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Species richness,phylogenetic diversity and phylogenetic structure patterns of exotic and native plants along an elevational gradient in the Himalaya

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Ecological Processes 2021年第1期10卷 877-889页

作者： kumar manish Department of Environmental Studies Dr.Bhim Rao Ambedkar CollegeUniversity of DelhiDelhiIndia

Background:So far,macroecological studies in the Himalaya have mostly concentrated on spatial variation of overall species richness along the elevational *** few studies have attempted to document the diference in elevational richness patterns of native and exotic *** this study,this knowledge gap is addressed by integrating data on phylogeny and elevational distribution of species to identify the variation in species richness,phylogenetic diversity and phylogenetic structure of exotic and native plant species along an elevational gradient in the ***:Species distribution patterns for exotic and native species difered;exotics tended to show maximum species richness at low elevations while natives tended to predominate at *** species assemblages showed higher phylogenetic diversity than the exotic species assemblages over the entire elevational gradient in the *** terms of phylogenetic structure,exotic species assemblages showed majorly phylogenetic clustering while native species assemblages were characterized by phylogenetic overdispersion over the entire ***:The fndings of this study indicate that areas with high native species richness and phylogenetic diversity are less receptive to exotic species and vice versa in the *** assemblages with high native phylogenetic overdispersion are less receptive to exotic species than the phylogenetically clustered *** ecological processes(ecological fltering in case of exotics and resource and niche competition in case of natives)may govern the distribution of exotic and native species along the elevational gradient in the Himalaya.

关键词： Exotic species Native species Himalaya Elevational gradient Richness pattern

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A Novel Approach for Enhancement of Geometric and Contrast Resolution Properties of Low Contrast Images

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IEEE/CAA Journal of Automatica Sinica 2018年第2期5卷 628-638页

作者： Koushlendra kumar Singh manish kumar Bajpai Rajesh kumar Pandey Department of Computer Science and Engineering National Institute of Technology Jamshedpur 831014 India Indian Institute of Information Technology Design & Manufacturing Jabalpur MP 482005 India Department of Mathematical Sciences In- dian Institute of Technology Varanasi U.P. 221005 India

The present work encompasses a new image enhancement algorithm using newly constructed Chebyshev fractional order differentiator. We have used Chebyshev polynomials to design Chebyshev fractional order differentiator. We have generated the high pass filter corresponding to it. The designed filters are applied for decomposing the input image into four bands and low-low(L-L) sub-band is updated using correction coefficients. Reconstructed image with updated L-L sub-band provides the enhanced image. The visual results obtained are encouraging for image enhancement. The applicability of the developed algorithm is illustrated on three different test *** effects of order of differentiation on the edges of images have also been presented and discussed.

关键词： Index Terms-Chebyshev polynomial based approximation contrast enhancement fractional order differentiator.

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Machine Learning-based Electric Load Forecasting for Peak Demand Control in Smart Grid

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Computers, Materials & Continua 2023年第3期74卷 4785-4799页

作者： manish kumar Nitai Pal Department of Electrical Engineering Indian Institute of Technology(ISM)Dhanbad826004India Department of Nuclear Science&Technology Pandit Deendayal Energy UniversityGandhinagarIndia

Increasing energy demands due to factors such as population,globalization,and industrialization has led to increased challenges for existing energy *** ways of energy generation and energy consumption like smart grids and smart homes are implemented to face these challenges with reliable,cheap,and easily available sources of *** integration of renewable energy and other clean distributed generation is increasing continuously to reduce carbon and other air pollutants *** the integration of distributed energy sources and increase in electric demand enhance instability in the ***-term electrical load forecasting reduces the grid fluctuation and enhances the robustness and power quality of the *** load forecasting in advance on the basic historical data modelling plays a crucial role in peak electrical demand control,reinforcement of the grid demand,and generation balancing with cost *** accurate forecasting of electrical data is a very challenging task due to the nonstationary and nonlinearly nature of the *** learning and artificial intelligence have recognized more accurate and reliable load forecastingmethods based on historical load *** purpose of this study is to model the electrical load of Jajpur,Orissa Grid for forecasting of load using regression type machine learning algorithms Gaussian process regression(GPR).The historical electrical data and whether data of Jajpur is taken for modelling and simulation and the data is decided in such a way that the model will be considered to learn the connection among past,current,and future dependent variables,factors,and the relationship among *** on this modelling of data the network will be able to forecast the peak load of the electric grid one day *** study is very helpful in grid stability and peak load control management.

