parameter and the order of the fractional derivative play role in local as well as global stability of equilibrium points and Hopf bifurcation.We have demonstrated the analytical results of our proposed model system through several numerical simulations.

关键词： Caputo fractional differentiation SIS epidemic model global asymptotic stability treatment control fractional Hopf bifurcation fractional back.ard bifurcation