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MXenes: Versatile 2D materials with tailored surface chemistry and diverse applications

Journal of Energy Chemistry 2024年第3期90卷 253-293,I0008页

作者： sunil kumar Nitu kumari Yongho Seo Department of Nanotechnology and Advanced Materials Engineering and HMC Sejong UniversitySeoul 05006South Korea School of Studies in Chemistry Jiwaji UniversityGwalior 474011India

MXenes,the most recent addition to the 2D material family,have attracted significant attention owing to their distinctive characteristics,including high surface area,conductivity,surface characteristics,mechanical strength,*** review begins by presenting MXenes,providing insights into their structural characteristics,synthesis methods,and surface functional *** review covers a thorough analysis of MXene surface properties,including surface chemistry and termination group *** properties of MXenes are influenced by their synthesis,which can be fluorine-based or ***-based synthesis techniques involve etching with fluorine-based reagents,mainly including HF or LiF/HCl,while fluorine-free methods include electrochemical etching,chemical vapor deposition(CVD),alkaline etching,Lewis acid-based etching,*** techniques result in the emergence of functional groups such as-F,-O,-OH,-Cl,*** the MXenes surface,depending on the synthesis method *** of MXenes,such as electrical conductivity,electronic properties,catalytic activity,magnetic properties,mechanical strength,and chemical and thermal stability,are examined,and the role of functional groups in determining these properties is *** review delves into the diverse applications of MXenes,encompassing supercapacitors,battery materials,hydrogen storage,fuel cells,electromagnetic interference(EMI) shielding,pollutant removal,water purification,flexible electronics,sensors,additive manufacturing,catalysis,biomedical and healthcare fields,***,this article outlines the challenges and opportunities in the current and future development of MXenes research,addressing various aspects such as synthesis scalability,etching challenges,and multifunctionality,and exploring novel *** review concludes with future prospects and conclusions envisioning the impact of MXenes on future technologies and innovation.

关键词： MXenes 2D materials Surface chemistry MXenes structure Synthesis Applications

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A Novel Clustering Technique for Efficient Clustering of Big Data in Hadoop Ecosystem

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Big Data Mining and Analytics 2019年第4期2卷 240-247页

作者： sunil kumar Maninder Singh Directorate of Livestock Farms Guru Angad Dev Veterinary and Animal Sciences UniversityLudhiana 141001India Department of Computer Science Punjabi UniversityPunjab 147002India

Big data analytics and data mining are techniques used to analyze data and to extract hidden *** approaches to analysis and extraction do not work well for big data because this data is complex and of very high volume. A major data mining technique known as data clustering groups the data into clusters and makes it easy to extract information from these clusters. However, existing clustering algorithms, such as k-means and hierarchical, are not efficient as the quality of the clusters they produce is compromised. Therefore, there is a need to design an efficient and highly scalable clustering algorithm. In this paper, we put forward a new clustering algorithm called hybrid clustering in order to overcome the disadvantages of existing clustering algorithms. We compare the new hybrid algorithm with existing algorithms on the bases of precision, recall, F-measure, execution time, and accuracy of results. From the experimental results, it is clear that the proposed hybrid clustering algorithm is more accurate, and has better precision, recall, and F-measure values.

关键词： clustering Hadoop big data k-means hierarchical

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Big Data Analytics for Healthcare Industry:Impact,Applications,and Tools

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Big Data Mining and Analytics 2019年第1期2卷 48-57页

作者： sunil kumar Maninder Singh the Directorate of Livestock Farms Guru Angad Dev Veterinary and Animal Sciences University the Department of Computer Science Punjabi University

In recent years, huge amounts of structured, unstructured, and semi-structured data have been generated by various institutions around the world and, collectively, this heterogeneous data is referred to as big data. The health industry sector has been confronted by the need to manage the big data being produced by various sources,which are well known for producing high volumes of heterogeneous data. Various big-data analytics tools and techniques have been developed for handling these massive amounts of data, in the healthcare sector. In this paper, we discuss the impact of big data in healthcare, and various tools available in the Hadoop ecosystem for handling it. We also explore the conceptual architecture of big data analytics for healthcare which involves the data gathering history of different branches, the genome database, electronic health records, text/imagery, and clinical decisions support system.