关键词： Artificial intelligence electric load forecasting machine learning peak-load control renewable energy smart grids

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Emergence of blockchain-technology application in peer-to-peer electrical-energy trading: a review

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Clean Energy 2021年第1期5卷 104-123页

作者： manish kumar Thukral Manipal University Jaipur Electrical Engineering DepartmentJaipurIndia

Renewable-energy resources require overwhelming adoption by the common masses for safeguarding the environment from *** this context,the prosumer is an important emerging concept.A prosumer in simple terms is the one who consumes as well as produces electricity and sells it either to the grid or to a *** the present scenario,peer-to-peer(P2P)energy trading is gaining momentum as a new vista of research that is viewed as a possible way for prosumers to sell energy to *** P2P energy trading is the only method of making renewable-energy sources popular among the common *** making P2P energy trading successful,blockchain technology is sparking considerable interest among *** with smart contracts,a blockchain provides secure tamper-proof records of transactions that are recorded in distributed ledgers that are *** paper explores,using a thorough review of recently published research work,how the existing power sector is reshaping in the direction of P2P energy trading with the application of blockchain *** challenges that are being faced by researchers in the implementation of blockchain technology in the energy sector are ***,this paper presents different start-ups that have emerged in the energy-sector domain that are using blockchain *** give insight into the application of blockchain technology in the energy sector,a case of the application of blockchain technology in P2P trading in electrical-vehicle charging is *** the end,some possible areas of research in the application of blockchain technology in the energy sector are discussed.

关键词： blockchain consensus algorithms microgrid peer-to-peer energy trading distributed energy resources electric-vehicle charging

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A Clinical Data Analysis Based Diagnostic Systems for Heart Disease Prediction Using Ensemble Method

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Big Data Mining and Analytics 2023年第4期6卷 513-525页

作者： Ankit kumar Kamred Udham Singh manish kumar Department of Computer Engineering and Applications GLA UniversityMathura 281406India School of Computing Graphic Era Hill UniversityDehradun 248002India Department of Electronics and Communication Engineering GLA UniversityMathura 281406India.

The correct diagnosis of heart disease can save lives,while the incorrect diagnosis can be *** UCI machine learning heart disease dataset compares the results and analyses of various machine learning approaches,including deep *** used a dataset with 13 primary characteristics to carry out the *** vector machine and logistic regression algorithms are used to process the datasets,and the latter displays the highest accuracy in predicting coronary *** programming is used to process the *** research initiatives have used machine learning to speed up the healthcare *** also used conventional machine learning approaches in our investigation to uncover the links between the numerous features available in the dataset and then used them effectively in anticipation of heart infection *** the accuracy and confusion matrix has resulted in some favorable *** get the best results,the dataset contains certain unnecessary features that are dealt with using isolation logistic regression and Support Vector Machine(SVM)classification.

关键词： artificial intelligence support vector machine logistic regression cleveland dataset supervised algorithm human sensing

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SEIHCRD Model for COVID-19 Spread Scenarios,Disease Predictions and Estimates the Basic Reproduction Number,Case Fatality Rate,Hospital,and ICU Beds Requirement

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Computer Modeling in Engineering & Sciences 2020年第12期125卷 991-1031页

作者： Avaneesh Singh manish kumar Bajpai PDPM Indian Institute of Information Technology Design and ManufacturingJabalpur482005India

We have proposed a new mathematical method,the SEIHCRD model,which has an excellent potential to predict the incidence of COVID-19 *** proposed SEIHCRD model is an extension of the SEIR ***-compartments have added death,hospitalized,and critical,which improves the basic understanding of disease spread and *** have studiedCOVID-19 cases of six countries,where the impact of this disease in the highest are Brazil,India,Italy,Spain,the United Kingdom,and the United *** estimating model parameters based on available clinical data,the modelwill propagate and forecast dynamic *** calculates the Basic reproduction number over time using logistic regression and the Case fatality rate based on the selected countries’age-category *** calculates two types of Case fatality rate one is CFR daily,and the other is total *** proposed model estimates the approximate time when the disease is at its peak and the approximate time when death cases rarely occur and calculate how much hospital beds and ICU beds will be needed in the peak days of *** SEIHCRD model outperforms the classic ARXmodel and the ARIMA ***,MAPE,andRsquaredmatrices are used to evaluate results and are graphically represented using Taylor and Target *** result shows RMSE has improved by 56%–74%,and MAPE has a 53%–89%improvement in prediction accuracy.

关键词： COVID-19 coronavirus SIER model SEIHCRD model parameter estimation mathematical model India Brazil United Kingdom United States Spain Italy hospital beds ICU beds basic reproduction number case fatality rate

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Study of Non-Pharmacological Interventions on COVID-19 Spread

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Computer Modeling in Engineering & Sciences 2020年第12期125卷 967-990页

作者： Avaneesh Singh Saroj kumar Chandra manish kumar Bajpai PDPM Indian Institute of Information Technology Design and ManufacturingJabalpur482005India

COVID-19 disease has emerged as one of the life threatening threat to the society.A novel beta coronavirus causes *** began as unidentified pneumonia of unknown etiology in Wuhan City,Hubei province in China emerged in December *** vaccine has been produced till *** models are used to study the impact of different measures used to decrease *** models have been designed to estimate the numbers of spreaders in different scenarios in the present *** the present manuscript,three different mathematical models have been proposed with different scenarios,such as screening,quarantine,andNPIs,to estimate the number of virus *** analysis shows that the numbers of COVID-19 patients will be more without screening the peoples coming from other *** every people suffering fromCOVID-19 disease are *** screening and quarantine with NPIs have been implemented to study their impact on the *** has been found that NPI measures can reduce the number of *** NPI measures reduce the spread function’s growth and provide decision makers more time to prepare with in dealing with the disease.

关键词： Coronavirus COVID-19 mathematical modelling epidemic

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Impact of land cover change on land surface temperature: A case study of Spiti Valley

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Journal of Mountain Science 2018年第8期15卷 1658-1670页

作者： kumar Pankaj HUSAIN Arif SINGH Ram Babu kumar manish Department of Geography Delhi School of Economics University of Delhi Department of Geography Kalindi College University of Delhi

Land surface temperature(LST) is the skin temperature of the earth surface. LST depends on the amount of sunlight received by any geographical area. Apart from sun light, LST is also affected by the land cover, which leads to change in land surface temperature. Impact of land cover change(LCC) on LST has been assessed using Landsat TM5, Landsat 8 TIRS/OLI and Digital Elevation Model(ASTER) for Spiti Valley, Himachal Pradesh, India. In the present study, Spiti valley was divided into three altitudinal zones to check the pattern of changing land cover along different altitudes and LST was calculated for all the four land cover categories extracted from remote sensing data for the years of 1990 and 2015. Matrix table was used as a technique to evaluate the land cover change between two different years. Matrix table shows that as a whole, about 2,151,647 ha(30%) area of Spiti valley experienced change in land cover in the last 25 years. The result also shows vegetation and water bodies increased by 107,560.2 ha(605.87%) and 45 ha(0.98%), respectively. Snow cover and barren land decreased by 19,016.5 ha(23.92%) and 88,589(14.14%), during the study period. A significant increase has been noticed in vegetation amongst all land cover types. Minimum, maximum and mean LST for three altitudinal zones have been calculated. The mean LST recorded was 11℃ in 1990 but it rose by 2℃ and reached to 13℃ in 2015. Changes in LST were obtained for each land cover categories. The mean temperature of different land cover types was calculated by averaging value of all pixels of a given land cover types. The mean LST of vegetation, barren land, snow cover and water body increased by 6℃, 9℃, 1℃, and 7℃, respectively. Further, relationships between LST, Normalized Difference Snow Index(NDSI), and Normalised Difference Vegetation Index(NDVI) were established using Linear Regression.