关键词： big data healthcare Hadoop MapReduce

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Embedding in Anisotropic Spheres

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Research in Astronomy and Astrophysics 2022年第4期22卷 62-70页

作者： Alberto k.Mathias sunil D.Maharaj Jefta M.Sunzu Jason M.Mkenyeleye Department of Mathematics and Statistics The University of DodomaP.O.Box 338DodomaTanzania Astrophysics Research Centre School of MathematicsStatistics and Computer ScienceUniversity of KwaZulu-NatalDurban 4000South Africa

Exact solutions to the Einstein field equations for class I spacetime symmetry in relativistic stars are *** symmetry provides a relation between the gravitational potentials that lead to generalized solutions of the Einstein field *** choose one of the gravitational potentials on a physical basis,which allows us to obtain the other gravitational potential via an embedding *** is therefore possible to generate a model with astrophysical *** model generated satisfies physical properties like stability,causality,regularity,equilibrium and energy conditions.

关键词： gravitation astroparticle physics relativistic processes

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Estimation of the effectiveness of multi-criteria decision analysis and machine learning approaches for agricultural land capability in Gangarampur Subdivision, Eastern India

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Artificial Intelligence in Geosciences 2022年第1期3卷 179-191页

作者： sunil Saha Prolay Mondal Department of Geography Raiganj UniversityRaiganjWest Bengal733134India

Land suitability analysis(LSA)is an evaluation method that measures the degree to which land is suitable for certain land *** primary aims of this study are to identify potentially viable agricultural land in the Gangarampur subdivision(West Bengal)using Multiple Criteria Decision Making(MCDM)and machine learning procedures and to evaluate the efficacy of the employed *** Analytic Hierarchy Process(AHP)model was used to assign relative weights to the fifteen various criteria in this suitability analysis,and then the Fuzzy Complex Proportional Assessment(FCOPRAS)model was applied using the AHP’s normalised pairwise comparison matrix,whereas the Waikato Environment for knowledge Analysis(Weka)Software was used to apply machine learning algorithms to the field *** Random Forest(RF)model,on the other hand,is a better fit for the locational study of soil *** to the RF findings,areas of 14.67 per cent(15368.46 ha)are excellent(ZONE Ⅴ)for growing crops,approximately 22.30 per cent(23367.9 ha)are highly suitable(ZONE Ⅳ),and 23.63 per cent(24762.12 ha)are moderately suitable(ZONE Ⅲ)for cultivation,*** numbers for FCOPRAS are roughly 15.39%(16130.52 ha),22.54%(23620.65 ha),and 19.79%(20733.26 ha).The Receiver Operating Characteristic(ROC)curve and accuracy measurements of the results indicate the high accuracy of the applied models,with Random Forest and FCOPRAS being the most popular and effective *** study will make an important contribution to evaluations of soil fertility and site *** will help local government officials,academics,and farmers scientifically use the land.

关键词： Analytic Hierarchy Process Fuzzy Complex Proportional Assessment Receiver Operating Characteristic Gangarampur subdivision Soil Capability

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Development and Application of a Production Data Analysis Model for a Shale Gas Production Well

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Fluid Dynamics & Materials Processing 2020年第3期16卷 411-424页

作者： Dongkwon Han sunil kwon Department of Energy and Mineral Resources Dong-A UniversityBusan49315Korea

This paper presents the development and application of a production data analysis software that can analyze and forecast the production performance and reservoir properties of shale gas *** theories used in the study were based on the analytical and empirical *** reliability has been confirmed through comparisons with a commercial *** transient data relating to multi-stage hydraulic fractured horizontal wells,it was confirmed that the accuracy of the modified hyperbolic method showed an error of approximately 4%compared to the actual estimated ultimate recovery(EUR).On the basis of the developed model,reliable productivity forecasts have been obtained by analyzing field production data relating to wells in *** EUR was computed as 9.6 Bcf using the modified hyperbolic *** the Pow Law Exponential method,the EUR would be 9.4 *** models developed in this study will allow in the future integration of new analytical and empirical theories in a relatively readily than commercial models.

关键词： Production data analysis shale gas multi-stage hydraulic fractured horizontal wells estimated ultimate recovery

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Diabetes mellitus and stroke: A clinical update

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World Journal of Diabetes 2017年第6期8卷 235-248页

作者： Nyo Nyo Tun Ganesan Arunagirinathan sunil k Munshi Joseph M Pappachan Department of Endocrinology and Diabetes Western General Hospital Department of Stroke Medicine Nottingham University Hospitals NHS Trust and University of Nottingham Department of Endocrinology and Diabetes Royal Lancaster InfirmaryUniversity Hospitals Morecambe NHS Trust

Cardiovascular disease including stroke is a major complication that tremendously increases the morbidity and mortality in patients with diabetes mellitus(DM). DM poses about four times higher risk for stroke. Cardiometabolic risk factors including obesity, hypertension, and dyslipidaemia often co-exist in patients with DM that add on to stroke risk. Because of the strong association between DM and other stroke risk factors, physicians and diabetologists managing patients should have thorough understanding of these risk factors and management. This review is an evidence-based approach to the epidemiological aspects, pathophysiology, diagnostic work up and management algorithms for patients with diabetes and stroke.