关键词： Land surface temperature Land cover change Normalised difference snow index Normalised Difference Vegetation Index DEM Remote Sensing GIS Linear Regression

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Prediction of bearing capacity of pile foundation using deep learning approaches

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Frontiers of Structural and Civil Engineering 2024年第6期18卷 870-886页

作者： manish kumar Divesh Ranjan kumar Jitendra KHATTI Pijush SAMUI Kamaldeep Singh GROVER Department of Civil Engineering SRM Institute of Science and Technology Tiruchirappalli CampusTrichy 621105India Department of Civil Engineering National Institute of Technology PatnaBihar 800005India Department of Civil Engineering Faculty of EngineeringThammasat School of EngineeringThammasat UniversityBangkok10200Thailand Department of Civil Engineering Rajasthan Technical UniversityKota 324010India

The accurate prediction of bearing capacity is crucial in ensuring the structural integrity and safety of pile *** research compares the Deep Neural Networks(DNN),Convolutional Neural Networks(CNN),Recurrent Neural Networks(RNN),Long Short-Term Memory(LSTM),and Bidirectional LSTM(BiLSTM)algorithms utilizing a data set of 257 dynamic pile load tests for the first ***,this research illustrates the multicollinearity effect on DNN,CNN,RNN,LSTM,and BiLSTM models’performance and accuracy for the first time.A comprehensive comparative analysis is conducted,employing various statistical performance parameters,rank analysis,and error matrix to evaluate the performance of these *** performance is further validated using external validation,and visual interpretation is provided using the regression error characteristics(REC)curve and Taylor *** from the comparative analysis reveal that the DNN(Coefficient of determination(R^(2))_(training(TR))=0.97,root mean squared error(RMSE)_(TR)=0.0413;R^(2)_(testing(TS))=0.9,RMSE_(TS)=0.08)followed by BiLSTM(R^(2)_(TR)=0.91,RMSE_(TR)=0.782;R^(2)_(TS)=0.89,RMSE_(TS)=0.0862)model demonstrates the highest performance *** is noted that the BiLSTM model is better than LSTM because the BiLSTM model,which increases the amount of information for the network,is a sequence processing model made up of two LSTMs,one of which takes the input in a forward manner,and the other in a backward *** prediction of pile-bearing capacity is strongly influenced by ram weight(having a considerable multicollinearity level),and the effect of the considerable multicollinearity level has been determined for the model based on the recurrent neural network *** this study,the recurrent neural network model has the least performance and accuracy in predicting the pile-bearing capacity.

关键词： deep learning algorithms high-strain dynamic pile test bearing capacity of the pile

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PDE solution to UAV/UGV trajectory planning problem by spatio-temporal estimation during wildfires

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Chinese Journal of Aeronautics 2021年第5期34卷 601-616页

作者： Mohammadreza RADM ANESH Balaji SHARMA manish kumar Donald FRENCH Aerial Mobility Group NASA Jet Propulsion Lcib(JPL)Pasadena 91016USA Mathworks BuffaloNew YorkUSA Department of Mechanical and Material Engineering.University of Cincinnati Cincinnati 45221USA Department of Mathematical Sciences University of CincinnatiCincinnati 45221USA

Unmanned Aerial Vehicles(UAVs)and Unmanned Ground Vehicles(UGVs)have been used in research and development community due to their strong potential in high-risk *** of the most important civilian implementations of UAV/UGV cooperative path planning is delivering medical or emergency supplies during disasters such as wildfires,the focus of this ***,wildfires themselves pose risk to the UAVs/UGVs and their paths should be planned to avert the risk as well as complete the *** this paper,wildfire growth is simulated using a coupled Partial Differential Equation(PDE)model,widely used in literature for modeling wildfires,in a grid environment with added process and measurement *** principles of Proper Orthogonal Decomposition(POD),and with an appropriate choice of decomposition modes,a low-dimensional equivalent fire growth model is obtained for the deployment of the space-time Kalman Filtering(KF)paradigm for estimation of wildfires using simulated *** KF paradigm is then used to estimate and predict the propagation of wildfire based on local data obtained from a camera mounted on the *** information is then used to obtain a safe path for the UGV that needs to travel from an initial location to the final position while the UAV’s path is planned to gather information on *** planning of both UAV and UGV is carried out using a PDE based method that allows incorporation of threats due to wildfire and other obstacles in the form of risk *** results from numerical simulation are presented to validate the proposed estimation and path planning methods.

关键词： State-estimation Trajectory planning UAV UGV Wild-fire

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