关键词： Diabetes mellitus Stroke Metabolic memory Cardiovascular disease Glycaemic management

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Soil Microbial Dynamics Modeling in Fluctuating Ecological Situations by Using Subtractive Clustering and Fuzzy Rule-Based Inference Systems

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Computer Modeling in Engineering & Sciences 2017年第4期113卷 443-459页

作者： sunil kr.Jha Zulfiqar Ahmad School of Computer and Software Nanjing University of Information Science and TechnologyNanjing 210044China Department of Environmental Sciences University of CaliforniaRiversideCA 92521USA

Microbial population and enzyme activities are the significant indicators of soil *** microbial dynamics characterize microbial population and enzyme *** present study explores the development of efficient predictive modeling systems for the estimation of specific soil microbial dynamics,like rock phosphate solubilization,bacterial population,and ACC-deaminase *** specifically,optimized subtractive clustering(SC)and Wang and Mendel's(WM)fuzzy inference systems(FIS)have been implemented with the objective to achieve the best estimation accuracy of microbial *** measurements were performed using controlled pot experiment using minimal salt media with rock phosphate as sole carbon source inoculated with phosphate solubilizing microorganism in order to estimate rock phosphate solubilization potential of selected *** experimental parameters,including temperature,pH,and incubation period have been used as inputs SC-FIS and *** better performance of the SC-FIS has been observed as compared to the WM-FIS in the estimation of phosphate solubilization and bacterial population with the maximum value of the coefficient of determination(0.9988)2 R=in the estimation of previous microbial dynamics.

关键词： Phosphate solubilizing bacteria bacterial population ACC-deaminase activity subtractive clustering fuzzy rule-based prediction system

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Genetic Algorithm with Variable Length Chromosomes for Network Intrusion Detection

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International Journal of Automation and computing 2015年第3期12卷 337-342页

作者： sunil Nilkanth Pawar Rajankumar Sadashivrao Bichkar Jawaharlal Nehru Engineering College G.H.Raisoni College of Engineering & Management

Genetic algorithm(GA) has received significant attention for the design and implementation of intrusion detection systems. In this paper, it is proposed to use variable length chromosomes(VLCs) in a GA-based network intrusion detection *** chromosomes with relevant features are used for rule generation. An effective fitness function is used to define the fitness of each rule. Each chromosome will have one or more rules in it. As each chromosome is a complete solution to the problem, fewer chromosomes are sufficient for effective intrusion detection. This reduces the computational time. The proposed approach is tested using Defense Advanced Research Project Agency(DARPA) 1998 data. The experimental results show that the proposed approach is efficient in network intrusion detection.

关键词： Genetic algorithms intrusion detection variable length chromosome network security evolutionary optimization.

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An Effective Feature Generation and Selection Approach for Lymph Disease Recognition

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Computer Modeling in Engineering & Sciences 2021年第11期129卷 567-594页

作者： sunil kr.Jha Zulfiqar Ahmad School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China Institute of Hydrobiology Chinese Academy of SciencesWuhan430072China

Health care data mining is noteworthy in disease diagnosis and recognition *** exist several potentials to further improve the performance of machine learning based-classification methods in healthcare data *** selection of a substantial subset of features is one of the feasible approaches to achieve improved recognition results of classification methods in disease diagnosis *** the present study,a novel combined approach of feature generation using latent semantic analysis(LSA)and selection using ranker search(RAS)has been proposed to improve the performance of classification methods in lymph disease diagnosis *** performance of the proposed combined approach(LSA-RAS)for feature generation and selection is validated using three function-based and two tree-based classification *** performance of the LSA-RAS selected features is compared with the original attributes and other subsets of attributes and features chosen by nine different attributes and features selection approaches in the analysis of a most widely used benchmark and open access lymph disease *** LSA-RAS selected features improve the recognition accuracy of the classification methods significantly in the diagnosis prediction of the lymph *** tree-based classification methods have better recognition accuracy than the function-based classification *** best performance(recognition accuracy of 93.91%)is achieved for the logistic model tree(LMT)classification method using the feature subset generated by the proposed combined approach(LSA-RAS).

关键词： Disease data mining feature selection classification lymph diagnosis

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